2023
Baker, Clayton
Predictive Modelling of Human Reasoning Using AGM Belief Revision Conference
Doctoral Consortium at the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), 2023.
Abstract | Links | BibTeX | Tags:
@conference{Baker2023b,
title = {Predictive Modelling of Human Reasoning Using AGM Belief Revision},
author = {Clayton Baker},
url = {https://airu.org.za/wp-content/uploads/2023/07/Baker-Predictive-Modelling-of-Human-Reasoning-Using-AGM-Belief-Revision.pdf},
year = {2023},
date = {2023-08-21},
urldate = {2023-08-21},
booktitle = {Doctoral Consortium at the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023)},
abstract = {While many forms of belief change exist, the relationship between belief revision and human reasoning is of primary interest in this work. The theory of belief revision extends classical two-valued logic with an approach to resolve the conflict between a set of beliefs and newly learned information. The goal of this project is to test how humans revise conflicting beliefs. Experiments are proposed in which human subjects are required to resolve conflicting beliefs via relevance and confidence. In our analysis, the human responses will be evaluated against the predictions of two perspectives of propositional belief revision: formal and psychological.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
While many forms of belief change exist, the relationship between belief revision and human reasoning is of primary interest in this work. The theory of belief revision extends classical two-valued logic with an approach to resolve the conflict between a set of beliefs and newly learned information. The goal of this project is to test how humans revise conflicting beliefs. Experiments are proposed in which human subjects are required to resolve conflicting beliefs via relevance and confidence. In our analysis, the human responses will be evaluated against the predictions of two perspectives of propositional belief revision: formal and psychological.
Moodley, Deshendran; Seebregts, Christopher
Workshop on AI for Digital Twins and Cyber-physical Applications at the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), 2023.
Abstract | Links | BibTeX | Tags:
@workshop{Moodley2023b,
title = {Re-imagining health and well-being in low resource African settings using an augmented AI system and a 3D digital twin},
author = {Deshendran Moodley and Christopher Seebregts},
url = {https://people.cs.uct.ac.za/~deshen/IJCAI23_AI4DT_CP_workshop-revised.pdf},
year = {2023},
date = {2023-08-19},
urldate = {2023-08-19},
booktitle = {Workshop on AI for Digital Twins and Cyber-physical Applications at the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023)},
abstract = {This paper discusses and explores the potential and relevance of recent developments in artificial intelligence (AI) and digital twins for health and well-being in low-resource African countries. We use the case of public health emergency response to disease outbreaks and epidemic control. There is potential to take advantage of the increasing availability of data and digitization to develop advanced AI methods for analysis and prediction. Using an AI systems perspective, we review emerging trends in AI systems and digital twins and propose an initial augmented AI system architecture to illustrate how an AI system can work with a 3D digital twin to address public health goals. We highlight scientific knowledge discovery, continual learning, pragmatic interoperability, and interactive explanation and decision-making as essential research challenges for AI systems and digital twins.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
This paper discusses and explores the potential and relevance of recent developments in artificial intelligence (AI) and digital twins for health and well-being in low-resource African countries. We use the case of public health emergency response to disease outbreaks and epidemic control. There is potential to take advantage of the increasing availability of data and digitization to develop advanced AI methods for analysis and prediction. Using an AI systems perspective, we review emerging trends in AI systems and digital twins and propose an initial augmented AI system architecture to illustrate how an AI system can work with a 3D digital twin to address public health goals. We highlight scientific knowledge discovery, continual learning, pragmatic interoperability, and interactive explanation and decision-making as essential research challenges for AI systems and digital twins.
Gillis-Webber, Frances
Concept Mismatches Between a Source and Target Natural Language Workshop
Workshop on Modular Knowledge at the 13th International Conference on Formal Ontology in Information Systems (FOIS 2023), 2023.
Abstract | Links | BibTeX | Tags:
@workshop{GillisWebber2023-2b,
title = {Concept Mismatches Between a Source and Target Natural Language},
author = {Frances Gillis-Webber},
url = {https://airu.org.za/wp-content/uploads/2023/08/Gillis-Webber-Concept-Mismatches-Between-a-Source-and-Target-Natural-Language.pdf},
year = {2023},
date = {2023-07-20},
booktitle = {Workshop on Modular Knowledge at the 13th International Conference on Formal Ontology in Information Systems (FOIS 2023)},
abstract = {Numerous mismatches have been identified when aligning heterogenous resources. In this paper, the focus is on the mismatches for a concept between a source and target viewpoint, where each viewpoint is natural language-specific. A concept is first defined as a 6-tuple, comprising of its viewpoint, the lexical realisation of the concept, the axiomatisation thereof, as well as asserted individuals. The same concept is then defined as another tuple, this time for a target viewpoint, with each element therein compared to the original. A total of 22 mismatches and correspondences have been identified, with three pertaining to lexical realisations, twelve pertaining to the axiomatisation of a concept, and seven pertaining to individuals and assertions.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
Numerous mismatches have been identified when aligning heterogenous resources. In this paper, the focus is on the mismatches for a concept between a source and target viewpoint, where each viewpoint is natural language-specific. A concept is first defined as a 6-tuple, comprising of its viewpoint, the lexical realisation of the concept, the axiomatisation thereof, as well as asserted individuals. The same concept is then defined as another tuple, this time for a target viewpoint, with each element therein compared to the original. A total of 22 mismatches and correspondences have been identified, with three pertaining to lexical realisations, twelve pertaining to the axiomatisation of a concept, and seven pertaining to individuals and assertions.
Gillis-Webber, Frances
Towards an Ontology of Viewpoints Conference
13th International Conference on Formal Ontology in Information Systems (FOIS 2023), Sherbrooke, Quebec, Canada, 2023.
Abstract | Links | BibTeX | Tags:
@conference{GillisWebber2023d,
title = {Towards an Ontology of Viewpoints},
author = {Frances Gillis-Webber},
url = {https://airu.org.za/wp-content/uploads/2023/08/Gillis-Webber-Towards-an-Ontology-of-Viewpoints.pdf},
year = {2023},
date = {2023-07-17},
urldate = {2023-07-17},
booktitle = {13th International Conference on Formal Ontology in Information Systems (FOIS 2023)},
address = {Sherbrooke, Quebec, Canada},
abstract = {In a multilingual domain ontology developed using the labels approach, where each ontological entity is labelled with a language-tagged string, two scenarios result: (1) the ontology is 'language-independent', where there is an equal number of labels per natural language, or (2) the ontology is a 'primary-language' ontology, where one natural language takes precedence over the other languages used. In a multilingual ontology, it is assumed there is full equivalence between the different languages, however, each natural language, as an embodiment of a culture, differs in how it interprets and organises the world. The result is that although the viewpoint expressed by the multilingual domain ontology is thought to be universal, one natural language is very often privileged, typically English.
Using the culture-bound concepts of 'dowry' and 'bride price', we demonstrate the differences in perspective when considered for different languages and sub-domains. We propose an ontology, Model of Multiple Viewpoints (MULTI), where both language and culture are considered together, and language is classified as a social norm of a community. MULTI is formalised in OWL and aligned to DOLCE+DnS Ultralite, a foundational ontology suitable for modelling contexts. The evaluation of MULTI is done against the identified use cases. The expected result is that an ontology can be annotated with its viewpoint, thus making the viewpoint of the ontology explicit.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
In a multilingual domain ontology developed using the labels approach, where each ontological entity is labelled with a language-tagged string, two scenarios result: (1) the ontology is 'language-independent', where there is an equal number of labels per natural language, or (2) the ontology is a 'primary-language' ontology, where one natural language takes precedence over the other languages used. In a multilingual ontology, it is assumed there is full equivalence between the different languages, however, each natural language, as an embodiment of a culture, differs in how it interprets and organises the world. The result is that although the viewpoint expressed by the multilingual domain ontology is thought to be universal, one natural language is very often privileged, typically English.
Using the culture-bound concepts of 'dowry' and 'bride price', we demonstrate the differences in perspective when considered for different languages and sub-domains. We propose an ontology, Model of Multiple Viewpoints (MULTI), where both language and culture are considered together, and language is classified as a social norm of a community. MULTI is formalised in OWL and aligned to DOLCE+DnS Ultralite, a foundational ontology suitable for modelling contexts. The evaluation of MULTI is done against the identified use cases. The expected result is that an ontology can be annotated with its viewpoint, thus making the viewpoint of the ontology explicit.
Using the culture-bound concepts of 'dowry' and 'bride price', we demonstrate the differences in perspective when considered for different languages and sub-domains. We propose an ontology, Model of Multiple Viewpoints (MULTI), where both language and culture are considered together, and language is classified as a social norm of a community. MULTI is formalised in OWL and aligned to DOLCE+DnS Ultralite, a foundational ontology suitable for modelling contexts. The evaluation of MULTI is done against the identified use cases. The expected result is that an ontology can be annotated with its viewpoint, thus making the viewpoint of the ontology explicit.
Wanyana, Tezira; Nzomo, Mbithe; Price, C. Sue; Moodley, Deshendran
A Personal Health Agent for Decision Support in Arrhythmia Diagnosis Proceedings Article
In: Maciaszek, Leszek A.; Mulvenna, Maurice D.; Ziefle, Martina (Ed.): Information and Communication Technologies for Ageing Well and e-Health. ICT4AWE ICT4AWE 2021 2022., pp. 385–407, Springer, Cham, 2023.
Abstract | Links | BibTeX | Tags:
@inproceedings{Wanyana2023b,
title = {A Personal Health Agent for Decision Support in Arrhythmia Diagnosis},
author = {Tezira Wanyana and Mbithe Nzomo and C. Sue Price and Deshendran Moodley},
editor = {Leszek A. Maciaszek and Maurice D. Mulvenna and Martina Ziefle},
url = {https://airu.org.za/wp-content/uploads/2023/07/Wanyana-et-al.-A-Personal-Health-Agent-for-Decision-Support-in-Arrhythmia-Diagnosis.pdf},
doi = {10.1007/978-3-031-37496-8_20},
year = {2023},
date = {2023-07-14},
urldate = {2023-07-14},
booktitle = {Information and Communication Technologies for Ageing Well and e-Health. ICT4AWE ICT4AWE 2021 2022.},
volume = {1856},
pages = {385–407},
publisher = {Springer, Cham},
series = {Communications in Computer and Information Science},
abstract = {We propose an architecture for a personal health agent (PHA) that combines machine learning and a Bayesian network (BN) for detecting and diagnosing heart disease, specifically arrhythmia. Machine learning (ML) is used for classifying a patient’s ECG signal. Four ML models, i.e. gradient boosting, random forest, multilayer perceptron and support vector machine, are compared and evaluated using a dataset of 5,340 records containing 12-lead ECG signals created from the Chapman-Shaoxing database. Among the four models, the gradient boosting model produces the best accuracy of 82.88% when classifying an ECG signal as either atrial fibrillation, other arrhythmia, or no arrhythmia. The detected pattern is integrated into a BN that captures expert knowledge about the causes of arrhythmia. The BN structure and parameters are informed by expert knowledge from the literature and evaluated using Pitchforth and Mengersen’s framework. The agent uses a decision support module to guide the diagnosis process. It suggests what questions to ask to increase certainty of the presence of arrhythmia, and it suggests what arrhythmia causes to follow up. This is achieved using sensitivity analysis and diagnostic Bayesian reasoning respectively. The architecture is evaluated using application use cases.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose an architecture for a personal health agent (PHA) that combines machine learning and a Bayesian network (BN) for detecting and diagnosing heart disease, specifically arrhythmia. Machine learning (ML) is used for classifying a patient’s ECG signal. Four ML models, i.e. gradient boosting, random forest, multilayer perceptron and support vector machine, are compared and evaluated using a dataset of 5,340 records containing 12-lead ECG signals created from the Chapman-Shaoxing database. Among the four models, the gradient boosting model produces the best accuracy of 82.88% when classifying an ECG signal as either atrial fibrillation, other arrhythmia, or no arrhythmia. The detected pattern is integrated into a BN that captures expert knowledge about the causes of arrhythmia. The BN structure and parameters are informed by expert knowledge from the literature and evaluated using Pitchforth and Mengersen’s framework. The agent uses a decision support module to guide the diagnosis process. It suggests what questions to ask to increase certainty of the presence of arrhythmia, and it suggests what arrhythmia causes to follow up. This is achieved using sensitivity analysis and diagnostic Bayesian reasoning respectively. The architecture is evaluated using application use cases.
Nzomo, Mbithe; Moodley, Deshendran
Semantic Technologies in Sensor-Based Personal Health Monitoring Systems: A Systematic Mapping Study Bachelor Thesis
2023.
Abstract | Links | BibTeX | Tags:
@bachelorthesis{Nzomo2023,
title = {Semantic Technologies in Sensor-Based Personal Health Monitoring Systems: A Systematic Mapping Study},
author = {Mbithe Nzomo and Deshendran Moodley},
doi = {10.48550/arXiv.2306.04335},
year = {2023},
date = {2023-06-07},
urldate = {2023-06-07},
abstract = {In recent years, there has been an increased focus on early detection, prevention, and prediction of diseases. This, together with advances in sensor technology and the Internet of Things, has led to accelerated efforts in the development of personal health monitoring systems. Semantic technologies have emerged as an effective way to not only deal with the issue of interoperability associated with heterogeneous health sensor data, but also to represent expert health knowledge to support complex reasoning required for decision-making. This study evaluates the state of the art in the use of semantic technologies in sensor-based personal health monitoring systems. Using a systematic approach, a total of 40 systems representing the state of the art in the field are analysed. Through this analysis, six key challenges that such systems must overcome for optimal and effective health monitoring are identified: interoperability, context awareness, situation detection, situation prediction, decision support, and uncertainty handling. The study critically evaluates the extent to which these systems incorporate semantic technologies to deal with these challenges and identifies the prominent architectures, system development and evaluation methodologies that are used. The study provides a comprehensive mapping of the field, identifies inadequacies in the state of the art, and provides recommendations for future research directions.},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
In recent years, there has been an increased focus on early detection, prevention, and prediction of diseases. This, together with advances in sensor technology and the Internet of Things, has led to accelerated efforts in the development of personal health monitoring systems. Semantic technologies have emerged as an effective way to not only deal with the issue of interoperability associated with heterogeneous health sensor data, but also to represent expert health knowledge to support complex reasoning required for decision-making. This study evaluates the state of the art in the use of semantic technologies in sensor-based personal health monitoring systems. Using a systematic approach, a total of 40 systems representing the state of the art in the field are analysed. Through this analysis, six key challenges that such systems must overcome for optimal and effective health monitoring are identified: interoperability, context awareness, situation detection, situation prediction, decision support, and uncertainty handling. The study critically evaluates the extent to which these systems incorporate semantic technologies to deal with these challenges and identifies the prominent architectures, system development and evaluation methodologies that are used. The study provides a comprehensive mapping of the field, identifies inadequacies in the state of the art, and provides recommendations for future research directions.
Bashingwa, Jean Juste Harrisson; Mohan, Diwakar; Chamberlain, Sara; Scott, Kerry; Ummer, Osama; Godfrey, Anna; Mulder, Nicola; Moodley, Deshendran; LeFevre, Amnesty Elizabeth
In: BMJ Open, vol. 13, iss. 3, pp. e063354, 2023, ISSN: 2044-6055.
Abstract | Links | BibTeX | Tags:
@article{Bashingwa2023b,
title = {Can we design the next generation of digital health communication programs by leveraging the power of artificial intelligence to segment target audiences, bolster impact and deliver differentiated services? A machine learning analysis of survey data from rural India},
author = {Jean Juste Harrisson Bashingwa and Diwakar Mohan and Sara Chamberlain and Kerry Scott and Osama Ummer and Anna Godfrey and Nicola Mulder and Deshendran Moodley and Amnesty Elizabeth LeFevre},
doi = {10.1136/bmjopen-2022-063354},
issn = {2044-6055},
year = {2023},
date = {2023-03-01},
urldate = {2023-03-01},
journal = {BMJ Open},
volume = {13},
issue = {3},
pages = {e063354},
publisher = {British Medical Journal Publishing Group},
abstract = {Objectives Direct to beneficiary (D2B) mobile health communication programmes have been used to provide reproductive, maternal, neonatal and child health information to women and their families in a number of countries globally. Programmes to date have provided the same content, at the same frequency, using the same channel to large beneficiary populations. This manuscript presents a proof of concept approach that uses machine learning to segment populations of women with access to phones and their husbands into distinct clusters to support differential digital programme design and delivery.
Setting Data used in this study were drawn from cross-sectional survey conducted in four districts of Madhya Pradesh, India. Participants Study participant included pregnant women with access to a phone (n=5095) and their husbands (n=3842) Results We used an iterative process involving K-Means clustering and Lasso regression to segment couples into three distinct clusters. Cluster 1 (n=1408) tended to be poorer, less educated men and women, with low levels of digital access and skills. Cluster 2 (n=666) had a mid-level of digital access and skills among men but not women. Cluster 3 (n=1410) had high digital access and skill among men and moderate access and skills among women. Exposure to the D2B programme ‘Kilkari’ showed the greatest difference in Cluster 2, including an 8% difference in use of reversible modern contraceptives, 7% in child immunisation at 10 weeks, 3% in child immunisation at 9 months and 4% in the timeliness of immunisation at 10 weeks and 9 months.
Conclusions Findings suggest that segmenting populations into distinct clusters for differentiated programme design and delivery may serve to improve reach and impact.
Trial registration number NCT03576157.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objectives Direct to beneficiary (D2B) mobile health communication programmes have been used to provide reproductive, maternal, neonatal and child health information to women and their families in a number of countries globally. Programmes to date have provided the same content, at the same frequency, using the same channel to large beneficiary populations. This manuscript presents a proof of concept approach that uses machine learning to segment populations of women with access to phones and their husbands into distinct clusters to support differential digital programme design and delivery.
Setting Data used in this study were drawn from cross-sectional survey conducted in four districts of Madhya Pradesh, India. Participants Study participant included pregnant women with access to a phone (n=5095) and their husbands (n=3842) Results We used an iterative process involving K-Means clustering and Lasso regression to segment couples into three distinct clusters. Cluster 1 (n=1408) tended to be poorer, less educated men and women, with low levels of digital access and skills. Cluster 2 (n=666) had a mid-level of digital access and skills among men but not women. Cluster 3 (n=1410) had high digital access and skill among men and moderate access and skills among women. Exposure to the D2B programme ‘Kilkari’ showed the greatest difference in Cluster 2, including an 8% difference in use of reversible modern contraceptives, 7% in child immunisation at 10 weeks, 3% in child immunisation at 9 months and 4% in the timeliness of immunisation at 10 weeks and 9 months.
Conclusions Findings suggest that segmenting populations into distinct clusters for differentiated programme design and delivery may serve to improve reach and impact.
Trial registration number NCT03576157.
Setting Data used in this study were drawn from cross-sectional survey conducted in four districts of Madhya Pradesh, India. Participants Study participant included pregnant women with access to a phone (n=5095) and their husbands (n=3842) Results We used an iterative process involving K-Means clustering and Lasso regression to segment couples into three distinct clusters. Cluster 1 (n=1408) tended to be poorer, less educated men and women, with low levels of digital access and skills. Cluster 2 (n=666) had a mid-level of digital access and skills among men but not women. Cluster 3 (n=1410) had high digital access and skill among men and moderate access and skills among women. Exposure to the D2B programme ‘Kilkari’ showed the greatest difference in Cluster 2, including an 8% difference in use of reversible modern contraceptives, 7% in child immunisation at 10 weeks, 3% in child immunisation at 9 months and 4% in the timeliness of immunisation at 10 weeks and 9 months.
Conclusions Findings suggest that segmenting populations into distinct clusters for differentiated programme design and delivery may serve to improve reach and impact.
Trial registration number NCT03576157.
Casini, Giovanni; Meyer, Thomas; Varzinczak, Ivan
Situated conditional reasoning Journal Article
In: Artificial Intelligence, vol. 319, pp. 103917, 2023.
@article{DBLP:journals/ai/CasiniMV23,
title = {Situated conditional reasoning},
author = {Giovanni Casini and Thomas Meyer and Ivan Varzinczak},
url = {https://doi.org/10.1016/j.artint.2023.103917},
doi = {10.1016/J.ARTINT.2023.103917},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = { Artificial Intelligence},
volume = {319},
pages = {103917},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Baker, Clayton Kevin; Meyer, Thomas
Do Humans Find Postulates of Belief Change Plausible? Journal Article
In: Journal of Applied Logics: The IfCoLog Journal of Logics and their Applications, vol. 10, iss. 2, pp. 249–267, 2023.
Abstract | Links | BibTeX | Tags:
@article{DBLP:journals/flap/Baker023,
title = {Do Humans Find Postulates of Belief Change Plausible?},
author = {Clayton Kevin Baker and Thomas Meyer},
url = {https://collegepublications.co.uk/downloads/ifcolog00058.pdf},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Journal of Applied Logics: The IfCoLog Journal of Logics and their Applications},
volume = {10},
issue = {2},
pages = {249–267},
abstract = {Various empirical methods were used to test whether humans agree with postulates of non-monotonic reasoning and belief change. This work investigates through surveys whether postulates of revision and update are plausible with human reasoners when presented as material implication statements. We used statistical methods to measure the association between the antecedent and the consequent of each postulate. The results show that participants tend to find postulates of update more plausible than postulates of revision.},
keywords = {},
pubstate = {published},
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Various empirical methods were used to test whether humans agree with postulates of non-monotonic reasoning and belief change. This work investigates through surveys whether postulates of revision and update are plausible with human reasoners when presented as material implication statements. We used statistical methods to measure the association between the antecedent and the consequent of each postulate. The results show that participants tend to find postulates of update more plausible than postulates of revision.
Heyninck, Jesse; Kern-Isberner, Gabriele; Meyer, Thomas Andreas; Haldimann, Jonas Philipp; Beierle, Christoph
Conditional Syntax Splitting for Non-monotonic Inference Operators Proceedings Article
In: Williams, Brian; Chen, Yiling; Neville, Jennifer (Ed.): Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, IAAI 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2023, Washington, DC, USA, February 7-14, 2023, pp. 6416–6424, AAAI Press, 2023.
@inproceedings{DBLP:conf/aaai/HeyninckKMHB23,
title = {Conditional Syntax Splitting for Non-monotonic Inference Operators},
author = {Jesse Heyninck and Gabriele Kern-Isberner and Thomas Andreas Meyer and Jonas Philipp Haldimann and Christoph Beierle},
editor = {Brian Williams and Yiling Chen and Jennifer Neville},
url = {https://doi.org/10.1609/aaai.v37i5.25789},
doi = {10.1609/AAAI.V37I5.25789},
year = {2023},
date = {2023-01-01},
booktitle = {Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI
2023, Thirty-Fifth Conference on Innovative Applications of Artificial
Intelligence, IAAI 2023, Thirteenth Symposium on Educational Advances
in Artificial Intelligence, EAAI 2023, Washington, DC, USA, February
7-14, 2023},
pages = {6416–6424},
publisher = {AAAI Press},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Jafta, Yahlieel; Leenen, Louise; Meyer, Thomas
Investigating Ontology-Based Data Access with GitHub Proceedings Article
In: Pesquita, Catia; Jiménez-Ruiz, Ernesto; McCusker, Jamie P.; Faria, Daniel; Dragoni, Mauro; Dimou, Anastasia; Troncy, Raphaël; Hertling, Sven (Ed.): The Semantic Web - 20th International Conference, ESWC 2023, Hersonissos, Crete, Greece, May 28 - June 1, 2023, Proceedings, pp. 644–660, Springer, 2023.
@inproceedings{DBLP:conf/esws/JaftaLM23,
title = {Investigating Ontology-Based Data Access with GitHub},
author = {Yahlieel Jafta and Louise Leenen and Thomas Meyer},
editor = {Catia Pesquita and Ernesto Jiménez-Ruiz and Jamie P. McCusker and Daniel Faria and Mauro Dragoni and Anastasia Dimou and Raphaël Troncy and Sven Hertling},
url = {https://doi.org/10.1007/978-3-031-33455-9_38},
doi = {10.1007/978-3-031-33455-9_38},
year = {2023},
date = {2023-01-01},
booktitle = {The Semantic Web - 20th International Conference, ESWC 2023, Hersonissos,
Crete, Greece, May 28 - June 1, 2023, Proceedings},
volume = {13870},
pages = {644–660},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Haldimann, Jonas; Beierle, Christoph; Kern-Isberner, Gabriele; Meyer, Thomas
Conditionals, Infeasible Worlds, and Reasoning with System W Proceedings Article
In: Franklin, Michael; Chun, Soon Ae (Ed.): Proceedings of the Thirty-Sixth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2023, Clearwater Beach, FL, USA, May 14-17, 2023, AAAI Press, 2023.
@inproceedings{DBLP:conf/flairs/HaldimannBK023,
title = {Conditionals, Infeasible Worlds, and Reasoning with System W},
author = {Jonas Haldimann and Christoph Beierle and Gabriele Kern-Isberner and Thomas Meyer},
editor = {Michael Franklin and Soon Ae Chun},
url = {https://doi.org/10.32473/flairs.36.133268},
doi = {10.32473/FLAIRS.36.133268},
year = {2023},
date = {2023-01-01},
booktitle = {Proceedings of the Thirty-Sixth International Florida Artificial Intelligence
Research Society Conference, FLAIRS 2023, Clearwater Beach, FL,
USA, May 14-17, 2023},
publisher = {AAAI Press},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Haldimann, Jonas; Meyer, Thomas; Kern-Isberner, Gabriele; Beierle, Christoph
Rational Closure Extension in SPO-Representable Inductive Inference Operators Proceedings Article
In: Gaggl, Sarah Alice; Martinez, Maria Vanina; Ortiz, Magdalena (Ed.): Logics in Artificial Intelligence - 18th European Conference, JELIA 2023, Dresden, Germany, September 20-22, 2023, Proceedings, pp. 561–576, Springer, 2023.
@inproceedings{DBLP:conf/jelia/HaldimannMKB23,
title = {Rational Closure Extension in SPO-Representable Inductive Inference
Operators},
author = {Jonas Haldimann and Thomas Meyer and Gabriele Kern-Isberner and Christoph Beierle},
editor = {Sarah Alice Gaggl and Maria Vanina Martinez and Magdalena Ortiz},
url = {https://doi.org/10.1007/978-3-031-43619-2_38},
doi = {10.1007/978-3-031-43619-2_38},
year = {2023},
date = {2023-01-01},
booktitle = {Logics in Artificial Intelligence - 18th European Conference, JELIA
2023, Dresden, Germany, September 20-22, 2023, Proceedings},
volume = {14281},
pages = {561–576},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Heyninck, Jesse; Casini, Giovanni; Meyer, Thomas; Straccia, Umberto
Revising Typical Beliefs: One Revision to Rule Them All Proceedings Article
In: Marquis, Pierre; Son, Tran Cao; Kern-Isberner, Gabriele (Ed.): Proceedings of the 20th International Conference on Principles of Knowledge Representation and Reasoning, KR 2023, Rhodes, Greece, September 2-8, 2023, pp. 355–364, 2023.
@inproceedings{DBLP:conf/kr/HeyninckC0S23,
title = {Revising Typical Beliefs: One Revision to Rule Them All},
author = {Jesse Heyninck and Giovanni Casini and Thomas Meyer and Umberto Straccia},
editor = {Pierre Marquis and Tran Cao Son and Gabriele Kern-Isberner},
url = {https://doi.org/10.24963/kr.2023/35},
doi = {10.24963/KR.2023/35},
year = {2023},
date = {2023-01-01},
booktitle = {Proceedings of the 20th International Conference on Principles of
Knowledge Representation and Reasoning, KR 2023, Rhodes, Greece,
September 2-8, 2023},
pages = {355–364},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Delgrande, James P.; Glimm, Birte; Meyer, Thomas Andreas; Truszczynski, Miroslaw; Wolter, Frank
Current and Future Challenges in Knowledge Representation and Reasoning Technical Report
2023.
@techreport{DBLP:journals/corr/abs-2308-04161,
title = {Current and Future Challenges in Knowledge Representation and Reasoning},
author = {James P. Delgrande and Birte Glimm and Thomas Andreas Meyer and Miroslaw Truszczynski and Frank Wolter},
url = {https://doi.org/10.48550/arXiv.2308.04161},
doi = {10.48550/ARXIV.2308.04161},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {arXiv preprint arXiv:2308.04161},
volume = {abs/2308.04161},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Gillis-Webber, Frances
Refinement of the Classification of Translations – Extension of the vartrans Module in OntoLex-Lemon Proceedings Article
In: LDK 2023 – 4th Conference on Language, Data and Knowledge, 2023.
@inproceedings{Frances2023refine,
title = {Refinement of the Classification of Translations – Extension of the vartrans Module in OntoLex-Lemon},
author = {Frances Gillis-Webber},
url = {https://aclanthology.org/2023.ldk-1.4/},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {LDK 2023 – 4th Conference on Language, Data and Knowledge},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Taylor, Daniel; Shock, Jonathan; Moodley, Deshendran; Ipser, Jonathan; Treder, Matthias
Brain structural saliency over the ages Proceedings Article
In: Machine Learning, Optimization, and Data Science: 8th International Conference, LOD 2022, Certosa di Pontignano, Italy, September 19–22, 2022, Revised Selected Papers, Part II, pp. 525–548, Springer Nature Switzerland 2023.
@inproceedings{taylor2023brain,
title = {Brain structural saliency over the ages},
author = {Daniel Taylor and Jonathan Shock and Deshendran Moodley and Jonathan Ipser and Matthias Treder},
url = {https://link.springer.com/chapter/10.1007/978-3-031-25891-6_40},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Machine Learning, Optimization, and Data Science: 8th International Conference, LOD 2022, Certosa di Pontignano, Italy, September 19–22, 2022, Revised Selected Papers, Part II},
pages = {525–548},
organization = {Springer Nature Switzerland},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2022
Davidson, Mikhail; Moodley, Deshendran
ST-GNNs for Weather Prediction in South Africa Proceedings Article
In: Artificial Intelligence Research. SACAIR 2022., pp. 93-107, Springer, Cham, 2022.
Abstract | Links | BibTeX | Tags:
@inproceedings{Davidson2022b,
title = {ST-GNNs for Weather Prediction in South Africa},
author = {Mikhail Davidson and Deshendran Moodley},
doi = {10.1007/978-3-031-22321-1_7},
year = {2022},
date = {2022-11-28},
urldate = {2022-11-28},
booktitle = {Artificial Intelligence Research. SACAIR 2022.},
volume = {1734},
pages = {93-107},
publisher = {Springer, Cham},
series = {Communications in Computer and Information Science},
abstract = {Spatial-temporal graph neural networks (ST-GNN) have been shown to be highly effective for flow prediction in dynamic systems, but are under explored for weather prediction applications. We compare and evaluate Graph WaveNet (GWN) and the Low Rank Weighted Graph Neural Network (WGN) for weather prediction in South Africa. We compare these results to two basic temporal deep neural networks architectures, i.e. the Long Short-Term Memory (LSTM) and the Temporal Convolutional Neural Network (TCN), for maximum temperature prediction across 21 weather stations in South Africa. We also perform rigorous experiments to evaluate the stability and robustness of both ST-GNNs. The results show that the GWN model outperforms the other models across different prediction horizons with an average SMAPE score of 8.30%. We also analyse and compare learnt adjacency matrices of the two ST-GNNs to gain insights into the prominent spatial-temporal dependencies between weather stations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Spatial-temporal graph neural networks (ST-GNN) have been shown to be highly effective for flow prediction in dynamic systems, but are under explored for weather prediction applications. We compare and evaluate Graph WaveNet (GWN) and the Low Rank Weighted Graph Neural Network (WGN) for weather prediction in South Africa. We compare these results to two basic temporal deep neural networks architectures, i.e. the Long Short-Term Memory (LSTM) and the Temporal Convolutional Neural Network (TCN), for maximum temperature prediction across 21 weather stations in South Africa. We also perform rigorous experiments to evaluate the stability and robustness of both ST-GNNs. The results show that the GWN model outperforms the other models across different prediction horizons with an average SMAPE score of 8.30%. We also analyse and compare learnt adjacency matrices of the two ST-GNNs to gain insights into the prominent spatial-temporal dependencies between weather stations.
Wang, Steve; Meyer, Thomas; Moodley, Deshen
Defeasible Justification Using the KLM Framework Proceedings Article
In: Pillay, Anban; Jembere, Edgar; Gerber, Aurona (Ed.): Artificial Intelligence Research. SACAIR 2022., pp. 187-201, Springer, Cham, 2022.
Abstract | Links | BibTeX | Tags:
@inproceedings{Wang2022b,
title = {Defeasible Justification Using the KLM Framework},
author = {Steve Wang and Thomas Meyer and Deshen Moodley},
editor = {Anban Pillay and Edgar Jembere and Aurona Gerber},
doi = {10.1007/978-3-031-22321-1_13},
year = {2022},
date = {2022-11-28},
urldate = {2022-11-28},
booktitle = {Artificial Intelligence Research. SACAIR 2022.},
volume = {1743},
pages = {187-201},
publisher = {Springer, Cham},
series = {Communications in Computer and Information Science},
abstract = {The Kraus, Lehmann and Magidor (KLM) framework is an extension of Propositional Logic (PL) that can perform defeasible reasoning. The results of defeasible reasoning using the KLM framework are often challenging to understand. Therefore, one needs a framework within which it is possible to provide justifications for conclusions drawn from defeasible reasoning. This paper proposes a theoretical framework for defeasible justification in PL and a software tool that implements the framework. The theoretical framework is based on an existing theoretical framework for Description Logic (DL). The defeasible justification algorithm uses the statement ranking required by the KLM-style form of defeasible entailment known as Rational Closure. Classical justifications
are computed based on materialised formulas (classical counterparts of defeasible formulas). The resulting classical justifications are converted to defeasible justifications, based on the input knowledge base. We provide an initial evaluation of the framework and the software tool by testing it with a representative example.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The Kraus, Lehmann and Magidor (KLM) framework is an extension of Propositional Logic (PL) that can perform defeasible reasoning. The results of defeasible reasoning using the KLM framework are often challenging to understand. Therefore, one needs a framework within which it is possible to provide justifications for conclusions drawn from defeasible reasoning. This paper proposes a theoretical framework for defeasible justification in PL and a software tool that implements the framework. The theoretical framework is based on an existing theoretical framework for Description Logic (DL). The defeasible justification algorithm uses the statement ranking required by the KLM-style form of defeasible entailment known as Rational Closure. Classical justifications
are computed based on materialised formulas (classical counterparts of defeasible formulas). The resulting classical justifications are converted to defeasible justifications, based on the input knowledge base. We provide an initial evaluation of the framework and the software tool by testing it with a representative example.
are computed based on materialised formulas (classical counterparts of defeasible formulas). The resulting classical justifications are converted to defeasible justifications, based on the input knowledge base. We provide an initial evaluation of the framework and the software tool by testing it with a representative example.
Heyninck, Jesse; Meyer, Thomas
Relevance in the Computation of Non-monotonic Inferences Proceedings Article
In: Pillay, Anban; Jembere, Edgar; Gerber, Aurona (Ed.): Artificial Intelligence Research. SACAIR 2022., pp. 202-214, Springer, Cham, 2022.
Abstract | Links | BibTeX | Tags:
@inproceedings{Heyninck2022b,
title = {Relevance in the Computation of Non-monotonic Inferences},
author = {Jesse Heyninck and Thomas Meyer},
editor = {Anban Pillay and Edgar Jembere and Aurona Gerber},
doi = {10.1007/978-3-031-22321-1_14},
year = {2022},
date = {2022-11-28},
booktitle = {Artificial Intelligence Research. SACAIR 2022.},
volume = {1743},
pages = {202-214},
publisher = {Springer, Cham},
series = {Communications in Computer and Information Science},
abstract = {Inductive inference operators generate non-monotonic inference relations on the basis of a set of conditionals. Examples include rational closure, system P and lexicographic inference. For most of these systems, inference has a high worst-case computational complexity. Recently, the notion of syntax splitting has been formulated, which allows restricting attention to subsets of conditionals relevant for a given query. In this paper, we define algorithms for inductive inference that take advantage of syntax splitting in order to obtain more efficient decision procedures. In particular, we show that relevance allows to use the modularity of knowledge base is a parameter that leads to tractable cases of inference for inductive inference operators such as lexicographic inference.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Inductive inference operators generate non-monotonic inference relations on the basis of a set of conditionals. Examples include rational closure, system P and lexicographic inference. For most of these systems, inference has a high worst-case computational complexity. Recently, the notion of syntax splitting has been formulated, which allows restricting attention to subsets of conditionals relevant for a given query. In this paper, we define algorithms for inductive inference that take advantage of syntax splitting in order to obtain more efficient decision procedures. In particular, we show that relevance allows to use the modularity of knowledge base is a parameter that leads to tractable cases of inference for inductive inference operators such as lexicographic inference.
Casini, Giovanni; Meyer, Thomas; Paterson-Jones, Guy; Varzinczak, Ivan
KLM-Style Defeasibility for Restricted First-Order Logic Conference
Proceedings of the 6th International Joint Conference on Rules and Reasoning, 2022.
Abstract | Links | BibTeX | Tags:
@conference{casini_2022b,
title = {KLM-Style Defeasibility for Restricted First-Order Logic},
author = {Giovanni Casini and Thomas Meyer and Guy Paterson-Jones and Ivan Varzinczak},
doi = {10.1007/978-3-031-21541-4_6},
year = {2022},
date = {2022-09-26},
urldate = {2020-12-11},
booktitle = {Proceedings of the 6th International Joint Conference on Rules and Reasoning},
pages = {10.1007/978-3-031-21541-4_6},
abstract = {In this paper, we extend the KLM approach to defeasible reasoning beyond the propositional setting. We do so by making it applicable to a restricted version of first-order logic. We describe defeasibility for this logic using a set of rationality postulates, provide a suitable and intuitive semantics for it, and present a representation result characterising the semantic description of defeasibility in terms of our postulates. An advantage of our semantics is that it is sufficiently general to be applicable to other restricted versions of first-order logic as well. Based on this theoretical core, we then propose a version of defeasible entailment that is inspired by the well-known notion of Rational Closure as it is defined for defeasible propositional logic and defeasible description logics. We show that this form of defeasible entailment is rational in the sense that it adheres to the full set of rationality postulates.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
In this paper, we extend the KLM approach to defeasible reasoning beyond the propositional setting. We do so by making it applicable to a restricted version of first-order logic. We describe defeasibility for this logic using a set of rationality postulates, provide a suitable and intuitive semantics for it, and present a representation result characterising the semantic description of defeasibility in terms of our postulates. An advantage of our semantics is that it is sufficiently general to be applicable to other restricted versions of first-order logic as well. Based on this theoretical core, we then propose a version of defeasible entailment that is inspired by the well-known notion of Rational Closure as it is defined for defeasible propositional logic and defeasible description logics. We show that this form of defeasible entailment is rational in the sense that it adheres to the full set of rationality postulates.
Price, C. Sue; Moodley, Deshendran; Pillay, Anban; Rens, Gavin
An adaptive probabilistic agent architecture for modelling sugarcane growers’ decision-making Journal Article
In: South African Computer Journal, vol. 34, iss. 1, pp. 152-191, 2022.
Abstract | Links | BibTeX | Tags:
@article{Price2022b,
title = {An adaptive probabilistic agent architecture for modelling sugarcane growers’ decision-making},
author = {C. Sue Price and Deshendran Moodley and Anban Pillay and Gavin Rens},
doi = {10.18489/sacj.v34i1.857},
year = {2022},
date = {2022-07-22},
urldate = {2022-07-00},
journal = {South African Computer Journal},
volume = {34},
issue = {1},
pages = {152-191},
abstract = {Building computational models of agents in dynamic, partially observable and stochastic environments is challenging. We propose a cognitive computational model of sugarcane growers’ daily decision-making to examine sugarcane supply chain complexities. Growers make decisions based on uncertain weather forecasts; cane dryness; unforeseen emergencies; and the mill’s unexpected call for delivery of a different amount of cane. The Belief-Desire-Intention (BDI) architecture has been used to model cognitive agents in many domains, including agriculture. However, typical implementations of this architecture have represented beliefs symbolically, so uncertain beliefs are usually not catered for. Here we show that a BDI architecture, enhanced with a dynamic decision network (DDN), suitably models sugarcane grower agents’ repeated daily decisions. Using two complex scenarios, we demonstrate that the agent selects the appropriate intention, and suggests how the grower should act adaptively and proactively to achieve his goals. In addition, we provide a mapping for using a DDN in a BDI architecture. This architecture can be used for modelling sugarcane grower agents in an agent-based simulation. The mapping of the DDN’s use in the BDI architecture enables this work to be applied to other domains for modelling agents’ repeated decisions in partially observable, stochastic and dynamic environments.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Building computational models of agents in dynamic, partially observable and stochastic environments is challenging. We propose a cognitive computational model of sugarcane growers’ daily decision-making to examine sugarcane supply chain complexities. Growers make decisions based on uncertain weather forecasts; cane dryness; unforeseen emergencies; and the mill’s unexpected call for delivery of a different amount of cane. The Belief-Desire-Intention (BDI) architecture has been used to model cognitive agents in many domains, including agriculture. However, typical implementations of this architecture have represented beliefs symbolically, so uncertain beliefs are usually not catered for. Here we show that a BDI architecture, enhanced with a dynamic decision network (DDN), suitably models sugarcane grower agents’ repeated daily decisions. Using two complex scenarios, we demonstrate that the agent selects the appropriate intention, and suggests how the grower should act adaptively and proactively to achieve his goals. In addition, we provide a mapping for using a DDN in a BDI architecture. This architecture can be used for modelling sugarcane grower agents in an agent-based simulation. The mapping of the DDN’s use in the BDI architecture enables this work to be applied to other domains for modelling agents’ repeated decisions in partially observable, stochastic and dynamic environments.
Wanyana, Tezira; Nzomo, Mbithe; Price, C. Sue; Moodley, Deshendran
Combining Machine Learning and Bayesian Networks for ECG Interpretation and Explanation Conference
Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health, SciTePress, INSTICC, 2022, ISBN: 978-989-758-566-1.
Abstract | Links | BibTeX | Tags:
@conference{Wanyana2022b,
title = {Combining Machine Learning and Bayesian Networks for ECG Interpretation and Explanation},
author = {Tezira Wanyana and Mbithe Nzomo and C. Sue Price and Deshendran Moodley},
doi = {10.5220/0011046100003188},
isbn = {978-989-758-566-1},
year = {2022},
date = {2022-04-23},
urldate = {2022-04-23},
booktitle = {Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health},
journal = {Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE},
pages = {81-92},
publisher = {SciTePress},
address = {INSTICC},
abstract = {We explore how machine learning (ML) and Bayesian networks (BNs) can be combined in a personal health agent (PHA) for the detection and interpretation of electrocardiogram (ECG) characteristics. We propose a PHA that uses ECG data from wearables to monitor heart activity, and interprets and explains the observed readings. We focus on atrial fibrillation (AF), the commonest type of arrhythmia. The absence of a P-wave in an ECG is the hallmark indication of AF. Four ML models are trained to classify an ECG signal based on the presence or absence of the P-wave: multilayer perceptron (MLP), logistic regression, support vector machine, and random forest. The MLP is the best performing model with an accuracy of 89.61% and an F1 score of 88.68%. A BN representing AF risk factors is developed based on expert knowledge from the literature and evaluated using Pitchforth and Mengersen’s validation framework. The P-wave presence or absence as determined by the ML model is input into the BN. The PHA is evaluated using sample use cases to illustrate how the BN can explain the occurrence of AF using diagnostic reasoning. This gives the most likely AF risk factors for the individual},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
We explore how machine learning (ML) and Bayesian networks (BNs) can be combined in a personal health agent (PHA) for the detection and interpretation of electrocardiogram (ECG) characteristics. We propose a PHA that uses ECG data from wearables to monitor heart activity, and interprets and explains the observed readings. We focus on atrial fibrillation (AF), the commonest type of arrhythmia. The absence of a P-wave in an ECG is the hallmark indication of AF. Four ML models are trained to classify an ECG signal based on the presence or absence of the P-wave: multilayer perceptron (MLP), logistic regression, support vector machine, and random forest. The MLP is the best performing model with an accuracy of 89.61% and an F1 score of 88.68%. A BN representing AF risk factors is developed based on expert knowledge from the literature and evaluated using Pitchforth and Mengersen’s validation framework. The P-wave presence or absence as determined by the ML model is input into the BN. The PHA is evaluated using sample use cases to illustrate how the BN can explain the occurrence of AF using diagnostic reasoning. This gives the most likely AF risk factors for the individual
Aarons, Shai; Moodley, D; Nzomo, M
A Generalizable Hybrid Deep Learning Algorithm for the Detection of Atrial Fibrillation from Diverse Electrocardiogram Data Technical Report
Technical Report, University of Cape Town, Cape Town, 2022. URL: https~… 2022.
@techreport{aaronsgeneralizable,
title = {A Generalizable Hybrid Deep Learning Algorithm for the Detection of Atrial Fibrillation from Diverse Electrocardiogram Data},
author = {Shai Aarons and D Moodley and M Nzomo},
url = {https://projects.cs.uct.ac.za/honsproj/cgi-bin/view/2022/aarons_fisher_rosenthal.zip/deliverables/finalpapershai.pdf},
year = {2022},
date = {2022-03-01},
urldate = {2022-03-01},
institution = {Technical Report, University of Cape Town, Cape Town, 2022. URL: https~…},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
Everett, Lloyd; Morris, Emily; Meyer, Thomas
Explanation for KLM-Style Defeasible Reasoning Proceedings Article
In: Jembere, Edgar; Gerber, Aurona; Viriri, Serestina; Pillay, Anban (Ed.): Artificial Intelligence Research. SACAIR 2021., pp. 192-207, Springer, Cham, 2022.
Abstract | Links | BibTeX | Tags:
@inproceedings{Everett2022b,
title = {Explanation for KLM-Style Defeasible Reasoning},
author = {Lloyd Everett and Emily Morris and Thomas Meyer},
editor = {Edgar Jembere and Aurona Gerber and Serestina Viriri and Anban Pillay},
doi = {10.1007/978-3-030-95070-5_13},
year = {2022},
date = {2022-01-29},
urldate = {2022-01-29},
booktitle = {Artificial Intelligence Research. SACAIR 2021.},
volume = {1551},
pages = {192-207},
publisher = {Springer, Cham},
series = {Communications in Computer and Information Science},
abstract = {Explanation services are a crucial aspect of symbolic reasoning systems but they have not been explored in detail for defeasible formalisms such as KLM. We evaluate prior work on the topic with a focus on KLM propositional logic and find that a form of defeasible explanation initially described for Rational Closure which we term weak justification can be adapted to Relevant and Lexicographic Closure as well as described in terms of intuitive properties derived from the KLM postulates. We also consider how a more general definition of defeasible explanation known as strong explanation applies to KLM and propose an algorithm that enumerates these justifications for Rational Closure.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Explanation services are a crucial aspect of symbolic reasoning systems but they have not been explored in detail for defeasible formalisms such as KLM. We evaluate prior work on the topic with a focus on KLM propositional logic and find that a form of defeasible explanation initially described for Rational Closure which we term weak justification can be adapted to Relevant and Lexicographic Closure as well as described in terms of intuitive properties derived from the KLM postulates. We also consider how a more general definition of defeasible explanation known as strong explanation applies to KLM and propose an algorithm that enumerates these justifications for Rational Closure.
Baker, Clayton; Meyer, Tommie
Belief Change in Human Reasoning: An Empirical Investigation on MTurk Conference
Proceedings of the 2nd Southern African Conference for Artificial Intelligence Research (SACAIR), Online, 2022.
Abstract | Links | BibTeX | Tags:
@conference{Baker2022b,
title = {Belief Change in Human Reasoning: An Empirical Investigation on MTurk},
author = {Clayton Baker and Tommie Meyer},
url = {https://2021.sacair.org.za/proceedings/},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Proceedings of the 2nd Southern African Conference for Artificial Intelligence Research (SACAIR)},
journal = {Second Southern African Conference for AI Research (SACAIR 2022)},
pages = {218-234},
address = {Online},
abstract = {Belief revision and belief update are approaches to represent and reason with knowledge in artificial intelligence. Previous empirical studies have shown that human reasoning is consistent with non-monotonic logic and postulates of defeasible reasoning, belief revision and belief update. We extended previous work, which tested natural language translations of the postulates of defeasible reasoning, belief revision and belief update with human reasoners via surveys, in three respects. Firstly, we only tested postulates of belief revision and belief update, taking the position that belief change aligns more with human reasoning than non-monotonic defeasible reasoning. Secondly, we decomposed the postulates of revision and update into material implication statements of the form “If x is the case, then y is the case”, each containing a premise
and a conclusion, and then translated the premises and conclusions into natural language. Thirdly, we asked human participants to judge each component of the postulate for plausibility. In our analysis, we measured the strength of the association between the premises and the conclusion of each postulate. We used Possibility theory to determine whether the postulates hold with our participants in general. Our results showed that our participants’ reasoning is consistent with postulates of belief
revision and belief update when judging the premises and conclusion of the postulate separately.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Belief revision and belief update are approaches to represent and reason with knowledge in artificial intelligence. Previous empirical studies have shown that human reasoning is consistent with non-monotonic logic and postulates of defeasible reasoning, belief revision and belief update. We extended previous work, which tested natural language translations of the postulates of defeasible reasoning, belief revision and belief update with human reasoners via surveys, in three respects. Firstly, we only tested postulates of belief revision and belief update, taking the position that belief change aligns more with human reasoning than non-monotonic defeasible reasoning. Secondly, we decomposed the postulates of revision and update into material implication statements of the form “If x is the case, then y is the case”, each containing a premise
and a conclusion, and then translated the premises and conclusions into natural language. Thirdly, we asked human participants to judge each component of the postulate for plausibility. In our analysis, we measured the strength of the association between the premises and the conclusion of each postulate. We used Possibility theory to determine whether the postulates hold with our participants in general. Our results showed that our participants’ reasoning is consistent with postulates of belief
revision and belief update when judging the premises and conclusion of the postulate separately.
and a conclusion, and then translated the premises and conclusions into natural language. Thirdly, we asked human participants to judge each component of the postulate for plausibility. In our analysis, we measured the strength of the association between the premises and the conclusion of each postulate. We used Possibility theory to determine whether the postulates hold with our participants in general. Our results showed that our participants’ reasoning is consistent with postulates of belief
revision and belief update when judging the premises and conclusion of the postulate separately.
Heyninck, Jesse; Kern-Isberner, Gabriele; Meyer, Thomas Andreas
Lexicographic Entailment, Syntax Splitting and the Drowning Problem Proceedings Article
In: Raedt, Luc De (Ed.): Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022, pp. 2662–2668, ijcai.org, 2022.
@inproceedings{DBLP:conf/ijcai/HeyninckKM22,
title = {Lexicographic Entailment, Syntax Splitting and the Drowning Problem},
author = {Jesse Heyninck and Gabriele Kern-Isberner and Thomas Andreas Meyer},
editor = {Luc De Raedt},
url = {https://doi.org/10.24963/ijcai.2022/369},
doi = {10.24963/IJCAI.2022/369},
year = {2022},
date = {2022-01-01},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July
2022},
pages = {2662–2668},
publisher = {ijcai.org},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Heyninck, Jesse; Kern-Isberner, Gabriele; Meyer, Thomas Andreas
Conditional Syntax Splitting, Lexicographic Entailment and the Drowning Effect Proceedings Article
In: Arieli, Ofer; Casini, Giovanni; Giordano, Laura (Ed.): Proceedings of the 20th International Workshop on Non-Monotonic Reasoning, NMR 2022, Part of the Federated Logic Conference (FLoC 2022), Haifa, Israel, August 7-9, 2022, pp. 61–69, CEUR-WS.org, 2022.
@inproceedings{DBLP:conf/nmr/HeyninckKM22,
title = {Conditional Syntax Splitting, Lexicographic Entailment and the Drowning
Effect},
author = {Jesse Heyninck and Gabriele Kern-Isberner and Thomas Andreas Meyer},
editor = {Ofer Arieli and Giovanni Casini and Laura Giordano},
url = {https://ceur-ws.org/Vol-3197/paper6.pdf},
year = {2022},
date = {2022-01-01},
booktitle = {Proceedings of the 20th International Workshop on Non-Monotonic Reasoning,
NMR 2022, Part of the Federated Logic Conference (FLoC 2022), Haifa,
Israel, August 7-9, 2022},
volume = {3197},
pages = {61–69},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Baker, Clayton K.; Meyer, Thomas Andreas
Asking Human Reasoners to Judge Postulates of Belief Change for Plausibility Proceedings Article
In: Arieli, Ofer; Casini, Giovanni; Giordano, Laura (Ed.): Proceedings of the 20th International Workshop on Non-Monotonic Reasoning, NMR 2022, Part of the Federated Logic Conference (FLoC 2022), Haifa, Israel, August 7-9, 2022, pp. 139–142, CEUR-WS.org, 2022.
@inproceedings{DBLP:conf/nmr/BakerM22,
title = {Asking Human Reasoners to Judge Postulates of Belief Change for Plausibility},
author = {Clayton K. Baker and Thomas Andreas Meyer},
editor = {Ofer Arieli and Giovanni Casini and Laura Giordano},
url = {https://ceur-ws.org/Vol-3197/short1.pdf},
year = {2022},
date = {2022-01-01},
booktitle = {Proceedings of the 20th International Workshop on Non-Monotonic Reasoning,
NMR 2022, Part of the Federated Logic Conference (FLoC 2022), Haifa,
Israel, August 7-9, 2022},
volume = {3197},
pages = {139–142},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Casini, Giovanni; Meyer, Thomas Andreas; Varzinczak, Ivan
Situated Conditionals - A Brief Introduction Proceedings Article
In: Arieli, Ofer; Casini, Giovanni; Giordano, Laura (Ed.): Proceedings of the 20th International Workshop on Non-Monotonic Reasoning, NMR 2022, Part of the Federated Logic Conference (FLoC 2022), Haifa, Israel, August 7-9, 2022, pp. 151–154, CEUR-WS.org, 2022.
@inproceedings{DBLP:conf/nmr/CasiniMV22,
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Casini, Giovanni; Meyer, Thomas; Paterson-Jones, Guy; Varzinczak, Ivan
KLM-Style Defeasibility for Restricted First-Order Logic Proceedings Article
In: Governatori, Guido; Turhan, Anni-Yasmin (Ed.): Rules and Reasoning - 6th International Joint Conference on Rules and Reasoning, RuleML+RR 2022, Berlin, Germany, September 26-28, 2022, Proceedings, pp. 81–94, Springer, 2022.
@inproceedings{DBLP:conf/ruleml/CasiniMPV22,
title = {KLM-Style Defeasibility for Restricted First-Order Logic},
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Wang, Steve; Meyer, Thomas; Moodley, Deshendran
Defeasible Justification Using the KLM Framework Proceedings Article
In: Pillay, Anban W.; Jembere, Edgar; Gerber, Aurona (Ed.): Artificial Intelligence Research - Third Southern African Conference, SACAIR 2022, Stellenbosch, South Africa, December 5-9, 2022, Proceedings, pp. 187–201, Springer, 2022.
@inproceedings{DBLP:conf/sacair/Wang0M22,
title = {Defeasible Justification Using the KLM Framework},
author = {Steve Wang and Thomas Meyer and Deshendran Moodley},
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Heyninck, Jesse; Meyer, Thomas
Relevance in the Computation of Non-monotonic Inferences Proceedings Article
In: Pillay, Anban W.; Jembere, Edgar; Gerber, Aurona (Ed.): Artificial Intelligence Research - Third Southern African Conference, SACAIR 2022, Stellenbosch, South Africa, December 5-9, 2022, Proceedings, pp. 202–214, Springer, 2022.
@inproceedings{DBLP:conf/sacair/Heyninck022,
title = {Relevance in the Computation of Non-monotonic Inferences},
author = {Jesse Heyninck and Thomas Meyer},
editor = {Anban W. Pillay and Edgar Jembere and Aurona Gerber},
url = {https://doi.org/10.1007/978-3-031-22321-1_14},
doi = {10.1007/978-3-031-22321-1_14},
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Delgrande, James P.; Glimm, Birte; Meyer, Thomas; Truszczynski, Miroslaw; Teixeira, Milene Santos; Wolter, Frank
Current and Future Challenges in Knowledge Representation and Reasoning (Dagstuhl Seminar 22282) Technical Report
no. 7, 2022.
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title = {Current and Future Challenges in Knowledge Representation and Reasoning (Dagstuhl Seminar 22282)},
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url = {https://doi.org/10.4230/DagRep.12.7.62},
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Pillay, Kialan; Moodley, Deshendran
Exploring Graph Neural Networks for Stock Market Prediction on the JSE Proceedings Article
In: Southern African Conference for Artificial Intelligence Research, pp. 95–110, Springer, Cham 2022.
@inproceedings{pillay2022exploring,
title = {Exploring Graph Neural Networks for Stock Market Prediction on the JSE},
author = {Kialan Pillay and Deshendran Moodley},
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year = {2022},
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Drake, Rachel; Moodley, Deshendran
INVEST: Ontology Driven Bayesian Networks for Investment Decision Making on the JSE Proceedings Article
In: Southern African Conference for Artificial Intelligence Research (SACAIR), pp. 252–273, https://2021.sacair.org.za/wp-content/uploads/2022/02/SACAIR21-Proceedings~… 2022.
@inproceedings{drake2022invest,
title = {INVEST: Ontology Driven Bayesian Networks for Investment Decision Making on the JSE},
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year = {2022},
date = {2022-01-01},
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Blem, Morgan; Cristaudo, Chesney; Moodley, Deshendran
Deep Neural Networks For Online Trend Prediction Proceedings Article
In: 2022 25th International Conference on Information Fusion (FUSION), pp. 1–8, IEEE 2022.
@inproceedings{blem2022deep,
title = {Deep Neural Networks For Online Trend Prediction},
author = {Morgan Blem and Chesney Cristaudo and Deshendran Moodley},
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year = {2022},
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2021
Casini, Giovanni; Meyer, Thomas; Varzinczak, Ivan
Contextual Conditional Reasoning Conference
Proceedings of the 35th AAAI Conference on Artificial Intelligence, vol. 35, 2021.
Abstract | Links | BibTeX | Tags:
@conference{Casini2021b,
title = {Contextual Conditional Reasoning},
author = {Giovanni Casini and Thomas Meyer and Ivan Varzinczak},
doi = {10.1609/aaai.v35i7.16777},
year = {2021},
date = {2021-05-18},
urldate = {2021-05-18},
booktitle = {Proceedings of the 35th AAAI Conference on Artificial Intelligence},
volume = {35},
issue = {7},
pages = {6254-6261},
abstract = {We extend the expressivity of classical conditional reasoning by introducing context as a new parameter. The enriched conditional logic generalises the defeasible conditional setting in the style of Kraus, Lehmann, and Magidor, and allows for a refined semantics that is able to distinguish, for example, between expectations and counterfactuals. In this paper we introduce the language for the enriched logic and define an appropriate semantic framework for it. We analyse which properties generally associated with conditional reasoning are still satisfied by the new semantic framework, provide a suitable representation result, and define an entailment relation based on Lehmann and Magidor’s generally-accepted notion of Rational Closure.},
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We extend the expressivity of classical conditional reasoning by introducing context as a new parameter. The enriched conditional logic generalises the defeasible conditional setting in the style of Kraus, Lehmann, and Magidor, and allows for a refined semantics that is able to distinguish, for example, between expectations and counterfactuals. In this paper we introduce the language for the enriched logic and define an appropriate semantic framework for it. We analyse which properties generally associated with conditional reasoning are still satisfied by the new semantic framework, provide a suitable representation result, and define an entailment relation based on Lehmann and Magidor’s generally-accepted notion of Rational Closure.
Leenen, Louise; Meyer, Thomas
Artificial Intelligence and Big Data Analytics in Support of Cyber Defense Book Chapter
In: Association, Information Resources Management (Ed.): Research Anthology on Artificial Intelligence Applications in Security, Chapter 76, pp. 1738-1753, IGI Global, 2021.
Abstract | Links | BibTeX | Tags:
@inbook{Leenan2021b,
title = {Artificial Intelligence and Big Data Analytics in Support of Cyber Defense},
author = {Louise Leenen and Thomas Meyer},
editor = {Information Resources Management Association},
doi = {10.4018/978-1-7998-7705-9.ch076},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {Research Anthology on Artificial Intelligence Applications in Security},
pages = {1738-1753},
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chapter = {76},
abstract = {Cybersecurity analysts rely on vast volumes of security event data to predict, identify, characterize, and deal with security threats. These analysts must understand and make sense of these huge datasets in order to discover patterns which lead to intelligent decision making and advance warnings of possible threats, and this ability requires automation. Big data analytics and artificial intelligence can improve cyber defense. Big data analytics methods are applied to large data sets that contain different data types. The purpose is to detect patterns, correlations, trends, and other useful information. Artificial intelligence provides algorithms that can reason or learn and improve their behavior, and includes semantic technologies. A large number of automated systems are currently based on syntactic rules which are generally not sophisticated enough to deal with the level of complexity in this domain. An overview of artificial intelligence and big data technologies in cyber defense is provided, and important areas for future research are identified and discussed.},
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Cybersecurity analysts rely on vast volumes of security event data to predict, identify, characterize, and deal with security threats. These analysts must understand and make sense of these huge datasets in order to discover patterns which lead to intelligent decision making and advance warnings of possible threats, and this ability requires automation. Big data analytics and artificial intelligence can improve cyber defense. Big data analytics methods are applied to large data sets that contain different data types. The purpose is to detect patterns, correlations, trends, and other useful information. Artificial intelligence provides algorithms that can reason or learn and improve their behavior, and includes semantic technologies. A large number of automated systems are currently based on syntactic rules which are generally not sophisticated enough to deal with the level of complexity in this domain. An overview of artificial intelligence and big data technologies in cyber defense is provided, and important areas for future research are identified and discussed.
Britz, Katarina; Casini, Giovanni; Meyer, Thomas; Moodley, Kody; Sattler, Uli; Varzinczak, Ivan
Principles of KLM-style Defeasible Description Logics Journal Article
In: ACM Transactions on Computational Logic (TOCL), vol. 22, no. 1, pp. 1-46, 2021.
Abstract | Links | BibTeX | Tags:
@article{DBLP:journals/tocl/BritzCMMSV21,
title = {Principles of KLM-style Defeasible Description Logics},
author = {Katarina Britz and Giovanni Casini and Thomas Meyer and Kody Moodley and Uli Sattler and Ivan Varzinczak},
url = {https://doi.org/10.1145/3420258},
doi = {10.1145/3420258},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {ACM Transactions on Computational Logic (TOCL)},
volume = {22},
number = {1},
pages = {1-46},
abstract = {The past 25 years have seen many attempts to introduce defeasible-reasoning capabilities into a description logic setting. Many, if not most, of these attempts are based on preferential extensions of description logics, with a significant number of these, in turn, following the so-called KLM approach to defeasible reasoning initially advocated for propositional logic by Kraus, Lehmann, and Magidor. Each of these attempts has its own aim of investigating particular constructions and variants of the (KLM-style) preferential approach. Here our aim is to provide a comprehensive study of the formal foundations of preferential defeasible reasoning for description logics in the KLM tradition.
We start by investigating a notion of defeasible subsumption in the spirit of defeasible conditionals as studied by Kraus, Lehmann, and Magidor in the propositional case. In particular, we consider a natural and intuitive semantics for defeasible subsumption, and we investigate KLM-style syntactic properties for both preferential and rational subsumption. Our contribution includes two representation results linking our semantic constructions to the set of preferential and rational properties considered. Besides showing that our semantics is appropriate, these results pave the way for more effective decision procedures for defeasible reasoning in description logics. Indeed, we also analyse the problem of non-monotonic reasoning in description logics at the level of entailment and present an algorithm for the computation of rational closure of a defeasible knowledge base. Importantly, our algorithm relies completely on classical entailment and shows that the computational complexity of reasoning over defeasible knowledge bases is no worse than that of reasoning in the underlying classical DL ALC.},
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The past 25 years have seen many attempts to introduce defeasible-reasoning capabilities into a description logic setting. Many, if not most, of these attempts are based on preferential extensions of description logics, with a significant number of these, in turn, following the so-called KLM approach to defeasible reasoning initially advocated for propositional logic by Kraus, Lehmann, and Magidor. Each of these attempts has its own aim of investigating particular constructions and variants of the (KLM-style) preferential approach. Here our aim is to provide a comprehensive study of the formal foundations of preferential defeasible reasoning for description logics in the KLM tradition.
We start by investigating a notion of defeasible subsumption in the spirit of defeasible conditionals as studied by Kraus, Lehmann, and Magidor in the propositional case. In particular, we consider a natural and intuitive semantics for defeasible subsumption, and we investigate KLM-style syntactic properties for both preferential and rational subsumption. Our contribution includes two representation results linking our semantic constructions to the set of preferential and rational properties considered. Besides showing that our semantics is appropriate, these results pave the way for more effective decision procedures for defeasible reasoning in description logics. Indeed, we also analyse the problem of non-monotonic reasoning in description logics at the level of entailment and present an algorithm for the computation of rational closure of a defeasible knowledge base. Importantly, our algorithm relies completely on classical entailment and shows that the computational complexity of reasoning over defeasible knowledge bases is no worse than that of reasoning in the underlying classical DL ALC.
We start by investigating a notion of defeasible subsumption in the spirit of defeasible conditionals as studied by Kraus, Lehmann, and Magidor in the propositional case. In particular, we consider a natural and intuitive semantics for defeasible subsumption, and we investigate KLM-style syntactic properties for both preferential and rational subsumption. Our contribution includes two representation results linking our semantic constructions to the set of preferential and rational properties considered. Besides showing that our semantics is appropriate, these results pave the way for more effective decision procedures for defeasible reasoning in description logics. Indeed, we also analyse the problem of non-monotonic reasoning in description logics at the level of entailment and present an algorithm for the computation of rational closure of a defeasible knowledge base. Importantly, our algorithm relies completely on classical entailment and shows that the computational complexity of reasoning over defeasible knowledge bases is no worse than that of reasoning in the underlying classical DL ALC.
Botha, Leonard; Meyer, Thomas Andreas; Peñaloza, Rafael
The Probabilistic Description Logic Journal Article
In: Theory and Practice of Logic Programming, vol. 21, no. 4, pp. 404–427, 2021.
@article{DBLP:journals/tplp/BothaMP21,
title = {The Probabilistic Description Logic},
author = {Leonard Botha and Thomas Andreas Meyer and Rafael Peñaloza},
url = {https://doi.org/10.1017/S1471068420000460},
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Casini, Giovanni; Meyer, Thomas Andreas; Varzinczak, Ivan
Contextual Conditional Reasoning Proceedings Article
In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021, pp. 6254–6261, AAAI Press, 2021.
@inproceedings{DBLP:conf/aaai/CasiniMV21,
title = {Contextual Conditional Reasoning},
author = {Giovanni Casini and Thomas Andreas Meyer and Ivan Varzinczak},
url = {https://doi.org/10.1609/aaai.v35i7.16777},
doi = {10.1609/AAAI.V35I7.16777},
year = {2021},
date = {2021-01-01},
booktitle = {Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI
2021, Thirty-Third Conference on Innovative Applications of Artificial
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Everett, Lloyd; Morris, Emily; Meyer, Thomas
Explanation for KLM-Style Defeasible Reasoning Proceedings Article
In: Jembere, Edgar; Gerber, Aurona J.; Viriri, Serestina; Pillay, Anban W. (Ed.): Artificial Intelligence Research - Second Southern African Conference, SACAIR 2021, Durban, South Africa, December 6-10, 2021, Proceedings, pp. 192–207, Springer, 2021.
@inproceedings{DBLP:conf/sacair/EverettMM21,
title = {Explanation for KLM-Style Defeasible Reasoning},
author = {Lloyd Everett and Emily Morris and Thomas Meyer},
editor = {Edgar Jembere and Aurona J. Gerber and Serestina Viriri and Anban W. Pillay},
url = {https://doi.org/10.1007/978-3-030-95070-5_13},
doi = {10.1007/978-3-030-95070-5_13},
year = {2021},
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Kouassi, Kouame; Moodley, Deshendran
Automated deep learning for trend prediction in time series data Proceedings Article
In: 2021 IEEE 24th International Conference on Information Fusion (FUSION), pp. 1–8, IEEE 2021.
@inproceedings{kouassi2021automated,
title = {Automated deep learning for trend prediction in time series data},
author = {Kouame Kouassi and Deshendran Moodley},
url = {https://ieeexplore.ieee.org/abstract/document/9626910},
year = {2021},
date = {2021-01-01},
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Wanyana, Tezira; Moodley, Deshendran
An agent architecture for knowledge discovery and evolution Proceedings Article
In: KI 2021: Advances in Artificial Intelligence: 44th German Conference on AI, Virtual Event, September 27–October 1, 2021, Proceedings 44, pp. 241–256, Springer International Publishing 2021.
@inproceedings{wanyana2021agent,
title = {An agent architecture for knowledge discovery and evolution},
author = {Tezira Wanyana and Deshendran Moodley},
url = {https://link.springer.com/chapter/10.1007/978-3-030-87626-5_18},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {KI 2021: Advances in Artificial Intelligence: 44th German Conference on AI, Virtual Event, September 27–October 1, 2021, Proceedings 44},
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