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Pacome L. Ambassa is a young researcher heading toward the completion of his PhD degree with the Department of Computer Science at the University of Cape Town, South Africa. He received the BSc and MSc degrees in computer science from the University of Ngaoundere (Cameroon) in 2009 and 2013, respectively. His research interest includes security and privacy for e-healthcare systems, cyber-physical systems, smart grid system and critical infrastructures protection. As part of his PhD research programme, he completed 2 research visits (April -- May 2015) and (March-May 2016), respectively at Norwegian Information Security Laboratory, Norwegian University of Science and Technology, Gjovik, Norway, under the supervision of Prof. Stephen D. Wolthusen. He was recognized as an outstanding young researcher in computer science selected out of a hundred applicants over the world to attend the 4th Heidelberg Laureate Forum in 2016. He has co-authored many scientific publications in peer-reviewed International Journals and Conferences and as book chapter. Pacome has served in the Technical Committee member for IoTsm 2018. He is a reviewer for numerous international conferences and Journals. Aside from his research skills, Pacome is also involved in many leadership activities through community empowerment and awareness.
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I am a doctoral student researcher under the supervision of Prof. Tommie Meyer.
I employ empirical methods, that stem from data science and cognitive science, to evaluate the theory of belief revision with human reasoners.
I have been a part of the Knowledge Representation and Reasoning research group since 2019.
I employ empirical methods, that stem from data science and cognitive science, to evaluate the theory of belief revision with human reasoners.
I have been a part of the Knowledge Representation and Reasoning research group since 2019.
Latest Research Publications:
Baker CK, Meyer T. Belief Change in Human Reasoning: An Empirical Investigation on MTurk. In: Second Southern African Conference for AI Research (SACAIR 2022). Online: SACAIR 2021 Organising Committee; 2022. https://2021.sacair.org.za/proceedings/.
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.
@{427,
author = {Clayton Baker, Tommie Meyer},
title = {Belief Change in Human Reasoning: An Empirical Investigation on MTurk},
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.},
year = {2022},
journal = {Second Southern African Conference for AI Research (SACAIR 2022)},
pages = {218-234},
month = {06/12/2021-10/12/2021},
publisher = {SACAIR 2021 Organising Committee},
address = {Online},
isbn = {978-0-620-94410-6},
url = {https://2021.sacair.org.za/proceedings/},
}
Baker CK, Denny C, Freund P, Meyer T. Cognitive Defeasible Reasoning: the Extent to which Forms of Defeasible Reasoning Correspond with Human Reasoning. In: First Southern African Conference for AI Research (SACAIR 2020). Muldersdrift, South Africa: Springer; 2020. doi:10.1007/978-3-030-66151-9_13.
Classical logic forms the basis of knowledge representation and reasoning in AI. In the real world, however, classical logic alone is insufficient to describe the reasoning behaviour of human beings. It lacks the flexibility so characteristically required of reasoning under uncertainty, reasoning under incomplete information and reasoning with new information, as humans must. In response, non-classical extensions to propositional logic have been formulated, to provide non-monotonicity. It has been shown in previous studies that human reasoning exhibits non-monotonicity. This work is the product of merging three independent studies, each one focusing on a different formalism for non-monotonic reasoning: KLM defeasible reasoning, AGM belief revision and KM belief update. We investigate, for each of the postulates propounded to characterise these logic forms, the extent to which they have correspondence with human reasoners. We do this via three respective experiments and present each of the postulates in concrete and abstract form. We discuss related work, our experiment design, testing and evaluation, and report on the results from our experiments. We find evidence to believe that 1 out of 5 KLM defeasible reasoning postulates, 3 out of 8 AGM belief revision postulates and 4 out of 8 KM belief update postulates conform in both the concrete and abstract case. For each experiment, we performed an additional investigation. In the experiments of KLM defeasible reasoning and AGM belief revision, we analyse the explanations given by participants to determine whether the postulates have a normative or descriptive relationship with human reasoning. We find evidence that suggests, overall, KLM defeasible reasoning has a normative relationship with human reasoning while AGM belief revision has a descriptive relationship with human reasoning. In the experiment of KM belief update, we discuss counter-examples to the KM postulates.
@{412,
author = {Clayton Baker, Claire Denny, Paul Freund, Tommie Meyer},
title = {Cognitive Defeasible Reasoning: the Extent to which Forms of Defeasible Reasoning Correspond with Human Reasoning},
abstract = {Classical logic forms the basis of knowledge representation and reasoning in AI. In the real world, however, classical logic alone is insufficient to describe the reasoning behaviour of human beings. It lacks the flexibility so characteristically required of reasoning under uncertainty, reasoning under incomplete information and reasoning with new information, as humans must. In response, non-classical extensions to propositional logic have been formulated, to provide non-monotonicity. It has been shown in previous studies that human reasoning exhibits non-monotonicity. This work is the product of merging three independent studies, each one focusing on a different formalism for non-monotonic reasoning: KLM defeasible reasoning, AGM belief revision and KM belief update. We investigate, for each of the postulates propounded to characterise these logic forms, the extent to which they have correspondence with human reasoners. We do this via three respective experiments and present each of the postulates in concrete and abstract form. We discuss related work, our experiment design, testing and evaluation, and report on the results from our experiments. We find evidence to believe that 1 out of 5 KLM defeasible reasoning postulates, 3 out of 8 AGM belief revision postulates and 4 out of 8 KM belief update postulates conform in both the concrete and abstract case. For each experiment, we performed an additional investigation. In the experiments of KLM defeasible reasoning and AGM belief revision, we analyse the explanations given by participants to determine whether the postulates have a normative or descriptive relationship with human reasoning. We find evidence that suggests, overall, KLM defeasible reasoning has a normative relationship with human reasoning while AGM belief revision has a descriptive relationship with human reasoning. In the experiment of KM belief update, we discuss counter-examples to the KM postulates.},
year = {2020},
journal = {First Southern African Conference for AI Research (SACAIR 2020)},
pages = {199-219},
month = {22/02/2021-26/02/2021},
publisher = {Springer},
address = {Muldersdrift, South Africa},
isbn = {978-3-030-66151-9},
url = {https://link.springer.com/book/10.1007/978-3-030-66151-9},
doi = {10.1007/978-3-030-66151-9_13},
}
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Prof Etienne Barnard holds a PhD degree in Electrical and Computer Engineering from Carnegie Mellon University, and has been active in research and development in pattern recognition and speech processing for many years. He has held a number of academic and industry positions, holds international patents in speech processing, and is a past Associate Editor of the IEEE Transactions on Neural Networks. Barnard’s research contributions have been recognized in various ways, including Google Research Awards in 2009, 2011 and 2014. He is currently an extraordinary professor at the NWU.
Latest Research Publications:
Davel MH, Theunissen MW, Pretorius AP, Barnard E. DNNs as layers of cooperating classifiers. In: The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20). New York; 2020.
A robust theoretical framework that can describe and predict the generalization ability of deep neural networks (DNNs) in general circumstances remains elusive. Classical attempts have produced complexity metrics that rely heavily on global measures of compactness and capacity with little investigation into the effects of sub-component collaboration. We demonstrate intriguing regularities in the activation patterns of the hidden nodes within fully-connected feedforward networks. By tracing the origin of these patterns, we show how such networks can be viewed as the combination of two information processing systems: one continuous and one discrete. We describe how these two systems arise naturally from the gradient-based optimization process, and demonstrate the classification ability of the two systems, individually and in collaboration. This perspective on DNN classification offers a novel way to think about generalization, in which different subsets of the training data are used to train distinct classifiers; those classifiers are then combined to perform the classification task, and their consistency is crucial for accurate classification.
@{236,
author = {Marelie Davel, Marthinus Theunissen, Arnold Pretorius, Etienne Barnard},
title = {DNNs as layers of cooperating classifiers},
abstract = {A robust theoretical framework that can describe and predict the generalization ability of deep neural networks (DNNs) in general circumstances remains elusive. Classical attempts have produced complexity metrics that rely heavily on global measures of compactness and capacity with little investigation into the effects of sub-component collaboration. We demonstrate intriguing regularities in the activation patterns of the hidden nodes within fully-connected feedforward networks. By tracing the origin of these patterns, we show how such networks can be viewed as the combination of two information processing systems: one continuous and one discrete. We describe how these two systems arise naturally from the gradient-based optimization process, and demonstrate the classification ability of the two systems, individually and in collaboration. This perspective on DNN classification offers a novel way to think about generalization, in which different subsets of the training data are used to train distinct classifiers; those classifiers are then combined to perform the classification task, and their consistency is crucial for accurate classification.},
year = {2020},
journal = {The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20)},
pages = {3725 - 3732},
month = {07/02-12/02/2020},
address = {New York},
}
Theunissen MW, Davel MH, Barnard E. Insights regarding overfitting on noise in deep learning. In: South African Forum for Artificial Intelligence Research (FAIR). Cape Town, South Africa; 2019.
The understanding of generalization in machine learning is in a state of flux. This is partly due to the elatively recent revelation that deep learning models are able to completely memorize training data and still perform appropriately on out-of-sample data, thereby contradicting long-held intuitions about generalization. The phenomenon was brought to light and discussed in a seminal paper by Zhang et al. [24]. We expand upon this work by discussing local attributes of neural network training within the context of a relatively simple and generalizable framework. We describe how various types of noise can be compensated for within the proposed framework in order to allow the global deep learning model to generalize in spite of interpolating spurious function descriptors. Empirically, we support our postulates with experiments involving overparameterized multilayer perceptrons and controlled noise in the training data. The main insights are that deep learning models are optimized for training data modularly, with different regions in the function space dedicated to fitting distinct kinds of sample information. Detrimental overfitting is largely prevented by the fact that different regions in the function space are used for prediction based on the similarity between new input data and that which has been optimized for.
@{284,
author = {Marthinus Theunissen, Marelie Davel, Etienne Barnard},
title = {Insights regarding overfitting on noise in deep learning},
abstract = {The understanding of generalization in machine learning is in a state of flux. This is partly due to the elatively recent revelation that deep learning models are able to completely memorize training data and still perform appropriately on out-of-sample data, thereby contradicting long-held intuitions about generalization. The phenomenon was brought to light and discussed in a seminal paper by Zhang et al. [24]. We expand upon this work by discussing local attributes of neural network training within the context of a relatively simple and generalizable framework. We describe how various types of noise can be compensated for within the proposed framework in order to allow the global deep learning model to generalize in spite of interpolating spurious function descriptors. Empirically, we support our postulates with experiments involving overparameterized multilayer perceptrons and controlled noise in the training data. The main insights are that deep learning models are optimized for training data modularly, with different regions in the function space dedicated to fitting distinct kinds of sample information. Detrimental overfitting is largely prevented by the fact that different regions in the function space are used for prediction based on the similarity between new input data and that which has been optimized for.},
year = {2019},
journal = {South African Forum for Artificial Intelligence Research (FAIR)},
pages = {49-63},
address = {Cape Town, South Africa},
}
Pretorius AP, Barnard E, Davel MH. ReLU and sigmoidal activation functions. In: South African Forum for Artificial Intelligence Research (FAIR). Cape Town, South Africa: CEUR Workshop Proceedings; 2019.
The generalization capabilities of deep neural networks are not well understood, and in particular, the influence of activation functions on generalization has received little theoretical attention. Phenomena such as vanishing gradients, node saturation and network sparsity have been identified as possible factors when comparing different activation functions [1]. We investigate these factors using fully connected feedforward networks on two standard benchmark problems, and find that the most salient differences between networks with sigmoidal and ReLU activations relate to the way that class-distinctive information is propagated through a network.
@{279,
author = {Arnold Pretorius, Etienne Barnard, Marelie Davel},
title = {ReLU and sigmoidal activation functions},
abstract = {The generalization capabilities of deep neural networks are not well understood, and in particular, the influence of activation functions on generalization has received little theoretical attention. Phenomena such as vanishing gradients, node saturation and network sparsity have been identified as possible factors when comparing different activation functions [1]. We investigate these factors using fully connected feedforward networks on two standard benchmark problems, and find that the most salient differences between networks with sigmoidal and ReLU activations relate to the way that class-distinctive information is propagated through a network.},
year = {2019},
journal = {South African Forum for Artificial Intelligence Research (FAIR)},
pages = {37-48},
month = {04/12-07/12},
publisher = {CEUR Workshop Proceedings},
address = {Cape Town, South Africa},
}
Van der Walt C, Barnard E. Variable Kernel Density Estimation in High-dimensional Feature Spaces. In: AAAI Conf. on Artificial Intelligence (AAAI-17). ; 2017.
Estimating the joint probability density function of a dataset is a central task in many machine learning applications. In this work we address the fundamental problem of kernel bandwidth estimation for variable kernel density estimation in high-dimensional feature spaces. We derive a variable kernel bandwidth estimator by minimizing the leave-one-out entropy objective function and show that this estimator is capable of performing estimation in high-dimensional feature spaces with great success. We compare the performance of this estimator to state-of-the art maximum likelihood estimators on a number of representative high-dimensional machine learning tasks and show that the newly introduced minimum leave-one-out entropy estimator performs optimally on a number of high-dimensional datasets considered.
@{275,
author = {Christiaan Van der Walt, Etienne Barnard},
title = {Variable Kernel Density Estimation in High-dimensional Feature Spaces},
abstract = {Estimating the joint probability density function of a dataset is a central task in many machine learning applications. In this work we address the fundamental problem of kernel bandwidth estimation for variable kernel density estimation in high-dimensional feature spaces. We derive a variable kernel bandwidth estimator by minimizing the leave-one-out entropy objective function and show that this estimator is capable of performing estimation in high-dimensional feature spaces with great success. We compare the performance of this estimator to state-of-the art maximum likelihood estimators on a number of representative high-dimensional machine learning tasks and show that the newly introduced minimum leave-one-out entropy estimator performs optimally on a number of high-dimensional datasets considered.},
year = {2017},
journal = {AAAI Conf. on Artificial Intelligence (AAAI-17)},
pages = {2674-2680},
month = {04/02-09/04},
}
Kleynhans N, Barnard E. Efficient data selection for ASR. Language Resources and Evaluation. 2015;49(2). doi:10.1007/s10579-014-9285-0.
Automatic speech recognition (ASR) technology has matured over the past few decades and has made significant impacts in a variety of fields, from assistive technologies to commercial products. However, ASR system development is a resource intensive activity and requires language resources in the form of text annotated audio recordings and pronunciation dictionaries. Unfortunately, many languages found in the developing world fall into the resource-scarce category and due to this resource scarcity the deployment of ASR systems in the developing world is severely inhibited. One approach to assist with resource-scarce ASR system development, is to select 'useful' training samples which could reduce the resources needed to collect new corpora. In this work, we propose a new data selection framework which can be used to design a speech recognition corpus. We show for limited data sets, independent of language and bandwidth, the most effective strategy for data selection is frequency-matched selection and that the widely-used maximum entropy methods generally produced the least promising results. In our model, the frequency-matched selection method corresponds to a logarithmic relationship between accuracy and corpus size; we also investigated other model relationships, and found that a hyperbolic relationship (as suggested from simple asymptotic arguments in learning theory) may lead to somewhat better performance under certain conditions.
@article{291,
author = {Neil Kleynhans, Etienne Barnard},
title = {Efficient data selection for ASR},
abstract = {Automatic speech recognition (ASR) technology has matured over the past few decades and has made significant impacts in a variety of fields, from assistive technologies to commercial products. However, ASR system development is a resource intensive activity and requires language resources in the form of text annotated audio recordings and pronunciation dictionaries. Unfortunately, many languages found in the developing world fall into the resource-scarce category and due to this resource scarcity the deployment of ASR systems in the developing world is severely inhibited. One approach to assist with resource-scarce ASR system development, is to select 'useful' training samples which could reduce the resources needed to collect new corpora. In this work, we propose a new data selection framework which can be used to design a speech recognition corpus. We show for limited data sets, independent of language and bandwidth, the most effective strategy for data selection is frequency-matched selection and that the widely-used maximum entropy methods generally produced the least promising results. In our model, the frequency-matched selection method corresponds to a logarithmic relationship between accuracy and corpus size; we also investigated other model relationships, and found that a hyperbolic relationship (as suggested from simple asymptotic arguments in learning theory) may lead to somewhat better performance under certain conditions.},
year = {2015},
journal = {Language Resources and Evaluation},
volume = {49},
pages = {327-353},
issue = {2},
publisher = {Springer Science+Business Media},
address = {Dordrecht},
doi = {10.1007/s10579-014-9285-0},
}
More Info
IBS SCHOLARSHIP STUDENT.
DEGREES LINKED TO THIS RESEARCH GROUP:
1) 2018-current PhD (Philosophy): 'Interfaces between Knowledge Representation and Reasoning and Political Philosophy: The Symbiotic Relationship Between Morality and Justice'.
TALKS:
1) 'Philosophy in/as Translation' (PSSA 2019);
2) 'Decolonizing Knowledge' (PPA 2019);
3) 'AI and the Social Good' (4th CAIR/UP Symposium 2019);
4) 'Decolonization and Alterity: Intersecting Theories and Praxis' (PPA 2018).
DEGREES LINKED TO THIS RESEARCH GROUP:
1) 2018-current PhD (Philosophy): 'Interfaces between Knowledge Representation and Reasoning and Political Philosophy: The Symbiotic Relationship Between Morality and Justice'.
TALKS:
1) 'Philosophy in/as Translation' (PSSA 2019);
2) 'Decolonizing Knowledge' (PPA 2019);
3) 'AI and the Social Good' (4th CAIR/UP Symposium 2019);
4) 'Decolonization and Alterity: Intersecting Theories and Praxis' (PPA 2018).
Latest Research Publications:
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Favour Borokini is a technology policy researcher with a law background from Nigeria. In her role as Data and Digital Rights Researcher with Pollicy, a Ugandan civic technology not-for-profit, she researches a number of knotty topics related to technology-facilitated violence against women and the general impact of subsisting and emerging technologies on social justice and equality.
Latest Research Publications:
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Latest Research Publications:
Britz K, Casini G, Meyer T, Moodley K, Sattler U, Varzinczak I. Principles of KLM-style Defeasible Description Logics. Transactions on Computational Logic. 2020;22 (1). doi:10.1145/3420258.
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.
@article{433,
author = {Katarina Britz, Giovanni Casini, Tommie Meyer, Kody Moodley, Uli Sattler, Ivan Varzinczak},
title = {Principles of KLM-style Defeasible Description Logics},
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.},
year = {2020},
journal = {Transactions on Computational Logic},
volume = {22 (1)},
pages = {1-46},
publisher = {ACM},
url = {https://dl-acm-org.ezproxy.uct.ac.za/doi/abs/10.1145/3420258},
doi = {10.1145/3420258},
}
Britz K, Varzinczak I. Preferential tableaux for contextual defeasible ALC. In: 28th International Conference on Automated Reasoning with Analytic Tableaux and Related Methods (TABLEAUX). Springer LNAI no. 11714; 2019. https://www.springer.com/gp/book/9783030290252.
In recent work, we addressed an important limitation in previous ex- tensions of description logics to represent defeasible knowledge, namely the re- striction in the semantics of defeasible concept inclusion to a single preference or- der on objects of the domain. Syntactically, this limitation translates to a context- agnostic notion of defeasible subsumption, which is quite restrictive when it comes to modelling different nuances of defeasibility. Our point of departure in our recent proposal allows for different orderings on the interpretation of roles. This yields a notion of contextual defeasible subsumption, where the context is informed by a role. In the present paper, we extend this work to also provide a proof-theoretic counterpart and associated results. We define a (naïve) tableau- based algorithm for checking preferential consistency of contextual defeasible knowledge bases, a central piece in the definition of other forms of contextual defeasible reasoning over ontologies, notably contextual rational closure.
@{247,
author = {Katarina Britz, Ivan Varzinczak},
title = {Preferential tableaux for contextual defeasible ALC},
abstract = {In recent work, we addressed an important limitation in previous ex- tensions of description logics to represent defeasible knowledge, namely the re- striction in the semantics of defeasible concept inclusion to a single preference or- der on objects of the domain. Syntactically, this limitation translates to a context- agnostic notion of defeasible subsumption, which is quite restrictive when it comes to modelling different nuances of defeasibility. Our point of departure in our recent proposal allows for different orderings on the interpretation of roles. This yields a notion of contextual defeasible subsumption, where the context is informed by a role. In the present paper, we extend this work to also provide a proof-theoretic counterpart and associated results. We define a (naïve) tableau- based algorithm for checking preferential consistency of contextual defeasible knowledge bases, a central piece in the definition of other forms of contextual defeasible reasoning over ontologies, notably contextual rational closure.},
year = {2019},
journal = {28th International Conference on Automated Reasoning with Analytic Tableaux and Related Methods (TABLEAUX)},
pages = {39-57},
month = {03/09-05/09},
publisher = {Springer LNAI no. 11714},
isbn = {ISBN 978-3-030-29026-9},
url = {https://www.springer.com/gp/book/9783030290252},
}
Britz K, Varzinczak I. Contextual rational closure for defeasible ALC. Annals of Mathematics and Artificial Intelligence. 2019;87(1-2). doi:10.1007/s10472-019-09658-2.
Description logics have been extended in a number of ways to support defeasible reason- ing in the KLM tradition. Such features include preferential or rational defeasible concept inclusion, and defeasible roles in complex concept descriptions. Semantically, defeasible subsumption is obtained by means of a preference order on objects, while defeasible roles are obtained by adding a preference order to role interpretations. In this paper, we address an important limitation in defeasible extensions of description logics, namely the restriction in the semantics of defeasible concept inclusion to a single preference order on objects. We do this by inducing a modular preference order on objects from each modular preference order on roles, and using these to relativise defeasible subsumption. This yields a notion of contextualised rational defeasible subsumption, with contexts described by roles. We also provide a semantic construction for rational closure and a method for its computation, and present a correspondence result between the two.
@article{246,
author = {Katarina Britz, Ivan Varzinczak},
title = {Contextual rational closure for defeasible ALC},
abstract = {Description logics have been extended in a number of ways to support defeasible reason- ing in the KLM tradition. Such features include preferential or rational defeasible concept inclusion, and defeasible roles in complex concept descriptions. Semantically, defeasible subsumption is obtained by means of a preference order on objects, while defeasible roles are obtained by adding a preference order to role interpretations. In this paper, we address an important limitation in defeasible extensions of description logics, namely the restriction in the semantics of defeasible concept inclusion to a single preference order on objects. We do this by inducing a modular preference order on objects from each modular preference order on roles, and using these to relativise defeasible subsumption. This yields a notion of contextualised rational defeasible subsumption, with contexts described by roles. We also provide a semantic construction for rational closure and a method for its computation, and present a correspondence result between the two.},
year = {2019},
journal = {Annals of Mathematics and Artificial Intelligence},
volume = {87},
pages = {83-108},
issue = {1-2},
isbn = {ISSN: 1012-2443},
url = {https://link.springer.com/article/10.1007/s10472-019-09658-2},
doi = {10.1007/s10472-019-09658-2},
}
Britz K, Casini G, Meyer T, Varzinczak I. A KLM Perspective on Defeasible Reasoning for Description Logics. In: Description Logic, Theory Combination, and All That. Switzerland: Springer; 2019. doi:https://doi.org/10.1007/978-3-030-22102-7 _ 7.
In this paper we present an approach to defeasible reasoning for the description logic ALC. The results discussed here are based on work done by Kraus, Lehmann and Magidor (KLM) on defeasible conditionals in the propositional case. We consider versions of a preferential semantics for two forms of defeasible subsumption, and link these semantic constructions formally to KLM-style syntactic properties via representation results. In addition to showing that the semantics is appropriate, these results pave the way for more effective decision procedures for defeasible reasoning in description logics. With the semantics of the defeasible version of ALC in place, we turn to the investigation of an appropriate form of defeasible entailment for this enriched version of ALC. This investigation includes an algorithm for the computation of a form of defeasible entailment known as rational closure in the propositional case. Importantly, the algorithm relies completely on classical entailment checks and shows that the computational complexity of reasoning over defeasible ontologies is no worse than that of the underlying classical ALC. Before concluding, we take a brief tour of some existing work on defeasible extensions of ALC that go beyond defeasible subsumption.
@inbook{240,
author = {Katarina Britz, Giovanni Casini, Tommie Meyer, Ivan Varzinczak},
title = {A KLM Perspective on Defeasible Reasoning for Description Logics},
abstract = {In this paper we present an approach to defeasible reasoning for the description logic ALC. The results discussed here are based on work done by Kraus, Lehmann and Magidor (KLM) on defeasible conditionals in the propositional case. We consider versions of a preferential semantics for two forms of defeasible subsumption, and link these semantic constructions formally to KLM-style syntactic properties via representation results. In addition to showing that the semantics is appropriate, these results pave the way for more effective decision procedures for defeasible reasoning in description logics. With the semantics of the defeasible version of ALC in place, we turn to the investigation of an appropriate form of defeasible entailment for this enriched version of ALC. This investigation includes an algorithm for the computation of a form of defeasible entailment known as rational closure in the propositional case. Importantly, the algorithm relies completely on classical entailment checks and shows that the computational complexity of reasoning over defeasible ontologies is no worse than that of the underlying classical ALC. Before concluding, we take a brief tour of some existing work on defeasible extensions of ALC that go beyond defeasible subsumption.},
year = {2019},
journal = {Description Logic, Theory Combination, and All That},
pages = {147–173},
publisher = {Springer},
address = {Switzerland},
isbn = {978-3-030-22101-0},
url = {https://link.springer.com/book/10.1007%2F978-3-030-22102-7},
doi = {https://doi.org/10.1007/978-3-030-22102-7 _ 7},
}
Du Toit T, Berndt J, Britz K, Fischer B. ConceptCloud 2.0: Visualisation and exploration of geolocation-rich semi-structured data sets. ICFCA 2019 Conference and Workshops. 2019. http://ceur-ws.org/Vol-2378/.
ConceptCloud is a flexible interactive tool for exploring, vi- sualising, and analysing semi-structured data sets. It uses a combination of an intuitive tag cloud visualisation with an underlying concept lattice to provide a formal structure for navigation through a data set. Con- ceptCloud 2.0 extends the tool with an integrated map view to exploit the geolocation aspect of data. The tool’s implementation of exploratory search does not require prior knowledge of the structure of the data or compromise on scalability, and provides seamless navigation through the tag cloud and the map viewer.
@misc{227,
author = {Tiaan Du Toit, Joshua Berndt, Katarina Britz, Bernd Fischer},
title = {ConceptCloud 2.0: Visualisation and exploration of geolocation-rich semi-structured data sets},
abstract = {ConceptCloud is a flexible interactive tool for exploring, vi- sualising, and analysing semi-structured data sets. It uses a combination of an intuitive tag cloud visualisation with an underlying concept lattice to provide a formal structure for navigation through a data set. Con- ceptCloud 2.0 extends the tool with an integrated map view to exploit the geolocation aspect of data. The tool’s implementation of exploratory search does not require prior knowledge of the structure of the data or compromise on scalability, and provides seamless navigation through the tag cloud and the map viewer.},
year = {2019},
journal = {ICFCA 2019 Conference and Workshops},
month = {06/2019},
publisher = {CEUR-WS},
isbn = {1613-0073},
url = {http://ceur-ws.org/Vol-2378/},
}
Research Groups:
More Info
Latest Research Publications:
Casini G, Meyer T, Varzinczak I. Contextual Conditional Reasoning. In: 35th AAAI Conference on Artificial Intelligence. Online: AAAI Press; 2021.
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.
@{430,
author = {Giovanni Casini, Tommie Meyer, Ivan Varzinczak},
title = {Contextual Conditional Reasoning},
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.},
year = {2021},
journal = {35th AAAI Conference on Artificial Intelligence},
pages = {6254-6261},
month = {02/02/2021-09/02/2021},
publisher = {AAAI Press},
address = {Online},
}
Casini G, Meyer T, Paterson-Jones G. KLM-Style Defeasibility for Restricted First-Order Logic. In: 19th International Workshop on Non-Monotonic Reasoning. Online; 2021. https://drive.google.com/open?id=1WSIl3TOrXBhaWhckWN4NLXoD9AVFKp5R.
We extend the KLM approach to defeasible reasoning to be applicable to a restricted version of first-order logic. We describe defeasibility for this logic using a set of rationality postulates, provide an appropriate semantics for it, and present a representation result that characterises the semantic description of defeasibility in terms of the rationality postulates. Based on this theoretical core, we then propose a version of defeasible entailment that is inspired by 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 our rationality postulates. The work in this paper is the first step towards our ultimate goal of introducing KLM-style defeasible reasoning into the family of Datalog+/- ontology languages.
@{429,
author = {Giovanni Casini, Tommie Meyer, Guy Paterson-Jones},
title = {KLM-Style Defeasibility for Restricted First-Order Logic},
abstract = {We extend the KLM approach to defeasible reasoning to be applicable to a restricted version of first-order logic. We describe defeasibility for this logic using a set of rationality postulates, provide an appropriate semantics for it, and present a representation result that characterises the semantic description of defeasibility in terms of the rationality postulates. Based on this theoretical core, we then propose a version of defeasible entailment that is inspired by 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 our rationality postulates. The work in this paper is the first step towards our ultimate goal of introducing KLM-style defeasible reasoning into the family of Datalog+/- ontology languages.},
year = {2021},
journal = {19th International Workshop on Non-Monotonic Reasoning},
pages = {184-193},
month = {03/11/2021-05/11/2021},
address = {Online},
url = {https://drive.google.com/open?id=1WSIl3TOrXBhaWhckWN4NLXoD9AVFKp5R},
}
Britz K, Casini G, Meyer T, Moodley K, Sattler U, Varzinczak I. Principles of KLM-style Defeasible Description Logics. Transactions on Computational Logic. 2020;22 (1). doi:10.1145/3420258.
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.
@article{433,
author = {Katarina Britz, Giovanni Casini, Tommie Meyer, Kody Moodley, Uli Sattler, Ivan Varzinczak},
title = {Principles of KLM-style Defeasible Description Logics},
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.},
year = {2020},
journal = {Transactions on Computational Logic},
volume = {22 (1)},
pages = {1-46},
publisher = {ACM},
url = {https://dl-acm-org.ezproxy.uct.ac.za/doi/abs/10.1145/3420258},
doi = {10.1145/3420258},
}
Paterson-Jones G, Casini G, Meyer T. BKLM - An expressive logic for defeasible reasoning. 18th International Workshop on Non-Monotonic Reasoning. 2020.
Propositional KLM-style defeasible reasoning involves a core propositional logic capable of expressing defeasible (or conditional) implications. The semantics for this logic is based on Kripke-like structures known as ranked interpretations. KLM-style defeasible entailment is referred to as rational whenever the defeasible entailment relation under consideration generates a set of defeasible implications all satisfying a set of rationality postulates known as the KLM postulates. In a recent paper Booth et al. proposed PTL, a logic that is more expressive than the core KLM logic. They proved an impossibility result, showing that defeasible entailment for PTL fails to satisfy a set of rationality postulates similar in spirit to the KLM postulates. Their interpretation of the impossibility result is that defeasible entailment for PTL need not be unique.
In this paper we continue the line of research in which the expressivity of the core KLM logic is extended. We present the logic Boolean KLM (BKLM) in which we allow for disjunctions, conjunctions, and negations, but not nesting, of defeasible implications. Our contribution is twofold. Firstly, we show (perhaps surprisingly) that BKLM is more expressive than PTL. Our proof is based on the fact that BKLM can characterise all single ranked interpretations, whereas PTL cannot. Secondly, given that the PTL impossibility result also applies to BKLM, we adapt the different forms of PTL entailment proposed by Booth et al. to apply to BKLM.
@misc{383,
author = {Guy Paterson-Jones, Giovanni Casini, Tommie Meyer},
title = {BKLM - An expressive logic for defeasible reasoning},
abstract = {Propositional KLM-style defeasible reasoning involves a core propositional logic capable of expressing defeasible (or conditional) implications. The semantics for this logic is based on Kripke-like structures known as ranked interpretations. KLM-style defeasible entailment is referred to as rational whenever the defeasible entailment relation under consideration generates a set of defeasible implications all satisfying a set of rationality postulates known as the KLM postulates. In a recent paper Booth et al. proposed PTL, a logic that is more expressive than the core KLM logic. They proved an impossibility result, showing that defeasible entailment for PTL fails to satisfy a set of rationality postulates similar in spirit to the KLM postulates. Their interpretation of the impossibility result is that defeasible entailment for PTL need not be unique.
In this paper we continue the line of research in which the expressivity of the core KLM logic is extended. We present the logic Boolean KLM (BKLM) in which we allow for disjunctions, conjunctions, and negations, but not nesting, of defeasible implications. Our contribution is twofold. Firstly, we show (perhaps surprisingly) that BKLM is more expressive than PTL. Our proof is based on the fact that BKLM can characterise all single ranked interpretations, whereas PTL cannot. Secondly, given that the PTL impossibility result also applies to BKLM, we adapt the different forms of PTL entailment proposed by Booth et al. to apply to BKLM.},
year = {2020},
journal = {18th International Workshop on Non-Monotonic Reasoning},
month = {12/09/2020-24/09/2020},
}
Casini G, Meyer T, Varzinczak I. Rational Defeasible Belief Change. In: 17th International Conference on Principles of Knowledge Representation and Reasoning (KR 2020). Virtual: IJCAI; 2020. doi:10.24963/kr.2020/22.
We present a formal framework for modelling belief change within a non-monotonic reasoning system. Belief change and non-monotonic reasoning are two areas that are formally closely related, with recent attention being paid towards the analysis of belief change within a non-monotonic environment. In this paper we consider the classical AGM belief change operators, contraction and revision, applied to a defeasible setting in the style of Kraus, Lehmann, and Magidor. The investigation leads us to the formal characterisation of a number of classes of defeasible belief change operators. For the most interesting classes we need to consider the problem of iterated belief change, generalising the classical work of Darwiche and Pearl in the process. Our work involves belief change operators aimed at ensuring logical consistency, as well as the characterisation of analogous operators aimed at obtaining coherence—an important notion within the field of logic-based ontologies
@{382,
author = {Giovanni Casini, Tommie Meyer, Ivan Varzinczak},
title = {Rational Defeasible Belief Change},
abstract = {We present a formal framework for modelling belief change within a non-monotonic reasoning system. Belief change and non-monotonic reasoning are two areas that are formally closely related, with recent attention being paid towards the analysis of belief change within a non-monotonic environment. In this paper we consider the classical AGM belief change operators, contraction and revision, applied to a defeasible setting in the style of Kraus, Lehmann, and Magidor. The investigation leads us to the formal characterisation of a number of classes of defeasible belief change operators. For the most interesting classes we need to consider the problem of iterated belief change, generalising the classical work of Darwiche and Pearl in the process. Our work involves belief change operators aimed at ensuring logical consistency, as well as the characterisation of analogous operators aimed at obtaining coherence—an important notion within the field of logic-based ontologies},
year = {2020},
journal = {17th International Conference on Principles of Knowledge Representation and Reasoning (KR 2020)},
pages = {213-222},
month = {12/09/2020},
publisher = {IJCAI},
address = {Virtual},
url = {https://library.confdna.com/kr/2020/},
doi = {10.24963/kr.2020/22},
}
Research Groups:
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Research Groups:
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Latest Research Publications:
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