Tommie Meyer

2017

Rens, G. ., Meyer, T. ., & Moodley, D. . (2017). A Stochastic Belief Management Architecture for Agent Control. Retrieved from http://pubs.cs.uct.ac.za/archive/00001201/01/AGA_2017_Rens_et_al.pdf

Abstract BibTex Entry BibTeX Download PDF

AGA_2017_Rens_et_al.pdf

We propose an architecture for agent control, where the agent stores its beliefs and environment models as logical sentences. Given successive observations, the agent’s current state (of beliefs) is maintained by a combination of probability, POMDP and belief change theory. Two existing logics are employed for knowledge representation and reasoning: the stochastic decision logic of Rens et al. (2015) and p-logic of Zhuanget al. (2017) (a restricted version of a logic designedby Fagin et al. (1990)). The proposed architecture assumes two streams of observations: active, which correspond to agent intentions and passive, which is received without the agent’s direct involvement. Stochastic uncertainty, and ignorance due to lack of information are both dealt with in the architecture. Planning, and learning of environment models are assumed present but are not covered in this proposal.

@misc{155,
  author = {Gavin Rens and Tommie Meyer and Deshen Moodley},
  title = {A Stochastic Belief Management Architecture for Agent Control},
  abstract = {We propose an architecture for agent control, where the agent stores its beliefs and environment models as logical sentences. Given successive observations, the agent’s current state (of beliefs) is maintained by a combination of probability, POMDP and belief change theory. Two existing logics are employed for knowledge representation and reasoning: the stochastic decision logic of Rens et al. (2015) and p-logic of Zhuanget al. (2017) (a restricted version of a logic designedby Fagin et al. (1990)). The proposed architecture assumes two streams of observations: active, which correspond to agent intentions and passive, which is received without the agent’s direct involvement. Stochastic uncertainty, and ignorance due to lack of information are both dealt with in the architecture. Planning, and learning of environment models are assumed present but are not covered in this proposal.},
  year = {2017},
  url = {http://pubs.cs.uct.ac.za/archive/00001201/01/AGA_2017_Rens_et_al.pdf},
}

2016

Ojeme, B. ., Mbogho, A. ., & Meyer, T. . (2016). Probabilistic Expert Systems for Reasoning in Clinical Depressive Disorders. In 15th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE. http://doi.org/10.1109/ICMLA.2016.0105

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Like other real-world problems, reasoning in clinical depression presents cognitive challenges for clinicians. This is due to the presence of co-occuring diseases, incomplete data, uncertain knowledge, and the vast amount of data to be analysed. Current approaches rely heavily on the experience, knowledge, and subjective opinions of clinicians, creating scalability issues. Automating this process requires a good knowledge representation technique to capture the knowledge of the domain experts, and multidimensional inferential reasoning approaches that can utilise a few bits and pieces of information for efficient reasoning. This study presents knowledge-based system with variants of Bayesian network models for efficient inferential reasoning, translating from available fragmented depression data to the desired information in a visually interpretable and transparent manner. Mutual information, a Conditional independence test-based method was used to learn the classifiers.

@{361,
  author = {Blessing Ojeme and Audrey Mbogho and Tommie Meyer},
  title = {Probabilistic Expert Systems for Reasoning in Clinical Depressive Disorders},
  abstract = {Like other real-world problems, reasoning in clinical depression presents cognitive challenges for clinicians. This is due to the presence of co-occuring diseases, incomplete data, uncertain knowledge, and the vast amount of data to be analysed. Current approaches rely heavily on the experience, knowledge, and subjective opinions of clinicians, creating scalability issues. Automating this process requires a good knowledge representation technique to capture the knowledge of the domain experts, and multidimensional inferential reasoning approaches that can utilise a few bits and pieces of information for efficient reasoning. This study presents knowledge-based system with variants of Bayesian network models for efficient inferential reasoning, translating from available fragmented depression data to the desired information in a visually interpretable and transparent manner. Mutual information, a Conditional independence test-based method was used to learn the classifiers.},
  year = {2016},
  journal = {15th IEEE International Conference on Machine Learning and Applications (ICMLA)},
  month = {18/12 - 20/12},
  publisher = {IEEE},
  doi = {10.1109/ICMLA.2016.0105},
}

Casini, G. ., & Meyer, T. . (2016). Using Defeasible Information to Obtain Coherence. In Fifteenth International Conference on Principles of Knowledge Representation and Reasoning (KR). AAAI Press. http://doi.org/https://dl.acm.org/doi/10.5555/3032027.3032097

Abstract BibTex Entry BibTeX Download PDF

12852-57584-1-PB.pdf

We consider the problem of obtaining coherence in a propositional knowledge base using techniques from Belief Change. Our motivation comes from the ﬁeld of formal ontologies where coherence is interpreted to mean that a concept name has to be satisﬁable. In the propositional case we consider here, this translates to a propositional formula being satisﬁable. We deﬁne belief change operators in a framework of nonmonotonic preferential reasoning. We show how the introduction of defeasible information using contraction operators can be an effective means for obtaining coherence.

@{360,
  author = {Giovanni Casini and Tommie Meyer},
  title = {Using Defeasible Information to Obtain Coherence},
  abstract = {We consider the problem of obtaining coherence in a propositional knowledge base using techniques from Belief Change. Our motivation comes from the ﬁeld of formal ontologies where coherence is interpreted to mean that a concept name has to be satisﬁable. In the propositional case we consider here, this translates to a propositional formula being satisﬁable. We deﬁne belief change operators in a framework of nonmonotonic preferential reasoning. We show how the introduction of defeasible information using contraction operators can be an effective means for obtaining coherence.},
  year = {2016},
  journal = {Fifteenth International Conference on  Principles of Knowledge Representation and Reasoning (KR)},
  pages = {537-540},
  month = {25/04 - 29/04},
  publisher = {AAAI Press},
  doi = {https://dl.acm.org/doi/10.5555/3032027.3032097},
}

Rens, G. ., Casini, G. ., & Meyer, T. . (2016). On Revision of Partially Specified Convex Probabilistic Belief Bases. In European Conference on Artificial Intelligence (ECAI). IO Press. Retrieved from https://www.researchgate.net/publication/307577667_On_Revision_of_Partially_Specified_Convex_Probabilistic_Belief_Bases

Abstract BibTex Entry BibTeX Download PDF

kaminka107.pdf

We propose a method for an agent to revise its incomplete probabilistic beliefs when a new piece of propositional information is observed. In this work, an agent’s beliefs are represented by a set of probabilistic formulae – a belief base. The method involves determining a representative set of ‘boundary’ probability distributions consistent with the current belief base, revising each of these probability distributions and then translating the revised information into a new belief base. We use a version of Lewis Imaging as the revision operation. The correctness of the approach is proved. An analysis of the approach is done against six rationality postulates. The expressivity of the belief bases under consideration are rather restricted, but has some applications. We also discuss methods of belief base revision employing the notion of optimum entropy, and point out some of the beneﬁts and difﬁculties in those methods. Both the boundary distribution method and the optimum entropy methods are reasonable, yet yield different results.

@{359,
  author = {Gavin Rens and Giovanni Casini and Tommie Meyer},
  title = {On Revision of Partially Specified Convex Probabilistic Belief Bases},
  abstract = {We propose a method for an agent to revise its incomplete probabilistic beliefs when a new piece of propositional information is observed. In this work, an agent’s beliefs are represented by a set of probabilistic formulae – a belief base. The method involves determining a representative set of ‘boundary’ probability distributions consistent with the current belief base, revising each of these probability distributions and then translating the revised information into a new belief base. We use a version of Lewis Imaging as the revision operation. The correctness of the approach is proved. An analysis of the approach is done against six rationality postulates. The expressivity of the belief bases under consideration are rather restricted, but has some applications. We also discuss methods of belief base revision employing the notion of optimum entropy, and point out some of the beneﬁts and difﬁculties in those methods. Both the boundary distribution method and the optimum entropy methods are reasonable, yet yield different results.},
  year = {2016},
  journal = {European Conference on Artificial Intelligence (ECAI)},
  pages = {921-929},
  month = {29/08 - 2/09},
  publisher = {IO Press},
  url = {https://www.researchgate.net/publication/307577667_On_Revision_of_Partially_Specified_Convex_Probabilistic_Belief_Bases},
}

Leenen, L. ., & Meyer, T. . (2016). Semantic Technologies and Big Data: Analytics for Cyber Defence. International Journal of Cyber Warfare and Terrorism, 6(3).

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viewcomplimentarytitle.pdf

The Governments, military forces and other organisations responsible for cybersecurity deal with vast amounts of data that has to be understood in order to lead to intelligent decision making. Due to the vast amounts of information pertinent to cybersecurity, automation is required for processing and decision making, specifically to present advance warning of possible threats. The ability to detect patterns in vast data sets, and being able to understanding the significance of detected patterns are essential in the cyber defence domain. Big data technologies supported by semantic technologies can improve cybersecurity, and thus cyber defence by providing support for the processing and understanding of the huge amounts of information in the cyber environment. The term big data analytics refers to advanced analytic techniques such as machine learning, predictive analysis, and other intelligent processing techniques applied to large data sets that contain different data types. The purpose is to detect patterns, correlations, trends and other useful information. Semantic technologies is a knowledge representation paradigm where the meaning of data is encoded separately from the data itself. The use of semantic technologies such as logic-based systems to support decision making is becoming increasingly popular. However, most automated systems are currently based on syntactic rules. These rules are generally not sophisticated enough to deal with the complexity of decisions required to be made. The incorporation of semantic information allows for increased understanding and sophistication in cyber defence systems. This paper argues that both big data analytics and semantic technologies are necessary to provide counter measures against cyber threats. An overview of the use of semantic technologies and big data technologies in cyber defence is provided, and important areas for future research in the combined domains are discussed.

@article{229,
author = {Louise Leenen and Tommie Meyer},
title = {Semantic Technologies and Big Data: Analytics for Cyber Defence},
abstract = {The Governments, military forces and other organisations responsible for cybersecurity deal with
vast amounts of data that has to be understood in order to lead to intelligent decision making. Due
to the vast amounts of information pertinent to cybersecurity, automation is required for processing
and decision making, specifically to present advance warning of possible threats. The ability to detect
patterns in vast data sets, and being able to understanding the significance of detected patterns are
essential in the cyber defence domain. Big data technologies supported by semantic technologies
can improve cybersecurity, and thus cyber defence by providing support for the processing and
understanding of the huge amounts of information in the cyber environment. The term big data
analytics refers to advanced analytic techniques such as machine learning, predictive analysis, and
other intelligent processing techniques applied to large data sets that contain different data types. The
purpose is to detect patterns, correlations, trends and other useful information. Semantic technologies
is a knowledge representation paradigm where the meaning of data is encoded separately from the
data itself. The use of semantic technologies such as logic-based systems to support decision making
is becoming increasingly popular. However, most automated systems are currently based on syntactic
rules. These rules are generally not sophisticated enough to deal with the complexity of decisions
required to be made. The incorporation of semantic information allows for increased understanding and
sophistication in cyber defence systems. This paper argues that both big data analytics and semantic
technologies are necessary to provide counter measures against cyber threats. An overview of the
use of semantic technologies and big data technologies in cyber defence is provided, and important
areas for future research in the combined domains are discussed.},
year = {2016},
journal = {International Journal of Cyber Warfare and Terrorism},
volume = {6},
issue = {3},
}

Rens, G. ., Meyer, T. ., & Casini, G. . (2016). On Revision of Partially Specified Convex Probabilistic Belief Bases. In European Conference on Artificial Intelligence (ECAI).

Abstract BibTex Entry BibTeX Download PDF

rens-meyer-casini-2016.pdf

We propose a method for an agent to revise its incomplete probabilistic beliefs when a new piece of propositional information is observed. In this work, an agent’s beliefs are represented by a set of probabilistic formulae – a belief base. The method involves determining a representative set of ‘boundary’ probability distributions consistent with the current belief base, revising each of these probability distributions and then translating the revised information into a new belief base. We use a version of Lewis Imaging as the revision operation. The correctness of the approach is proved. An analysis of the approach is done against six rationality postulates. The expressivity of the belief bases under consideration are rather restricted, but has some applications. We also discuss methods of belief base revision employing the notion of optimum entropy, and point out some of the benefits and difficulties in those methods. Both the boundary distribution method and the optimum entropy method are reasonable, yet yield different results.

@{144,
  author = {Gavin Rens and Tommie Meyer and Giovanni Casini},
  title = {On Revision of Partially Specified Convex Probabilistic Belief Bases},
  abstract = {We propose a method for an agent to revise its incomplete probabilistic beliefs when a new piece of propositional information is observed. In this work, an agent’s beliefs are represented by a set of probabilistic formulae – a belief base. The method involves determining a representative set of ‘boundary’ probability distributions consistent with the current belief base, revising each of these probability distributions and then translating the revised information into a new belief base. We use a version of Lewis Imaging as the revision operation. The correctness of the approach is proved. An analysis of the approach is done against six rationality postulates. The expressivity of the belief bases under consideration are rather restricted, but has some applications. We also discuss methods of belief base revision employing the notion of optimum entropy, and point out some of the benefits and difficulties in those methods. Both the boundary distribution method and the optimum entropy method are reasonable, yet yield different results.},
  year = {2016},
  journal = {European Conference on Artificial Intelligence (ECAI)},
  pages = {921-929},
  month = {31/08-02/09},
}

Rens, G. ., Meyer, T. ., & Casini, G. . (2016). Revising Incompletely Specified Convex Probabilistic Belief Bases.

Abstract BibTex Entry BibTeX Download PDF

NMR-Paper-11-Rens-etal.pdf

We propose a method for an agent to revise its incomplete probabilistic beliefs when a new piece of propositional information is observed. In this work, an agent’s beliefs are represented by a set of probabilistic formulae – a belief base. The method involves determining a representative set of ‘boundary’ probability distributions consistent with the current belief base, revising each of these probability distributions and then translating the revised information into a new belief base. We use a version of Lewis Imaging as the revision operation. The correctness of the approach is proved. The expressivity of the belief bases under consideration are rather restricted, but has some applications. We also discuss methods of belief base revision employing the notion of optimum entropy, and point out some of the benefits and difficulties in those methods. Both the boundary distribution method and the optimum entropy method are reasonable, yet yield different results.

@misc{131,
  author = {Gavin Rens and Tommie Meyer and Giovanni Casini},
  title = {Revising Incompletely Specified Convex Probabilistic Belief Bases},
  abstract = {We propose a method for an agent to revise its incomplete probabilistic beliefs when a new piece of propositional information is observed. In this work, an agent’s beliefs are represented by a set of probabilistic formulae – a belief base. The method involves determining a representative set of ‘boundary’ probability distributions consistent with the current belief base, revising each of these probability distributions and then translating the revised information into a new belief base. We use a version of Lewis Imaging as the revision operation. The correctness of the approach is proved. The expressivity of the belief bases under consideration are rather restricted, but has some applications. We also discuss methods of belief base revision employing the notion of optimum entropy, and point out some of the benefits and difficulties in those methods. Both the boundary distribution method and the optimum entropy method are reasonable, yet yield different results.},
  year = {2016},
  isbn = {ISSN 0933-6192},
}

2015

Booth, R. ., Casini, G. ., Meyer, T. ., & Varzinczak, I. . (2015). On the Entailment Problem for a Logic of Typicality. In Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI). AAAI Press. Retrieved from https://www.semanticscholar.org/paper/On-the-Entailment-Problem-for-a-Logic-of-Typicality-Booth-Casini/47bb88f18d591df1355c7ad53593a1f1b46522f0

Abstract BibTex Entry BibTeX Download PDF

88f18d591df1355c7ad53593a1f1b46522f0.pdf

Propositional Typicality Logic (PTL) is a recently proposed logic, obtained by enriching classical propositional logic with a typicality operator. In spite of the non-monotonic features introduced by the semantics adopted for the typicality operator, the obvious Tarskian deﬁnition of entailment for PTL remains monotonic and is therefore not appropriate. We investigate different (semantic) versions of entailment for PTL, based on the notion of Rational Closure as deﬁned by Lehmann and Magidor for KLM-style conditionals, and constructed using minimality. Our ﬁrst important result is an impossibility theorem showing that a set of proposed postulates that at ﬁrst all seem appropriate for a notion of entailment with regard to typicality cannot be satisﬁed simultaneously. Closer inspection reveals that this result is best interpreted as an argument for advocating the development of more than one type of PTL entailment. In the spirit of this interpretation, we deﬁne two primary forms of entailment for PTL and discuss their advantages and disadvantages

@{363,
  author = {Richard Booth and Giovanni Casini and Tommie Meyer and Ivan Varzinczak},
  title = {On the Entailment Problem for a Logic of Typicality},
  abstract = {Propositional Typicality Logic (PTL) is a recently proposed logic, obtained by enriching classical propositional logic with a typicality operator. In spite of the non-monotonic features introduced by the semantics adopted for the typicality operator, the obvious Tarskian deﬁnition of entailment for PTL remains monotonic and is therefore not appropriate. We investigate different (semantic) versions of entailment for PTL, based on the notion of Rational Closure as deﬁned by Lehmann and Magidor for KLM-style conditionals, and constructed using minimality. Our ﬁrst important result is an impossibility theorem showing that a set of proposed postulates that at ﬁrst all seem appropriate for a notion of entailment with regard to typicality cannot be satisﬁed simultaneously. Closer inspection reveals that this result is best interpreted as an argument for advocating the development of more than one type of PTL entailment. In the spirit of this interpretation, we deﬁne two primary forms of entailment for PTL and discuss their advantages and disadvantages},
  year = {2015},
  journal = {Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI)},
  pages = {2805-2811},
  month = {25/07 - 31/07},
  publisher = {AAAI Press},
  url = {https://www.semanticscholar.org/paper/On-the-Entailment-Problem-for-a-Logic-of-Typicality-Booth-Casini/47bb88f18d591df1355c7ad53593a1f1b46522f0},
}

Rens, G. ., & Meyer, T. . (2015). A New Approach to Probabilistic Belief Change. In International Florida AI Research Society Conference (FLAIRS). AAAI Press. Retrieved from https://www.researchgate.net/publication/277004602_A_New_Approach_to_Probabilistic_Belief_Change

Abstract BibTex Entry BibTeX Download PDF

ProbBelChangeFLAIRS-CR4.pdf

One way for an agent to deal with uncertainty about its beliefs is to maintain a probability distribution over the worlds it believes are possible. A belief change operation may recommend some previously believed worlds to become impossible and some previously disbelieved worlds to become possible. This work investigates how to redistribute probabilities due to worlds being added to and removed from an agent’s belief-state. Two related approaches are proposed and analyzed.

@{362,
  author = {Gavin Rens and Tommie Meyer},
  title = {A New Approach to Probabilistic Belief Change},
  abstract = {One way for an agent to deal with uncertainty about its beliefs is to maintain a probability distribution over the worlds it believes are possible. A belief change operation may recommend some previously believed worlds to become impossible and some previously disbelieved worlds to become possible. This work investigates how to redistribute probabilities due to worlds being added to and removed from an agent’s belief-state. Two related approaches are proposed and analyzed.},
  year = {2015},
  journal = {International Florida AI Research Society Conference (FLAIRS)},
  pages = {582-587},
  month = {18/05 - 20/05},
  publisher = {AAAI Press},
  url = {https://www.researchgate.net/publication/277004602_A_New_Approach_to_Probabilistic_Belief_Change},
}

Britz, K. ., Casini, G. ., Meyer, T. ., Moodley, K. ., Sattler, U. ., & Varzinczak, I. . (2015). Rational Defeasible Reasoning for Expressive Description Logics.

Abstract BibTex Entry BibTeX Download PDF

TR-DefeasibleSubsumption.pdf

In this paper, we enrich description logics (DLs) with non-monotonic reasoning features in a number of ways. We start by investigating a notion of defeasible conditional in the spirit of KLM-style defeasible consequence. In particular, we consider a natural and intuitive semantics for defeasible subsumption in terms of DL interpretations enriched with a preference relation. We propose and investigate syntactic properties (à la Gentzen) for both preferential and rational conditionals and prove representation results for the description logic ALC. This representation result paves the way for more effective decision procedures for defeasible reasoning in DLs. We then move to non-monotonicity in DLs at the level of entailment. We investigate versions of entailment in the context of both preferential and rational subsumption, relate them to preferential and rational closure, and show that computing them can be reduced to classical ALC entailment. This provides further evidence that our semantic constructions are appropriate in a non-monotonic DL setting. One of the barriers to evaluating performance scalability of rational closure is the abscence of naturally occurring DL-based ontologies with defeasible features. We overcome this barrier by devising an approach to introduce defeasible subsumption into classical real world ontologies. This culminates in a set of semi-natural defeasible ontologies that is used, together with a purely artificial set, to test our rational closure algorithms. We found that performance is scalable on the whole with no major bottlenecks.

@misc{130,
author = {Katarina Britz and Giovanni Casini and Tommie Meyer and Kody Moodley and U. Sattler and Ivan Varzinczak},
title = {Rational Defeasible Reasoning for Expressive Description Logics},
abstract = {In this paper, we enrich description logics (DLs) with non-monotonic reasoning features in a number of ways. We start by investigating a notion of defeasible conditional in the spirit of KLM-style defeasible consequence. In particular, we consider a natural and intuitive semantics for defeasible subsumption in terms of DL interpretations enriched with a preference relation. We propose and investigate syntactic properties (à la Gentzen) for both preferential and rational conditionals and prove representation results for the description logic ALC. This representation result paves the way for more effective decision procedures for defeasible reasoning in DLs. We then move to non-monotonicity in DLs at the level of entailment. We investigate versions of entailment in the context of both preferential and rational subsumption, relate them to preferential and rational closure, and show that computing them can be reduced to classical ALC entailment. This provides further evidence that our semantic constructions are appropriate in a non-monotonic DL setting. One of the barriers to evaluating performance scalability of rational closure is the abscence of naturally occurring DL-based ontologies with defeasible features. We overcome this barrier by devising an approach to introduce defeasible subsumption into classical real world ontologies. This culminates in a set of semi-natural defeasible ontologies that is used, together with a purely artificial set, to test our rational closure algorithms. We found that performance is scalable on the whole with no major bottlenecks.},
year = {2015},
}

Casini, G. ., Straccia, U. ., & Meyer, T. . (2015). A Polynomial Time Subsumption Algorithm for EL⊥ under Rational Closure.

Abstract BibTex Entry BibTeX Download PDF

Rational Closure EL.pdf

No Abstract

@misc{117,
  author = {Giovanni Casini and Umberto Straccia and Tommie Meyer},
  title = {A Polynomial Time Subsumption Algorithm for EL⊥ under Rational Closure},
  abstract = {No Abstract},
  year = {2015},
}