Research Publications

2017

Fischer B, Esterhuizen M, Greene GJ. Visualizing and Exploring Software Version Control Repositories using Interactive Tag Clouds over Formal Concept Lattices. Elsevier. 2017;87(2017). https://www.sciencedirect.com/science/article/pii/S0950584916304050?via%3Dihub.

Context: version control repositories contain a wealth of implicit information that can be used to answer many questions about a project’s development process. However, this information is not directly accessible in the repositories and must be extracted and visualized. Objective: the main objective of this work is to develop a flexible and generic interactive visualization engine called ConceptCloud that supports exploratory search in version control repositories. Method: ConceptCloud is a flexible, interactive browser for SVN and Git repositories. Its main novelty is the combination of an intuitive tag cloud visualization with an underlying concept lattice that provides a formal structure for navigation. ConceptCloud supports concurrent navigation in multiple linked but individually customizable tag clouds, which allows for multi-faceted repository browsing, and scriptable construction of unique visualizations. Results: we describe the mathematical foundations and implementation of our approach and use ConceptCloud to quickly gain insight into the team structure and development process of three projects. We perform a user study to determine the usability of ConceptCloud. We show that untrained participants are able to answer historical questions about a software project better using ConceptCloud than using a linear list of commits. Conclusion: ConceptCloud can be used to answer many difficult questions such as “What has happened in this project while I was away?” and “Which developers collaborate?”. Tag clouds generated from our approach provide a visualization in which version control data can be aggregated and explored interactively.

@article{174,
  author = {Bernd Fischer and M. Esterhuizen and G.J. Greene},
  title = {Visualizing and Exploring Software Version Control Repositories using Interactive Tag Clouds over Formal Concept Lattices},
  abstract = {Context: version control repositories contain a wealth of implicit information that can be used to answer many questions about a project’s development process. However, this information is not directly accessible in the repositories and must be extracted and visualized.
Objective: the main objective of this work is to develop a flexible and generic interactive visualization engine called ConceptCloud that supports exploratory search in version control repositories.
Method: ConceptCloud is a flexible, interactive browser for SVN and Git repositories. Its main novelty is the combination of an intuitive tag cloud visualization with an underlying concept lattice that provides a formal structure for navigation. ConceptCloud supports concurrent navigation in multiple linked but individually customizable tag clouds, which allows for multi-faceted repository browsing, and scriptable construction of unique visualizations.
Results: we describe the mathematical foundations and implementation of our approach and use ConceptCloud to quickly gain insight into the team structure and development process of three projects. We perform a user study to determine the usability of ConceptCloud. We show that untrained participants are able to answer historical questions about a software project better using ConceptCloud than using a linear list of commits.
Conclusion: ConceptCloud can be used to answer many difficult questions such as “What has happened in this project while I was away?” and “Which developers collaborate?”. Tag clouds generated from our approach provide a visualization in which version control data can be aggregated and explored interactively.},
  year = {2017},
  journal = {Elsevier},
  volume = {87},
  pages = {223-241},
  issue = {2017},
  url = {https://www.sciencedirect.com/science/article/pii/S0950584916304050?via%3Dihub},
}
Fischer B, Dunaiski M, Greene GJ. Exploratory Search of Academic Publication and Citation Data using Interactive Tag Cloud Visualizations. Scientometrics (Springer). 2017;110(3). https://link.springer.com/article/10.1007%2Fs11192-016-2236-3.

Acquiring an overview of an unfamiliar discipline and exploring relevant papers and journals is often a laborious task for researchers. In this paper we show how exploratory search can be supported on a large collection of academic papers to allow users to answer complex scientometric questions which traditional retrieval approaches do not support optimally. We use our ConceptCloud browser, which makes use of a combination of concept lattices and tag clouds, to visually present academic publication data (specifically, the ACM Digital Library) in a browsable format that facilitates exploratory search. We augment this dataset with semantic categories, obtained through automatic keyphrase extraction from papers’ titles and abstracts, in order to provide the user with uniform keyphrases of the underlying data collection. We use the citations and references of papers to provide additional mechanisms for exploring relevant research by presenting aggregated reference and citation data not only for a single paper but also across topics, authors and journals, which is novel in our approach. We conduct a user study to evaluate our approach in which we asked 34 participants, from different academic backgrounds with varying degrees of research experience, to answer a variety of scientometric questions using our ConceptCloud browser. Participants were able to answer complex scientometric questions using our ConceptCloud browser with a mean correctness of 73%, with the user’s prior research experience having no statistically significant effect on the results.

@article{173,
  author = {Bernd Fischer and M. Dunaiski and G.J. Greene},
  title = {Exploratory Search of Academic Publication and Citation Data using Interactive Tag Cloud Visualizations},
  abstract = {Acquiring an overview of an unfamiliar discipline and exploring relevant papers and journals is often a laborious task for researchers. In this paper we show how exploratory search can be supported on a large collection of academic papers to allow users to answer complex scientometric questions which traditional retrieval approaches do not support optimally. We use our ConceptCloud browser, which makes use of a combination of concept lattices and tag clouds, to visually present academic publication data (specifically, the ACM Digital Library) in a browsable format that facilitates exploratory search. We augment this dataset with semantic categories, obtained through automatic keyphrase extraction from papers’ titles and abstracts, in order to provide the user with uniform keyphrases of the underlying data collection. We use the citations and references of papers to provide additional mechanisms for exploring relevant research by presenting aggregated reference and citation data not only for a single paper but also across topics, authors and journals, which is novel in our approach. We conduct a user study to evaluate our approach in which we asked 34 participants, from different academic backgrounds with varying degrees of research experience, to answer a variety of scientometric questions using our ConceptCloud browser. Participants were able to answer complex scientometric questions using our ConceptCloud browser with a mean correctness of 73%, with the user’s prior research experience having no statistically significant effect on the results.},
  year = {2017},
  journal = {Scientometrics (Springer)},
  volume = {110},
  pages = {1539-1571},
  issue = {3},
  address = {Netherlands},
  isbn = {0138-9130},
  url = {https://link.springer.com/article/10.1007%2Fs11192-016-2236-3},
}
Britz K, Varzinczak I. Context-based defeasible subsumption for dSROIQ. In: 13th International Symposium on Commonsense Reasoning. ; 2017.

The description logic dSROIQ is a decidable extension of SROIQ that supports defeasible reasoning in the KLM tradition. It features a parameterised preference order on binary relations in a domain of interpretation, which allows for the use of defeasible roles in complex concepts, as well as in defeasible concept and role subsumption, and in defeasible role assertions. In this paper, we address an important limitation both in dSROIQ and in other defeasible extensions of description logics, namely the restriction in the semantics of defeasible concept subsumption to a single preference order on objects. We do this by inducing preference orders on objects from preference orders on roles, and use these to relativise defeasible subsumption. This yields a notion of contextualised defeasible subsumption, with contexts described by roles.

@{169,
  author = {Katarina Britz and Ivan Varzinczak},
  title = {Context-based defeasible subsumption for dSROIQ},
  abstract = {The description logic dSROIQ is a decidable extension of SROIQ that supports defeasible reasoning in the KLM tradition. It features a parameterised preference order on binary relations in a domain of interpretation, which allows for the use of defeasible roles in complex concepts, as well as in defeasible concept and role subsumption, and in defeasible role assertions. In this paper, we address an important limitation both in dSROIQ and in other defeasible extensions of description logics, namely the restriction in the semantics of defeasible concept subsumption to a single preference order on objects. We do this by inducing preference orders on objects from preference orders on roles, and use these to relativise defeasible subsumption. This yields a notion of contextualised defeasible subsumption, with contexts described by roles.},
  year = {2017},
  journal = {13th International Symposium on Commonsense Reasoning},
  month = {06/11-08/11},
}
Britz K, Varzinczak I. Towards defeasible SROIQ. 2017. http://ceur-ws.org/Vol-1879/.

We present a decidable extension of the Description Logic SROIQ that supports defeasible reasoning in the KLM tradition, and extends it through the introduction of defeasible roles. The semantics of the resulting DL dSROIQ extends the classical semantics with a parameterised preference order on binary relations in a domain of interpretation. This allows for the use of defeasible roles in complex concepts, as well as in defeasible concept and role subsumption, and in defeasible role assertions. Reasoning over dSROIQ ontologies is made possible by a translation of entailment to concept satisfiability relative to an RBox only. A tableau algorithm then decides on consistency of dSROIQ-concepts in the preferential semantics.

@misc{168,
  author = {Katarina Britz and Ivan Varzinczak},
  title = {Towards defeasible SROIQ},
  abstract = {We present a decidable extension of the Description Logic SROIQ that supports defeasible reasoning in the KLM tradition, and extends it through the introduction of defeasible roles. The semantics of the resulting DL dSROIQ extends the classical semantics with a parameterised preference order on binary relations in a domain of interpretation. This allows for the use of defeasible roles in complex concepts, as well as in defeasible concept and role subsumption, and in defeasible role assertions.  Reasoning over dSROIQ ontologies is made possible by a translation of entailment to concept satisfiability relative to an RBox only. A tableau algorithm then decides on consistency of dSROIQ-concepts in the preferential semantics.},
  year = {2017},
  isbn = {ISSN 1613-0073},
  url = {http://ceur-ws.org/Vol-1879/},
}
Casini G, Meyer T. Belief Change in a Preferential Non-Monotonic Framework. In: International Joint Conference on Artificial Intelligence (IJCAI-17). ; 2017.

Belief change and non-monotonic reasoning are usually viewed as two sides of the same coin, with results showing that one can formally be defined in terms of the other. In this paper we show that we can also integrate the two formalisms by studying belief change within a (preferential) non-monotonic framework. This integration relies heavily on the identification of the monotonic core of a non-monotonic framework. We consider belief change operators in a non-monotonic propositional setting with a view towards preserving consistency. These results can also be applied to the preservation of coherence—an important notion within the field of logic-based ontologies. We show that the standard AGM approach to belief change can be adapted to a preferential non-monotonic framework, with the definition of expansion, contraction, and revision operators, and corresponding representation results. Surprisingly, preferential AGM belief change, as defined here, can be obtained in terms of classical AGM belief change.

@{167,
  author = {Giovanni Casini and Thomas Meyer},
  title = {Belief Change in a Preferential Non-Monotonic Framework},
  abstract = {Belief change and non-monotonic reasoning are usually viewed as two sides of the same coin, with results showing that one can formally be defined in terms of the other. In this paper we show that we can also integrate the two formalisms by studying belief change within a (preferential) non-monotonic framework. This integration relies heavily on the identification of the monotonic core of a non-monotonic framework. We consider belief change operators in a non-monotonic propositional setting with a view towards preserving consistency. These results can also be applied to the preservation of coherence—an important notion within the field of logic-based ontologies. We show that the standard AGM approach to belief change can be adapted to a preferential non-monotonic framework, with the definition of expansion, contraction, and revision operators, and corresponding representation results. Surprisingly, preferential AGM belief change, as defined here, can be obtained in terms of classical AGM belief change.},
  year = {2017},
  journal = {International Joint Conference on Artificial Intelligence (IJCAI-17)},
  pages = {929-935},
  month = {19/08-25/08},
  isbn = {978-0-9992411-0-3},
}
Mouton F, Teixeira M, Meyer T. Benchmarking a Mobile Implementation of the Social Engineering Prevention Training Tool. In: Information Security for South Africa (ISSA). ; 2017.

As the nature of information stored digitally becomes more important and confidential, the security of the systems put in place to protect this information needs to be increased. The human element, however, remains a vulnerability of the system and it is this vulnerability that social engineers attempt to exploit. The Social Engineering Attack Detection Model version 2 (SEADMv2) has been proposed to help people identify malicious social engineering attacks. Prior to this study, the SEADMv2 had not been implemented as a user friendly application or tested with real subjects. This paper describes how the SEADMv2 was implemented as an Android application. This Android application was tested on 20 subjects, to determine whether it reduces the probability of a subject falling victim to a social engineering attack or not. The results indicated that the Android implementation of the SEADMv2 significantly reducedthe number of subjects that fell victim to social engineering attacks. The Android application also significantly reduced the number of subjects that fell victim to malicious social engineering attacks, bidirectional communication social engineering attacks and indirect communication social engineering attacks. The Android application did not have a statistically significant effect on harmless scenarios and unidirectional communication social engineering attacks.

@{166,
  author = {F. Mouton and M. Teixeira and Thomas Meyer},
  title = {Benchmarking a Mobile Implementation of the Social Engineering Prevention Training Tool},
  abstract = {As the nature of information stored digitally becomes more important and confidential, the security of the systems put in place to protect this information needs to be increased. The human element, however, remains a vulnerability of the system and it is this vulnerability that social engineers attempt to exploit. The Social Engineering Attack Detection Model version 2 (SEADMv2) has been proposed to help people identify malicious social engineering attacks. Prior to this study, the SEADMv2 had not been implemented as a user friendly application or tested with real subjects. This paper describes how the SEADMv2 was implemented as an Android application. This Android application was tested on 20 subjects, to determine whether it reduces the probability of a subject falling victim to a social engineering attack or not. The results indicated that the Android implementation of the SEADMv2 significantly reducedthe number of subjects that fell victim to social engineering attacks. The Android application also significantly reduced the number of subjects that fell victim to malicious social engineering attacks, bidirectional communication social engineering attacks and indirect communication social engineering attacks. The Android application did not have a statistically significant effect on harmless scenarios and unidirectional communication social engineering attacks.},
  year = {2017},
  journal = {Information Security for South Africa (ISSA)},
  pages = {106-116},
  month = {16/08-17/08},
  isbn = {978-1-5386-0545-5},
}
Booth R, Casini G, Meyer T, Varzinczak I. Extending Typicality for Description Logics. 2017. http://orbilu.uni.lu/bitstream/10993/32165/1/TforDL-Technical_report.pdf.

Recent extensions of description logics for dealing with different forms of non-monotonic reasoning don’t take us beyond the case of defeasible subsumption. In this paper we enrich the DL EL⊥ with a (constrained version of) a typicality operator •, the intuition of which is to capture the most typical members of a class, providing us with the DL EL•⊥. We argue that EL•⊥ is the smallest step one can take to increase the expressivity beyond the case of defeasible subsumption for DLs, while still retaining all the rationality properties an appropriate notion of defeasible subsumption is required to satisfy, and investigate what an appropriate notion of non-monotonic entailment for EL• ⊥ should look like.

@misc{165,
  author = {Richard Booth and Giovanni Casini and Thomas Meyer and Ivan Varzinczak},
  title = {Extending Typicality for Description Logics},
  abstract = {Recent extensions of description logics for dealing with different forms of non-monotonic reasoning don’t take us beyond the case of defeasible subsumption. In this paper we enrich the DL EL⊥ with a (constrained version of) a typicality operator •, the intuition of which is to capture the most typical members of a class, providing us with the DL EL•⊥. We argue that EL•⊥ is the smallest step one can take to increase the expressivity beyond the case of defeasible subsumption for DLs, while still retaining all the rationality properties an appropriate notion of defeasible subsumption is required to satisfy, and investigate what an appropriate notion of non-monotonic entailment for EL• ⊥ should look like.},
  year = {2017},
  url = {http://orbilu.uni.lu/bitstream/10993/32165/1/TforDL-Technical_report.pdf},
}
Rens G, Meyer T. Imagining Probabilistic Belief Change as Imaging. 2017. https://arxiv.org/pdf/1705.01172.pdf.

Imaging is a form of probabilistic belief change which could be employed for both revision and update. In this paper, we propose a new framework for probabilistic belief change based on imaging, called Expected Distance Imaging (EDI). EDI is sufficiently general to define Bayesian conditioning and other forms of imaging previously defined in the literature. We argue that, and investigate how, EDI can be used for both revision and update. EDI’s definition depends crucially on a weight function whose properties are studied and whose effect on belief change operations is analysed. Finally, four EDI instantiations are proposed, two for revision and two for update, and probabilistic rationality postulates are suggested for their analysis.

@misc{164,
  author = {Gavin Rens and Thomas Meyer},
  title = {Imagining Probabilistic Belief Change as Imaging},
  abstract = {Imaging is a form of probabilistic belief change which could be employed for both revision and update. In this paper, we propose a new framework for probabilistic belief change based on imaging, called Expected Distance Imaging (EDI). EDI is sufficiently general to define Bayesian conditioning and other forms of imaging previously defined in the literature. We argue that, and investigate how, EDI can be used for both revision and update. EDI’s definition depends crucially on a weight function whose properties are studied and whose effect on belief change operations is analysed. Finally, four EDI instantiations are proposed, two for revision and two for update, and probabilistic rationality postulates are suggested for their analysis.},
  year = {2017},
  url = {https://arxiv.org/pdf/1705.01172.pdf},
}
Gerber A, Morar N, Meyer T, Eardley C. Ontology-based support for taxonomic functions. Ecological Informatics. 2017;41. https://ac.els-cdn.com/S1574954116301959/1-s2.0-S1574954116301959-main.pdf?_tid=487687ca-01b3-11e8-89aa-00000aacb35e&acdnat=1516873196_6a2c94e428089403763ccec46613cf0f.

This paper reports on an investigation into the use of ontology technologies to support taxonomic functions. Support for taxonomy is imperative given several recent discussions and publications that voiced concern over the taxonomic impediment within the broader context of the life sciences. Taxonomy is defined as the scientific classification, description and grouping of biological organisms into hierarchies based on sets of shared characteristics, and documenting the principles that enforce such classification. Under taxonomic functions we identified two broad categories: the classification functions concerned with identification and naming of organisms, and secondly classification functions concerned with categorization and revision (i.e. grouping and describing, or revisiting existing groups and descriptions). Ontology technologies within the broad field of artificial intelligence include computational ontologies that are knowledge representation mechanisms using standardized representations that are based on description logics (DLs). This logic base of computational ontologies provides for the computerized capturing and manipulation of knowledge. Furthermore, the set-theoretical basis of computational ontologies ensures particular suitability towards classification, which is considered as a core function of systematics or taxonomy. Using the specific case of Afrotropical bees, this experimental research study represents the taxonomic knowledge base as an ontology, explore the use of available reasoning algorithms to draw the necessary inferences that support taxonomic functions (identification and revision) over the ontology and implement a Web-based application (the WOC). The contributions include the ontology, a reusable and standardized computable knowledge base of the taxonomy of Afrotropical bees, as well as the WOC and the evaluation thereof by experts.

@article{163,
  author = {Aurona Gerber and Nishal Morar and Thomas Meyer and C. Eardley},
  title = {Ontology-based support for taxonomic functions},
  abstract = {This paper reports on an investigation into the use of ontology technologies to support taxonomic functions. Support for taxonomy is imperative given several recent discussions and publications that voiced concern over the taxonomic impediment within the broader context of the life sciences. Taxonomy is defined as the scientific classification, description and grouping of biological organisms into hierarchies based on sets of shared characteristics, and documenting the principles that enforce such classification. Under taxonomic functions we identified two broad categories: the classification functions concerned with identification and naming of organisms, and secondly classification functions concerned with categorization and revision (i.e. grouping and describing, or revisiting existing groups and descriptions).
Ontology technologies within the broad field of artificial intelligence include computational ontologies that are knowledge representation mechanisms using standardized representations that are based on description logics (DLs). This logic base of computational ontologies provides for the computerized capturing and manipulation of knowledge. Furthermore, the set-theoretical basis of computational ontologies ensures particular suitability towards classification, which is considered as a core function of systematics or taxonomy.
Using the specific case of Afrotropical bees, this experimental research study represents the taxonomic knowledge base as an ontology, explore the use of available reasoning algorithms to draw the necessary inferences that support taxonomic functions (identification and revision) over the ontology and implement a Web-based application (the WOC). The contributions include the ontology, a reusable and standardized computable knowledge base of the taxonomy of Afrotropical bees, as well as the WOC and the evaluation thereof by experts.},
  year = {2017},
  journal = {Ecological Informatics},
  volume = {41},
  pages = {11-23},
  publisher = {Elsevier},
  isbn = {1574-9541},
  url = {https://ac.els-cdn.com/S1574954116301959/1-s2.0-S1574954116301959-main.pdf?_tid=487687ca-01b3-11e8-89aa-00000aacb35e&acdnat=1516873196_6a2c94e428089403763ccec46613cf0f},
}
Seebregts C, Pillay A, Crichton R, Singh S, Moodley D. 14 Enterprise Architectures for Digital Health. Global Health Informatics: Principles of eHealth and mHealth to Improve Quality of Care. 2017. https://books.google.co.za/books?id=8p-rDgAAQBAJ&pg=PA173&lpg=PA173&dq=14+Enterprise+Architectures+for+Digital+Health&source=bl&ots=i6SQzaXiPp&sig=zDLJ6lIqt3Xox3Lt5LNCuMkUoJ4&hl=en&sa=X&ved=0ahUKEwivtK6jxPDYAhVkL8AKHXbNDY0Q6AEINDAB#v=onepage&q=14%20Enterp.

• Several different paradigms and standards exist for creating digital health architectures that are mostly complementary, but sometimes contradictory. • The potential benefits of using EA approaches and tools are that they help to ensure the appropriate use of standards for interoperability and data storage and exchange, and encourage the creation of reusable software components and metadata.

@article{162,
  author = {Chris Seebregts and Anban Pillay and Ryan Crichton and S. Singh and Deshen Moodley},
  title = {14 Enterprise Architectures for Digital Health},
  abstract = {• Several different paradigms and standards exist for creating digital health architectures that 
are mostly complementary, but sometimes contradictory.
• The potential benefits of using EA 
approaches and tools are that they help to ensure the appropriate use of standards for 
interoperability and data storage and exchange, and encourage the creation of reusable 
software components and metadata.},
  year = {2017},
  journal = {Global Health Informatics: Principles of eHealth and mHealth to Improve Quality of Care},
  pages = {173-182},
  publisher = {MIT Press},
  isbn = {978-0262533201},
  url = {https://books.google.co.za/books?id=8p-rDgAAQBAJ&pg=PA173&lpg=PA173&dq=14+Enterprise+Architectures+for+Digital+Health&source=bl&ots=i6SQzaXiPp&sig=zDLJ6lIqt3Xox3Lt5LNCuMkUoJ4&hl=en&sa=X&ved=0ahUKEwivtK6jxPDYAhVkL8AKHXbNDY0Q6AEINDAB#v=onepage&q=14%20Enterp},
}
Adeleke JA, Moodley D, Rens G, Adewumi AO. Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control. Sensors. 2017;17(4). http://pubs.cs.uct.ac.za/archive/00001219/01/sensors-17-00807.pdf.

Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM2.5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM2.5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web.

@article{160,
  author = {Jude Adeleke and Deshen Moodley and Gavin Rens and A.O. Adewumi},
  title = {Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control},
  abstract = {Proactive monitoring and control of our natural and built environments is important in various application scenarios. Semantic Sensor Web technologies have been well researched and used for environmental monitoring applications to expose sensor data for analysis in order to provide responsive actions in situations of interest. While these applications provide quick response to situations, to minimize their unwanted effects, research efforts are still necessary to provide techniques that can anticipate the future to support proactive control, such that unwanted situations can be averted altogether. This study integrates a statistical machine learning based predictive model in a Semantic Sensor Web using stream reasoning. The approach is evaluated in an indoor air quality monitoring case study. A sliding window approach that employs the Multilayer Perceptron model to predict short term PM2.5 pollution situations is integrated into the proactive monitoring and control framework. Results show that the proposed approach can effectively predict short term PM2.5 pollution situations: precision of up to 0.86 and sensitivity of up to 0.85 is achieved over half hour prediction horizons, making it possible for the system to warn occupants or even to autonomously avert the predicted pollution situations within the context of Semantic Sensor Web.},
  year = {2017},
  journal = {Sensors},
  volume = {17},
  pages = {1-23},
  issue = {4},
  publisher = {MDPI},
  isbn = {1424-8220},
  url = {http://pubs.cs.uct.ac.za/archive/00001219/01/sensors-17-00807.pdf},
}
Rens G, Meyer T, Moodley D. A Stochastic Belief Management Architecture for Agent Control. 2017. http://pubs.cs.uct.ac.za/archive/00001201/01/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 Thomas 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},
}
Coetzer W, Moodley D. A knowledge-based system for generating interaction networks from ecological data. Data & Knowledge Engineering. 2017;112. doi:http://dx.doi.org/10.1016/j.datak.2017.09.005.

Semantic heterogeneity hampers efforts to find, integrate, analyse and interpret ecological data. An application case-study is described, in which the objective was to automate the integration and interpretation of heterogeneous, flower-visiting ecological data. A prototype knowledgebased system is described and evaluated. The system's semantic architecture uses a combination of ontologies and a Bayesian network to represent and reason with qualitative, uncertain ecological data and knowledge. This allows the high-level context and causal knowledge of behavioural interactions between individual plants and insects, and consequent ecological interactions between plant and insect populations, to be discovered. The system automatically assembles ecological interactions into a semantically consistent interaction network (a new design of a useful, traditional domain model). We discuss the contribution of probabilistic reasoning to knowledge discovery, the limitations of knowledge discovery in the application case-study, the impact of the work and the potential to apply the system design to the study of ecological interaction networks in general.

@article{154,
  author = {Willem Coetzer and Deshen Moodley},
  title = {A knowledge-based system for generating interaction networks from ecological data},
  abstract = {Semantic heterogeneity hampers efforts to find, integrate, analyse and interpret ecological data. An application case-study is described, in which the objective was to automate the integration and interpretation of heterogeneous, flower-visiting ecological data. A prototype knowledgebased system is described and evaluated. The system's semantic architecture uses a combination of ontologies and a Bayesian network to represent and reason with qualitative, uncertain ecological data and knowledge. This allows the high-level context and causal knowledge of behavioural interactions between individual plants and insects, and consequent ecological interactions between plant and insect populations, to be discovered. The system automatically assembles ecological interactions into a semantically consistent interaction network (a new design of a useful, traditional domain model). We discuss the contribution of probabilistic reasoning to knowledge discovery, the limitations of knowledge discovery in the application case-study, the impact of the work and the potential to apply the system design to the study of ecological interaction networks in general.},
  year = {2017},
  journal = {Data & Knowledge Engineering},
  volume = {112},
  pages = {55-78},
  publisher = {Elsevier},
  isbn = {0169-023X},
  url = {http://pubs.cs.uct.ac.za/archive/00001220/01/coetzer-et-al-DKE-2017.pdf},
  doi = {http://dx.doi.org/10.1016/j.datak.2017.09.005},
}
Kroon S, Heavens A, Fantaye Y, et al. No evidence for extensions to the standard cosmological model. Physical Review Letters. 2017;119(2017). https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.119.101301.

No Abstract

@article{152,
  author = {Steve Kroon and A. Heavens and Y. Fantaye and E. Sellentin and H. Eggers and Z. Hosenie and A. Mootoovaloo},
  title = {No evidence for extensions to the standard cosmological model},
  abstract = {No Abstract},
  year = {2017},
  journal = {Physical Review Letters},
  volume = {119},
  pages = {101301-101305},
  issue = {2017},
  publisher = {American Physical Society},
  url = {https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.119.101301},
}
Kroon S, Yoon M, Bekker J. New reinforcement learning algorithm for robot soccer. Orion. 2017;33(1). http://orion.journals.ac.za/pub/article/view/542.

Reinforcement Learning (RL) is a powerful technique to develop intelligent agents in the field of Artificial Intelligence (AI). This paper proposes a new RL algorithm called the Temporal-Difference value iteration algorithm with state-value functions and presents applications of this algorithm to the decision-making problems challenged in the RoboCup Small Size League (SSL) domain. Six scenarios were defined to develop shooting skills for an SSL soccer robot in various situations using the proposed algorithm. Furthermore, an Artificial Neural Network (ANN) model, namely Multi-Layer Perceptron (MLP) was used as a function approximator in each application. The experimental results showed that the proposed RL algorithm had effectively trained the RL agent to acquire good shooting skills. The RL agent showed good performance under specified experimental conditions.

@article{151,
  author = {Steve Kroon and M. Yoon and J. Bekker},
  title = {New reinforcement learning algorithm for robot soccer},
  abstract = {Reinforcement Learning (RL) is a powerful technique to develop intelligent agents in the field of Artificial Intelligence (AI). This paper proposes a new RL algorithm called the Temporal-Difference value iteration algorithm with state-value functions and presents applications of this algorithm to the decision-making problems challenged in the RoboCup Small Size League (SSL) domain. Six scenarios were defined to develop shooting skills for an SSL soccer robot in various situations using the proposed algorithm. Furthermore, an Artificial Neural Network (ANN) model, namely Multi-Layer Perceptron (MLP) was used as a function approximator in each application. The experimental results showed that the proposed RL algorithm had effectively trained the RL agent to acquire good shooting skills. The RL agent showed good performance under specified experimental conditions.},
  year = {2017},
  journal = {Orion},
  volume = {33},
  pages = {1-20},
  issue = {1},
  publisher = {Operations Research Society of South Africa (ORSSA)},
  address = {South Africa},
  isbn = {2224-0004 (online)},
  url = {http://orion.journals.ac.za/pub/article/view/542},
}
Rens G, Moodley D. A hybrid POMDP-BDI agent architecture with online stochastic planning and plan caching. Cognitive Systems Research. 2017;43. doi:http://dx.doi.org/10.1016/j.cogsys.2016.12.002.

This article presents an agent architecture for controlling an autonomous agent in stochastic, noisy environments. The architecture combines the partially observable Markov decision process (POMDP) model with the belief-desire-intention (BDI) framework. The Hybrid POMDP-BDI agent architecture takes the best features from the two approaches, that is, the online generation of reward-maximizing courses of action from POMDP theory, and sophisticated multiple goal management from BDI theory. We introduce the advances made since the introduction of the basic architecture, including (i) the ability to pursue and manage multiple goals simultaneously and (ii) a plan library for storing pre-written plans and for storing recently generated plans for future reuse. A version of the architecture is implemented and is evaluated in a simulated environment. The results of the experiments show that the improved hybrid architecture outperforms the standard POMDP architecture and the previous basic hybrid architecture for both processing speed and effectiveness of the agent in reaching its goals.

@article{147,
  author = {Gavin Rens and Deshen Moodley},
  title = {A hybrid POMDP-BDI agent architecture with online stochastic planning and plan caching},
  abstract = {This article presents an agent architecture for controlling an autonomous agent in stochastic, noisy environments. The architecture combines the partially observable Markov decision process (POMDP) model with the belief-desire-intention (BDI) framework. The Hybrid POMDP-BDI agent architecture takes the best features from the two approaches, that is, the online generation of reward-maximizing courses of action from POMDP theory, and sophisticated multiple goal management from BDI theory. We introduce the advances made since the introduction of the basic architecture, including (i) the ability to pursue and manage multiple goals simultaneously and (ii) a plan library for storing pre-written plans and for storing recently generated plans for future reuse. A version of the architecture is implemented and is evaluated in a simulated environment. The results of the experiments show that the improved hybrid architecture outperforms the standard POMDP architecture and the previous basic hybrid architecture for both processing speed and effectiveness of the agent in reaching its goals.},
  year = {2017},
  journal = {Cognitive Systems Research},
  volume = {43},
  pages = {1-20},
  publisher = {Elsevier B.V.},
  isbn = {1389-0417},
  doi = {http://dx.doi.org/10.1016/j.cogsys.2016.12.002},
}

2016

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

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 Thomas 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},
}
  • CSIR
  • DSI
  • Covid-19