CAIR@UKZN Research Publications

The 3D computer gaming industry is constantly exploring new avenues for creating immersive and engaging environments. One avenue being explored is autonomous control of the behaviour of non-player characters (NPC). This paper reviews and compares existing artificial intelligence (AI) techniques for controlling the behaviour of non-human characters in Multiplayer Online Battle Arena (MOBA) game environments. Two techniques, the fuzzy state machine (FuSM) and the emotional behaviour tree (EBT), were reviewed and compared. In addition, an alternate and simple mechanism to incorporate emotion in a behaviour tree is proposed and tested. Initial tests of the mechanism show that it is a viable and promising mechanism for effectively tracking the emotional state of an NPC and for incorporating emotion in NPC decision making.

@{157,
  author = {Michael Waltham and Deshen Moodley},
  title = {An Analysis of Artificial Intelligence Techniques in Multiplayer Online Battle Arena Game Environments},
  abstract = {The 3D computer gaming industry is constantly exploring new avenues for creating immersive and engaging environments. One avenue being explored is autonomous control of the behaviour of non-player characters (NPC). This paper reviews and compares existing artificial intelligence (AI) techniques for controlling the behaviour of non-human characters in Multiplayer Online Battle Arena (MOBA) game environments. Two techniques, the fuzzy state machine (FuSM) and the emotional behaviour tree (EBT), were reviewed and compared. In addition, an alternate and simple mechanism to incorporate emotion in a behaviour tree is proposed and tested. Initial tests of the mechanism show that it is a viable and promising mechanism for effectively tracking the emotional state of an NPC and for incorporating emotion in NPC decision making.},
  year = {2016},
  journal = {Annual Conference of the South African Institute of Computer Scientists and Information Technologists (SAICSIT 2016)},
  pages = {45},
  month = {26/09-28/09},
  publisher = {ACM},
  address = {Johannesburg},
  isbn = {978-1-4503-4805-8},
  doi = {http://dx.doi.org/10.1145/2987491.2987513},
}

Extensive research has been done using machine learning techniques for hand gesture recognition (HGR) using camera-based devices; such as the Leap Motion Controller (LMC). However, limited research has investigated machine learning techniques for HGR in virtual reality applications (VR). This paper reports on the design, implementation, and evaluation of a static HGR system for VR applications using the LMC. The gesture recognition system incorporated a lightweight feature vector of five normalized tip-to-palm distances and a k-nearest neighbour (kNN) classifier. The system was evaluated in terms of response time, accuracy and usability using a case-study VR stellar data visualization application created in the Unreal Engine 4. An average gesture classification time of 0.057ms with an accuracy of 82.5% was achieved on four distinct gestures, which is comparable with previous results from Sign Language recognition systems. This shows the potential of HGR machine learning techniques applied to VR, which were previously applied to non-VR scenarios such as Sign Language recognition.

@{156,
  author = {A. Clark and Deshen Moodley},
  title = {A System for a Hand Gesture-Manipulated Virtual Reality Environment},
  abstract = {Extensive research has been done using machine learning techniques for hand gesture recognition (HGR) using camera-based devices; such as the Leap Motion Controller (LMC). However, limited research has investigated machine learning techniques for HGR in virtual reality applications (VR). This paper reports on the design, implementation, and evaluation of a static HGR system for VR applications using the LMC. The gesture recognition system incorporated a lightweight feature vector of five normalized tip-to-palm distances and a k-nearest neighbour (kNN) classifier. The system was evaluated in terms of response time, accuracy and usability using a case-study VR stellar data visualization application created in the Unreal Engine 4. An average gesture classification time of 0.057ms with an accuracy of 82.5% was achieved on four distinct gestures, which is comparable with previous results from Sign Language recognition systems. This shows the potential of HGR machine learning techniques applied to VR, which were previously applied to non-VR scenarios such as Sign Language recognition.},
  year = {2016},
  journal = {Annual Conference of the South African Institute of Computer Scientists and Information Technologists (SAICSIT 2016)},
  pages = {10},
  month = {26/09-28/09},
  publisher = {ACM},
  address = {Johannesburg},
  isbn = {978-1-4503-4805-8},
  doi = {http://dx.doi.org/10.1145/2987491.2987511},
}

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},
}

A framework for an agent to change its probabilistic beliefs after a stream of noisy observations is received is proposed. Observations which are no longer relevant, become default assumptions until overridden by newer, more prevalent observations. A distinction is made between background and foreground beliefs. Agent actions and environment events are distinguishable and form part of the agent model. It is left up to the agent designer to provide an environment model; a submodel of the agent model. An example of an environment model is provided in the paper, and an example scenario is based on it. Given the particular form of the agent model, several ‘patterns of cognition’ can be identified. An argument is made for four particular patterns.

@misc{140,
  author = {Gavin Rens},
  title = {A Stochastic Belief Change Framework with an Observation Stream and Defaults as Expired Observations},
  abstract = {A framework for an agent to change its probabilistic beliefs after a stream of noisy observations is received is proposed. Observations which are no longer relevant, become default assumptions until overridden by newer, more prevalent observations. A distinction is made between background and foreground beliefs. Agent actions and environment events are distinguishable and form part of the agent model. It is left up to the agent designer to provide an environment model; a submodel of the agent model. An example of an environment model is provided in the paper, and an example scenario is based on it. Given the particular form of the agent model, several ‘patterns of cognition’ can be identified. An argument is made for four particular patterns.},
  year = {2016},
}

Sometimes, strictly choosing between belief revision and belief update is inadequate in a dynamical, uncertain environment. Boutilier combined the two notions to allow updates in response to external changes to inform an agent about its prior beliefs. His approach is based on ranking functions. Rens proposed a new method to trade off probabilistic revision and update, in proportion to the agent’s confidence for whether to revise or update. In this paper, we translate Rens’s approach from a probabilistic setting to a setting with ranking functions. Given the translation, we are able to compare Boutilier’s and Rens’s approaches.We found that Rens’s approach is an extension of Boutilier’s.

@misc{139,
  author = {Gavin Rens and Gabriele Kern-Isberner},
  title = {An Approach to Qualitative Belief Change Modulo Ontic Strength},
  abstract = {Sometimes, strictly choosing between belief revision and belief update is inadequate in a dynamical, uncertain environment. Boutilier combined the two notions to allow updates in response to external changes to inform an agent about its prior beliefs. His approach is based on ranking functions. Rens proposed a new method to trade off probabilistic revision and update, in proportion to the agent’s confidence for whether to revise or update. In this paper, we translate Rens’s approach from a probabilistic setting to a setting with ranking functions. Given the translation, we are able to compare Boutilier’s and Rens’s approaches.We found that Rens’s approach is an extension of Boutilier’s.},
  year = {2016},
}

I propose a framework for an agent to change its probabilistic beliefs when a new piece of propositional information alpha is observed. Traditionally, belief change occurs by either a revision process or by an update process, depending on whether the agent is informed with alpha in a static world or, respectively, whether alpha is a ‘signal’ from the environment due to an event occurring. Boutilier suggested a unified model of qualitative belief change, which “combines aspects of revision and update, providing a more realistic characterization of belief change.” In this paper, I propose a unified model of quantitative belief change, where an agent’s beliefs are represented as a probability distribution over possible worlds. As does Boutilier, I take a dynamical systems perspective. The proposed approach is evaluated against several rationality postulated, and some properties of the approach are worked out.

@misc{132,
  author = {Gavin Rens},
  title = {On Stochastic Belief Revision and Update and their Combination},
  abstract = {I propose a framework for an agent to change its probabilistic beliefs when a new piece of propositional information alpha is observed. Traditionally, belief change occurs by either a revision process or by an update process, depending on whether the agent is informed with alpha in a static world or, respectively, whether alpha is a ‘signal’ from the environment due to an event occurring. Boutilier suggested a unified model of qualitative belief change, which “combines aspects of revision and update, providing a more realistic characterization of belief change.” In this paper, I propose a unified model of quantitative belief change, where an agent’s beliefs are represented as a probability distribution over possible worlds. As does Boutilier, I take a dynamical systems perspective. The proposed approach is evaluated against several rationality postulated, and some properties of the approach are worked out.},
  year = {2016},
  isbn = {ISSN 0933-6192},
}

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},
}

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},
}

Proactive monitoring and control of indoor air quality in homes where there are pregnant mothers and infants is essential for healthy development and well-being of children. This is especially true in low income households where cooking practices and exposure to harmful pollutants produced by nearby industries can negatively impact on a healthy home environment. Interdisciplinary expert knowledge is required to make sense of dynamic and complex environmental phenomena from multivariate low level sensor observations and high level human activities to detect health risks and enact decisions about control. We have developed an ontology for indoor environmental quality monitoring and control based on an ongoing real world case study in Durban, South Africa. We implemented an Indoor Air Quality Index and a thermal comfort index which can be automatically determined by reasoning on the ontology. We evaluated the ontology by populating it with test sensor data and showing how it can be queried to analyze health risk situations and determine control actions. Our evaluation shows that the ontology can be used for real world indoor monitoring and control applications in resource constrained settings.

@{127,
  author = {Jude Adeleke and Deshen Moodley},
  title = {An Ontology for Proactive Indoor Environmental Quality Monitoring and Control},
  abstract = {Proactive monitoring and control of indoor air quality in homes where there are pregnant mothers and infants is essential for healthy development and well-being of children. This is especially true in low income households where cooking practices and exposure to harmful pollutants produced by nearby industries can negatively impact on a healthy home environment. Interdisciplinary expert knowledge is required to make sense of dynamic and complex environmental phenomena from multivariate low level sensor observations and high level human activities to detect health risks and enact decisions about control. We have developed an ontology for indoor environmental quality monitoring and control based on an ongoing real world case study in Durban, South Africa. We implemented an Indoor Air Quality Index and a thermal comfort index which can be automatically determined by reasoning on the ontology. We evaluated the ontology by populating it with test sensor data and showing how it can be queried to analyze health risk situations and determine control actions. Our evaluation shows that the ontology can be used for real world indoor monitoring and control applications in resource constrained settings.},
  year = {2015},
  journal = {The 2015 Annual Conference of the South African Institute of Computer Scientists and Information Technologists (SAICSIT '15)},
  month = {28/09-30/09},
  address = {New York, NY, USA ©2015},
  isbn = {978-1-4503-3683-3},
}

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.

@{115,
  author = {Richard Booth},
  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 = {IJCAI 2015},
  month = {25/07-31/07},
}

In recent years, various approaches have been developed for representing and reasoning with exceptions in OWL. The price one pays for such capabilities, in terms of practical performance, is an important factor that is yet to be quantified comprehensively. A major barrier is the lack of naturally occurring ontologies with defeasible features - the ideal candidates for evaluation. Such data is unavailable due to absence of tool support for representing defeasible features. In the past, defeasible reasoning implementations have favoured automated generation of defeasible ontologies. While this suffices as a preliminary approach, we posit that a method somewhere in between these two would yield more meaningful results. In this work, we describe a systematic approach to modify real-world OWL ontologies to include defeasible features, and we apply this to the Manchester OWL Repository to generate defeasible ontologies for evaluating our reasoner DIP (Defeasible-Inference Platform). The results of this evaluation are provided together with some insights into where the performance bottle-necks lie for this kind of reasoning. We found that reasoning was feasible on the whole, with surprisingly few bottle-necks in our evaluation.

@{113,
  author = {Giovanni Casini and Tommie Meyer and Kody Moodley and Ivan Varzinczak and U. Sattler},
  title = {Introducing Defeasibility into OWL Ontologies},
  abstract = {In recent years, various approaches have been developed for representing and reasoning with exceptions in OWL. The price one pays for such capabilities, in terms of practical performance, is an important factor that is yet to be quantified comprehensively. A major barrier is the lack of naturally occurring ontologies with defeasible features - the ideal candidates for evaluation. Such data is unavailable due to absence of tool support for representing defeasible features. In the past, defeasible reasoning implementations have favoured automated generation of defeasible ontologies. While this suffices as a preliminary approach, we posit that a method somewhere in between these two would yield more meaningful results. In this work, we describe a systematic approach to modify real-world OWL ontologies to include defeasible features, and we apply this to the Manchester OWL Repository to generate defeasible ontologies for evaluating our reasoner DIP (Defeasible-Inference Platform). The results of this evaluation are provided together with some insights into where the performance bottle-necks lie for this kind of reasoning. We found that reasoning was feasible on the whole, with surprisingly few bottle-necks in our evaluation.},
  year = {2015},
  journal = {The International Semantic Web Conference},
  month = {11/10-15/10},
}

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.

@{111,
  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},
  pages = {582-587},
  month = {18/05-20/05},
  isbn = {978-1-57735-730-8},
}

Interoperability and system integration are central problems that limit the effective use of health information systems to improve efficiency and effectiveness of health service delivery. There is currently no proven technology that provides a general solution in low and middle income countries where the challenges are especially acute. Engineering health information systems in low resource environments have several challenges that include poor infrastructure, skills shortages, fragmented and piecemeal applications deployed and managed by multiple organisations as well as low levels of resourcing. An important element of modern solutions to these problems is a health information exchange that enable disparate systems to share health information. It is a challenging task to develop systems as complex as health information exchanges that will have wide applicability in low and middle income countries. This work takes a case study approach and uses the development of a health information exchange in Rwanda as the case study. This research reports on the design, implementation and analysis of an architecture, the Health Information Mediator, that is a central component of a health information exchange. While such architectures have been used successfully in high income countries their efficacy has not been demonstrated in low and middle income countries. The Rwandan case study was used to understand and identify the challenges and requirements for health information exchange in low and middle income countries. These requirements were used to derive a set of key concerns for the architecture that were then used to drive its design. Novel features of the architecture include: the ability to mediate messages at both the service provider and service consumer interfaces; support for multiple internal representations of messages to facilitate the adoption of new and evolving standards; and the provision of a general method for mediating health information exchange transactions agnostic of the type of transactions. The architecture is shown to satisfy the key concerns and was validated by implementing and deploying a reference application, the OpenHIM, within the Rwandan health information exchange. The architecture is also analysed using the Architecture Trade-off Analysis Method. It has also been successfully implemented in other low and middle income countries with relatively minor configuration changes which demonstrates the architectures generalizability.

@phdthesis{110,
  author = {Ryan Crichton and Anban Pillay and Deshen Moodley},
  title = {The Open Health Information Mediator: an Architecture for Enabling Interoperability in Low to Middle Income Countries},
  abstract = {Interoperability and system integration are central problems that limit the effective use of health information systems to improve efficiency and effectiveness of health service delivery. There is currently no proven technology that provides a general solution in low and middle income countries where the challenges are especially acute. Engineering health information systems in low resource environments have several challenges that include poor infrastructure, skills shortages, fragmented and piecemeal applications deployed and managed by multiple organisations as well as low levels of resourcing. An important element of modern solutions to these problems is a health information exchange that enable disparate systems to share health information. 

It is a challenging task to develop systems as complex as health information exchanges that will have wide applicability in low and middle income countries. This work takes a case study approach and uses the development of a health information exchange in Rwanda as the case study. This research reports on the design, implementation and analysis of an architecture, the Health Information Mediator, that is a central component of a health information exchange. While such architectures have been used successfully in high income countries their efficacy has not been demonstrated in low and middle income countries. The Rwandan case study was used to understand and identify the challenges and requirements for health information exchange in low and middle income countries. These requirements were used to derive a set of key concerns for the architecture that were then used to drive its design. Novel features of the architecture include: the ability to mediate messages at both the service provider and service consumer interfaces; support for multiple internal representations of messages to facilitate the adoption of new and evolving standards; and the provision of a general method for mediating health information exchange transactions agnostic of the type of transactions.

The architecture is shown to satisfy the key concerns and was validated by implementing and deploying a reference application, the OpenHIM, within the Rwandan health information exchange. The architecture is also analysed using the Architecture Trade-off Analysis Method. It has also been successfully implemented in other low and middle income countries with relatively minor configuration changes which demonstrates the architectures generalizability.},
  year = {2015},
  volume = {MSc},
}

Representing data that changes over time in conceptual data models is required by various application domains, and requires a language that is expressive enough to fully capture the operational semantics of the time-varying information. Temporal modelling languages typically focus on representing and reasoning over temporal classes and relationships, but have scant support for temporal attributes, if at all. This prevents one to fully utilise a temporal conceptual data model, which, however, is needed to model not only evolving objects (e.g., an employee’s role), but also its attributes, such as changes in salary and bonus payouts. To characterise temporal attributes precisely, we use the DLRUS Description Logic language to provide its model-theoretic semantics, there- with essentially completing the temporal ER language ERVT. The new notion of status attribute is introduced to capture the possible changes, which results in several logical implications they entail, including their interaction with temporal classes to ensure correct behaviour in subsumption hierarchies, paving the way to verify automatically whether a temporal conceptual data model is consistent.

@{105,
  author = {Nasubo Ongoma and Maria Keet},
  title = {Temporal Attributes: Status and Subsumption},
  abstract = {Representing data that changes over time in conceptual data models is required by various application domains, and requires a language that is expressive enough to fully capture the operational semantics of the time-varying information. Temporal modelling languages typically focus on representing and reasoning over temporal classes and relationships, but have scant support for temporal attributes, if at all. This prevents one to fully utilise a temporal conceptual data model, which, however, is needed to model not only evolving objects (e.g., an employee’s role), but also its attributes, such as changes in salary and bonus payouts. To characterise temporal attributes precisely, we use the DLRUS Description Logic language to provide its model-theoretic semantics, there- with essentially completing the temporal ER language ERVT. The new notion of status attribute is introduced to capture the possible changes, which results in several logical implications they entail, including their interaction with temporal classes to ensure correct behaviour in subsumption hierarchies, paving the way to verify automatically whether a temporal conceptual data model is consistent.},
  year = {2015},
  journal = {Asia-Pacific Conference on Conceptual Modelling},
  pages = {61-70},
  month = {27/01-30/01},
  address = {Sydney, Australia},
  isbn = {978-1-921770-47-0},
}

A novel algorithm to speed up online planning in partially observable Markov decision processes (POMDPs) is introduced. I propose a method for compressing nodes in belief-decision-trees while planning occurs. Whereas belief-decision-trees branch on actions and observations, with my method, they branch only on actions. This is achieved by unifying the branches required due to the nondeterminism of observations. The method is based on the expected values of domain features. The new algorithm is experimentally compared to three other online POMDP algorithms, outperforming them on the given test domain.

@{104,
  author = {Gavin Rens},
  title = {Speeding up Online POMDP Planning: Unification of Observation Branches by Belief-state Compression via Expected Feature Values},
  abstract = {A novel algorithm to speed up online planning in partially observable Markov decision processes (POMDPs) is introduced. I propose a method for compressing nodes in belief-decision-trees while planning occurs. Whereas belief-decision-trees branch on actions and observations, with my method, they branch only on actions. This is achieved by unifying the branches required due to the nondeterminism of observations. The method is based on the expected values of domain features. The new algorithm is experimentally compared to three other online POMDP algorithms, outperforming them on the given test domain.},
  year = {2015},
  journal = {International Conference on Agents and Artificial Intelligence (ICAART) Vol. 2},
  pages = {241-246},
  month = {10/01-12/01},
  isbn = {978-989-758-074-1},
}