Deshen Moodley

2019

Price, C. S. ., Moodley, D. ., & Pillay, A. . (2019). Modelling uncertain adaptive decisions: Application to KwaZulu-Natal sugarcane growers. In Forum for Artificial Intelligence Research (FAIR2019). Cape Town: CEUR. Retrieved from http://ceur-ws.org/Vol-2540/FAIR2019_paper_53.pdf

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FAIR2019_paper_53.pdf

A dynamic Bayesian decision network was developed to model the preharvest burning decision-making processes of sugarcane growers in a KwaZulu-Natal sugarcane supply chain and extends previous work by Price et al. (2018). This model was created using an iterative development approach. This paper recounts the development and validation process of the third version of the model. The model was validated using Pitchforth and Mengersen (2013)’s framework for validating expert elicited Bayesian networks. During this process, growers and cane supply members assessed the model in a focus group by executing the model, and reviewing the results of a prerun scenario. The participants were generally positive about how the model represented their decision-making processes. However, they identified some issues that could be addressed in the next iteration. Dynamic Bayesian decision networks offer a promising approach to modelling adaptive decisions in uncertain conditions. This model can be used to simulate the cognitive mechanism for a grower agent in a simulation of a sugarcane supply chain.

@{244,
  author = {C. Sue Price and Deshen Moodley and Anban Pillay},
  title = {Modelling uncertain adaptive decisions: Application to KwaZulu-Natal sugarcane growers},
  abstract = {A dynamic Bayesian decision network was developed to model the preharvest burning decision-making processes of sugarcane growers in a KwaZulu-Natal sugarcane supply chain and extends previous work by Price et al. (2018). This model was created using an iterative development approach. This paper recounts the development and validation process of the third version of the model. The model was validated using Pitchforth and Mengersen (2013)’s framework for validating expert elicited Bayesian networks. During this process, growers and cane supply members assessed the model in a focus group by executing the model, and reviewing the results of a prerun scenario. The participants were generally positive about how the model represented their decision-making processes. However, they identified some issues that could be addressed in the next iteration. Dynamic Bayesian decision networks offer a promising approach to modelling adaptive decisions in uncertain conditions. This model can be used to simulate the cognitive mechanism for a grower agent in a simulation of a sugarcane supply chain.},
  year = {2019},
  journal = {Forum for Artificial Intelligence Research (FAIR2019)},
  pages = {145-160},
  month = {4/12-6/12},
  publisher = {CEUR},
  address = {Cape Town},
  url = {http://ceur-ws.org/Vol-2540/FAIR2019_paper_53.pdf},
}

Nudelman, Z. ., Moodley, D. ., & Berman, S. . (2019). Using Bayesian Networks and Machine Learning to Predict Computer Science Success. In Annual Conference of the Southern African Computer Lecturers’ Association. Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-3-030-05813-5_14 (Original work published 2018)

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Nudelman2019_Chapter_UsingBayesianNetworksAndMachin.pdf

Bayesian Networks and Machine Learning techniques were evaluated and compared for predicting academic performance of Computer Science students at the University of Cape Town. Bayesian Networks performed similarly to other classification models. The causal links inherent in Bayesian Networks allow for understanding of the contributing factors for academic success in this field. The most effective indicators of success in first-year ‘core’ courses in Computer Science included the student’s scores for Mathematics and Physics as well as their aptitude for learning and their work ethos. It was found that unsuccessful students could be identified with ≈ 91% accuracy. This could help to increase throughput as well as student wellbeing at university.

@{216,
  author = {Z. Nudelman and Deshen Moodley and S. Berman},
  title = {Using Bayesian Networks and Machine Learning to Predict Computer Science Success},
  abstract = {Bayesian Networks and Machine Learning techniques were evaluated and compared for predicting academic performance of Computer Science students at the University of Cape Town. Bayesian Networks performed similarly to other classification models. The causal links inherent in Bayesian Networks allow for understanding of the contributing factors for academic success in this field. The most effective indicators of success in first-year ‘core’ courses in Computer Science included the student’s scores for Mathematics and Physics as well as their aptitude for learning and their work ethos. It was found that unsuccessful students could be identified with   ≈ 91% accuracy. This could help to increase throughput as well as student wellbeing at university.},
  year = {2019},
  journal = {Annual Conference of the Southern African Computer Lecturers&#039; Association},
  pages = {207-222},
  month = {18/06/2018 - 20/06/2018},
  publisher = {Springer},
  isbn = {978-3-030-05813-5},
  url = {https://link.springer.com/chapter/10.1007/978-3-030-05813-5_14},
}

2018

Moodley, D. ., Pillay, A. ., & Seebregts, C. . (2018). Establishing a Health Informatics Research Laboratory in South Africa . In Digital Re-imagination Colloquium 2018. NEMISA. Retrieved from http://uir.unisa.ac.za/bitstream/handle/10500/25615/Digital%20Skills%20Proceedings%202018.pdf?sequence=1&isAllowed=y

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HeAL-NEMISA-2018.pdf

Aim/Purpose The aim of this project was to explore models for stimulating health informatics innovation and capacity development in South Africa. Background There is generally a critical lack of health informatics innovation and capacity in South Africa and sub-Saharan Africa. This is despite the wide anticipation that digital health systems will play a fundamental role in strengthening health systems and improving service delivery Methodology We established a program over four years to train Masters and Doctoral students and conducted research projects across a wide range of biomedical and health informatics technologies at a leading South African university. We also developed a Health Architecture Laboratory Innovation and Development Ecosystem (HeAL-IDE) designed to be a long-lasting and potentially reproducible output of the project. Contribution We were able to demonstrate a successful model for building innovation and capacity in a sustainable way. Key outputs included: (i)a successful partnership model; (ii) a sustainable HeAL-IDE; (iii) research papers; (iv) a world-class software product and several demonstrators; and (iv) highly trained staff. Findings Our main findings are that: (i) it is possible to create a local ecosystem for innovation and capacity building that creates value for the partners (a university and a private non-profit company); (ii) the ecosystem is able to create valuable outputs that would be much less likely to have been developed singly by each partner, and; (iii) the ecosystem could serve as a powerful model for adoption in other settings. Recommendations for Practitioners Non-profit companies and non-governmental organizations implementing health information systems in South Africa and other low resource settings have an opportunity to partner with local universities for purposes of internal capacity development and assisting with the research, reflection and innovation aspects of their projects and programmes. Recommendation for Researchers Applied health informatics researchers working in low resource settings could productively partner with local implementing organizations in order to gain a better understanding of the challenges and requirements at field sites and to accelerate the testing and deployment of health information technology solutions. Impact on Society This research demonstrates a model that can deliver valuable software products for public health. Future Research It would be useful to implement the model in other settings and research whether the model is more generally useful

@{252,
  author = {Deshen Moodley and Anban Pillay and Chris Seebregts},
  title = {Establishing a Health Informatics Research Laboratory in South Africa},
  abstract = {Aim/Purpose 
The aim of this project was to explore models for stimulating health
informatics innovation and capacity development in South Africa.
Background 
There is generally a critical lack of health informatics innovation and capacity in South Africa and sub-Saharan Africa. This is despite the wide anticipation that digital health systems will play a fundamental role in strengthening health systems and improving service delivery
Methodology 
We established a program over four years to train Masters and Doctoral students and conducted research projects across a wide range of biomedical and health informatics technologies at a leading South African university. We also developed a Health Architecture Laboratory Innovation and Development Ecosystem (HeAL-IDE) designed to be a long-lasting and potentially reproducible output of the project.
Contribution 
We were able to demonstrate a successful model for building innovation and capacity in a sustainable way. Key outputs included: (i)a successful partnership model; (ii) a sustainable HeAL-IDE; (iii) research papers; (iv) a world-class software product and several
demonstrators; and (iv) highly trained staff.
Findings 
Our main findings are that: (i) it is possible to create a local ecosystem for innovation and capacity building that creates value for the partners (a university and a private non-profit company); (ii) the ecosystem is able to create valuable outputs that would be much less likely to have been developed singly by each partner, and; (iii) the ecosystem could serve as a powerful model for adoption in other settings.
Recommendations for Practitioners
Non-profit companies and non-governmental organizations implementing health information systems in South Africa and other low resource settings have an opportunity to partner with local universities for purposes of internal capacity development and assisting with the research, reflection and innovation aspects of their projects and programmes.
Recommendation for Researchers
Applied health informatics researchers working in low resource settings could productively partner with local implementing organizations in order to gain a better understanding of the challenges and requirements at field sites and to accelerate the testing and deployment of health information technology solutions.
Impact on Society 
This research demonstrates a model that can deliver valuable software products for public health.
Future Research 
It would be useful to implement the model in other settings and research whether the model is more generally useful},
  year = {2018},
  journal = {Digital Re-imagination Colloquium 2018},
  pages = {16 - 24},
  month = {13/03 - 15/03},
  publisher = {NEMISA},
  isbn = {978-0-6399275-0-3},
  url = {http://uir.unisa.ac.za/bitstream/handle/10500/25615/Digital%20Skills%20Proceedings%202018.pdf?sequence=1&amp;isAllowed=y},
}

Waltham, M. ., Moodley, D. ., & Pillay, A. . (2018). Q-Cog: A Q-Learning Based Cognitive Agent Architecture for Complex 3D Virtual Worlds. Durban University.

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Q-Cog copy.pdf

Intelligent cognitive agents requiring a high level of adaptability should contain min- imal initial data and be able to autonomously gather new knowledge from their own experiences. 3D virtual worlds provide complex environments in which autonomous software agents may learn and interact. In many applications within this domain, such as video games and virtual reality, the environment is partially observable and agents must make decisions and react in real-time. Due to the dynamic nature of virtual worlds, adaptability is of great importance for virtual agents. The Reinforce- ment Learning paradigm provides a mechanism for unsupervised learning that allows agents to learn from their own experiences in the environment. In particular, the Q- Learning algorithm allows agents to develop an optimal action-selection policy based on their environment experiences. This research explores the potential of cognitive architectures utilizing Reinforcement Learning whereby agents may contain a library of action-selection policies within virtual environments. The proposed cognitive archi- tecture, Q-Cog, utilizes a policy selection mechanism to develop adaptable 3D virtual agents. Results from experimentation indicates that Q-Cog provides an eﬀective basis for developing adaptive self-learning agents for 3D virtual worlds.

@phdthesis{190,
  author = {Michael Waltham and Deshen Moodley and Anban Pillay},
  title = {Q-Cog: A Q-Learning Based Cognitive Agent  Architecture for Complex 3D Virtual Worlds},
  abstract = {Intelligent cognitive agents requiring a high level of adaptability should contain min- imal initial data and be able to autonomously gather new knowledge from their own experiences. 3D virtual worlds provide complex environments in which autonomous software agents may learn and interact. In many applications within this domain, such as video games and virtual reality, the environment is partially observable and agents must make decisions and react in real-time. Due to the dynamic nature of virtual worlds, adaptability is of great importance for virtual agents. The Reinforce- ment Learning paradigm provides a mechanism for unsupervised learning that allows agents to learn from their own experiences in the environment. In particular, the Q- Learning algorithm allows agents to develop an optimal action-selection policy based on their environment experiences. This research explores the potential of cognitive architectures utilizing Reinforcement Learning whereby agents may contain a library of action-selection policies within virtual environments. The proposed cognitive archi- tecture, Q-Cog, utilizes a policy selection mechanism to develop adaptable 3D virtual agents. Results from experimentation indicates that Q-Cog provides an eﬀective basis for developing adaptive self-learning agents for 3D virtual worlds.},
  year = {2018},
  volume = {MSc},
  publisher = {Durban University},
}

Price, C. S. ., Moodley, D. ., & Pillay, A. . (2018). Dynamic Bayesian decision network to represent growers’ adaptive pre-harvest burning decisions in a sugarcane supply chain. In Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists (SAICSIT ’18). New York NY: ACM. Retrieved from https://dl.acm.org/citation.cfm?id=3278681

Abstract BibTex Entry BibTeX Download

Sugarcane growers usually burn their cane to facilitate its harvesting and transportation. Cane quality tends to deteriorate after burning, so it must be delivered as soon as possible to the mill for processing. This situation is dynamic and many factors, including weather conditions, delivery quotas and previous decisions taken, affect when and how much cane to burn. A dynamic Bayesian decision network (DBDN) was developed, using an iterative knowledge engineering approach, to represent sugarcane growers’ adaptive pre-harvest burning decisions. It was evaluated against five different scenarios which were crafted to represent the range of issues the grower faces when making these decisions. The DBDN was able to adapt reactively to delays in deliveries, although the model did not have enough states representing delayed delivery statuses. The model adapted proactively to rain forecasts, but only adapted reactively to high wind forecasts. The DBDN is a promising way of modelling such dynamic, adaptive operational decisions.

@{181,
  author = {C. Sue Price and Deshen Moodley and Anban Pillay},
  title = {Dynamic Bayesian decision network to represent growers’ adaptive pre-harvest burning decisions in a sugarcane supply chain},
  abstract = {Sugarcane growers usually burn their cane to facilitate its harvesting and transportation.  Cane quality tends to deteriorate after burning, so it must be delivered as soon as possible to the mill for processing.  This situation is dynamic and many factors, including weather conditions, delivery quotas and previous decisions taken, affect when and how much cane to burn.  A dynamic Bayesian decision network (DBDN) was developed, using an iterative knowledge engineering approach, to represent sugarcane growers’ adaptive pre-harvest burning decisions.  It was evaluated against five different scenarios which were crafted to represent the range of issues the grower faces when making these decisions.  The DBDN was able to adapt reactively to delays in deliveries, although the model did not have enough states representing delayed delivery statuses.  The model adapted proactively to rain forecasts, but only adapted reactively to high wind forecasts.   The DBDN is a promising way of modelling such dynamic, adaptive operational decisions.},
  year = {2018},
  journal = {Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists (SAICSIT &#039;18)},
  pages = {89-98},
  month = {26/09-28/09},
  publisher = {ACM},
  address = {New York NY},
  isbn = {978-1-4503-6647-2},
  url = {https://dl.acm.org/citation.cfm?id=3278681},
}

2017

Ogundele, O. ., Moodley, D. ., Seebregts, C. ., & Pillay, A. . (2017). Building Semantic Causal Models to Predict Treatment Adherence for Tuberculosis Patients in Sub-Saharan Africa. In Software Engineering in Health Care, LNCS vol. 9062. Springer, Cham. http://doi.org/https://doi.org/10.1007/978-3-319-63194-3_6

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Ogundele-et-al-2017-FHIES-LNCS9062_1.pdf

Poor adherence to prescribed treatment is a major factor contributing to tuberculosis patients developing drug resistance and failing treatment. Treatment adherence behaviour is influenced by diverse personal, cultural and socio-economic factors that vary between regions and communities. Decision network models can potentially be used to predict treatment adherence behaviour. However, determining the network structure (identifying the factors and their causal relations) and the conditional probabilities is a challenging task. To resolve the former we developed an ontology supported by current scientific literature to categorise and clarify the similarity and granularity of factors.

@inbook{250,
  author = {Olukunle Ogundele and Deshen Moodley and Chris Seebregts and Anban Pillay},
  title = {Building Semantic Causal Models to Predict Treatment Adherence for Tuberculosis Patients in Sub-Saharan Africa},
  abstract = {Poor adherence to prescribed treatment is a major factor contributing to tuberculosis patients developing drug resistance and failing treatment. Treatment adherence behaviour is influenced by diverse personal, cultural and socio-economic factors that vary between regions and communities. Decision network models can potentially be used to predict treatment adherence behaviour. However, determining the network structure (identifying the factors and their causal relations) and the conditional probabilities is a challenging task. To resolve the former we developed an ontology supported by current scientific literature to categorise and clarify the similarity and granularity of factors.},
  year = {2017},
  journal = {Software Engineering in Health Care, LNCS vol. 9062},
  pages = {81 - 95},
  publisher = {Springer, Cham},
  isbn = {978-3-319-63193-6},
  doi = {https://doi.org/10.1007/978-3-319-63194-3_6},
}

Gueorguiev, V. ., & Moodley, D. . (2017). Hyperparameter Optimization for Astronomy. University of Cape Town. Retrieved from http://projects.cs.uct.ac.za/honsproj/cgi-bin/view/2017/gueorguiev_henhaeyono_stopforth.zip/#downloads

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Hyperparameter Optimization for Astronomy2017.pdf

The task of phenomenon classification in astronomy provides a novel and challenging setting for the application of state-of-the-art techniques addressing the problem of combined algorithm selection and hyperparameter optimization (CASH) of machine learning algorithms, which find local applications such as at the data-intensive Square Kilometre Array (SKA). This work will use various algorithms for CASH to explore the possibility and efficacy of hyperparameter optimization on improving performance of machine learning techniques for astronomy. Then, with focus on the Galaxy Zoo project, these algorithms will be used to conduct an indepth comparison of state-of-the-art in hyperparameter optimization (HPO) along with techniques that aim to improve performance on large datasets and expensive function evaluations. Finally, the likelihood for an integration with a cognitive vision system for astronomy will be examined by conducting a brief exploration into different feature extraction and selection methods.

@phdthesis{180,
  author = {V. Gueorguiev and Deshen Moodley},
  title = {Hyperparameter Optimization for Astronomy},
  abstract = {The task of phenomenon classification in astronomy provides a novel and challenging setting for the application of state-of-the-art techniques addressing the problem of combined
algorithm selection and hyperparameter optimization (CASH) of machine learning algorithms, which find local applications such as at the data-intensive Square Kilometre Array
(SKA). This work will use various algorithms for CASH to explore the possibility and efficacy of hyperparameter optimization on improving performance of machine learning
techniques for astronomy. Then, with focus on the Galaxy Zoo project, these algorithms will be used to conduct an indepth comparison of state-of-the-art in hyperparameter optimization
(HPO) along with techniques that aim to improve performance on large datasets and expensive function evaluations. Finally, the likelihood for an integration with a cognitive
vision system for astronomy will be examined by conducting a brief exploration into different feature extraction and selection methods.},
  year = {2017},
  volume = {Honours},
  publisher = {University of Cape Town},
  url = {http://projects.cs.uct.ac.za/honsproj/cgi-bin/view/2017/gueorguiev_henhaeyono_stopforth.zip/#downloads},
}

Seebregts, C. ., Pillay, A. ., Crichton, R. ., Singh, S. ., & Moodley, D. . (2017). 14 Enterprise Architectures for Digital Health. Global Health Informatics: Principles of EHealth and MHealth to Improve Quality of Care. Retrieved from 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

Abstract BibTex Entry BibTeX Download

• 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&amp;pg=PA173&amp;lpg=PA173&amp;dq=14+Enterprise+Architectures+for+Digital+Health&amp;source=bl&amp;ots=i6SQzaXiPp&amp;sig=zDLJ6lIqt3Xox3Lt5LNCuMkUoJ4&amp;hl=en&amp;sa=X&amp;ved=0ahUKEwivtK6jxPDYAhVkL8AKHXbNDY0Q6AEINDAB#v=onepage&amp;q=14%20Enterp},
}

Adeleke, J. A., Moodley, D. ., Rens, G. ., & Adewumi, A. . (2017). Integrating Statistical Machine Learning in a Semantic Sensor Web for Proactive Monitoring and Control. Sensors, 17(4). Retrieved from http://pubs.cs.uct.ac.za/archive/00001219/01/sensors-17-00807.pdf

Abstract BibTex Entry BibTeX Download PDF

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. . (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},
}