C. Sue Price

2022

Price, C. S. ., Moodley, D. ., Pillay, A. ., & Rens, G. . (2022). An adaptive probabilistic agent architecture for modelling sugarcane growers’ decision-making. South African Computer Journal, 34(1). http://doi.org/https://doi.org/10.18489/sacj.v34i1.857

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An adaptive probabilistic agent architecture for modelling sugarcane growers’ decision-making

Building computational models of agents in dynamic, partially observable and stochastic environments is challenging. We propose a cognitive computational model of sugarcane growers’ daily decision-making to examine sugarcane supply chain complexities. Growers make decisions based on uncertain weather forecasts; cane dryness; unforeseen emergencies; and the mill’s unexpected call for delivery of a different amount of cane. The Belief-Desire-Intention (BDI) architecture has been used to model cognitive agents in many domains, including agriculture. However, typical implementations of this architecture have represented beliefs symbolically, so uncertain beliefs are usually not catered for. Here we show that a BDI architecture, enhanced with a dynamic decision network (DDN), suitably models sugarcane grower agents’ repeated daily decisions. Using two complex scenarios, we demonstrate that the agent selects the appropriate intention, and suggests how the grower should act adaptively and proactively to achieve his goals. In addition, we provide a mapping for using a DDN in a BDI architecture. This architecture can be used for modelling sugarcane grower agents in an agent-based simulation. The mapping of the DDN’s use in the BDI architecture enables this work to be applied to other domains for modelling agents’ repeated decisions in partially observable, stochastic and dynamic environments.

@article{488,
  author = {C. Sue Price and Deshen Moodley and Anban Pillay and Gavin Rens},
  title = {An adaptive probabilistic agent architecture for modelling sugarcane growers’ decision-making},
  abstract = {Building computational models of agents in dynamic, partially observable and stochastic environments is challenging.  We propose a cognitive computational model of sugarcane growers’ daily decision-making to examine sugarcane supply chain complexities.  Growers make decisions based on uncertain weather forecasts; cane dryness; unforeseen emergencies; and the mill’s unexpected call for delivery of a different amount of cane.  The Belief-Desire-Intention (BDI) architecture has been used to model cognitive agents in many domains, including agriculture.  However, typical implementations of this architecture have represented beliefs symbolically, so uncertain beliefs are usually not catered for.  Here we show that a BDI architecture, enhanced with a dynamic decision network (DDN), suitably models sugarcane grower agents’ repeated daily decisions.  Using two complex scenarios, we demonstrate that the agent selects the appropriate intention, and suggests how the grower should act adaptively and proactively to achieve his goals.  In addition, we provide a mapping for using a DDN in a BDI architecture.  This architecture can be used for modelling sugarcane grower agents in an agent-based simulation.  The mapping of the DDN’s use in the BDI architecture enables this work to be applied to other domains for modelling agents’ repeated decisions in partially observable, stochastic and dynamic environments.},
  year = {2022},
  journal = {South African Computer Journal},
  volume = {34},
  pages = {152-191},
  issue = {1},
  url = {https://sacj.cs.uct.ac.za/index.php/sacj/article/view/857},
  doi = {https://doi.org/10.18489/sacj.v34i1.857},
}

Wanyana, T. . ., Nzomo, M. ., Price, C. S. ., & Moodley, D. . (2022). Combining Machine Learning and Bayesian Networks for ECG Interpretation and Explanation. In Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE. INSTICC: SciTePress. http://doi.org/https://doi.org/10.5220/0011046100003188

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Combining Machine Learning and Bayesian Networks for ECG Interpretation and Explanation.pdf

We explore how machine learning (ML) and Bayesian networks (BNs) can be combined in a personal health agent (PHA) for the detection and interpretation of electrocardiogram (ECG) characteristics. We propose a PHA that uses ECG data from wearables to monitor heart activity, and interprets and explains the observed readings. We focus on atrial fibrillation (AF), the commonest type of arrhythmia. The absence of a P-wave in an ECG is the hallmark indication of AF. Four ML models are trained to classify an ECG signal based on the presence or absence of the P-wave: multilayer perceptron (MLP), logistic regression, support vector machine, and random forest. The MLP is the best performing model with an accuracy of 89.61% and an F1 score of 88.68%. A BN representing AF risk factors is developed based on expert knowledge from the literature and evaluated using Pitchforth and Mengersen’s validation framework. The P-wave presence or absence as determined by the ML model is input into the BN. The PHA is evaluated using sample use cases to illustrate how the BN can explain the occurrence of AF using diagnostic reasoning. This gives the most likely AF risk factors for the individual

@inbook{478,
  author = {Tezira Wanyana and Mbithe Nzomo and C. Sue Price and Deshen Moodley},
  title = {Combining Machine Learning and Bayesian Networks for ECG Interpretation and Explanation},
  abstract = {We explore how machine learning (ML) and Bayesian networks (BNs) can be combined in a personal health agent (PHA) for the detection and interpretation of electrocardiogram (ECG) characteristics. We propose a PHA that uses ECG data from wearables to monitor heart activity, and interprets and explains the observed readings. We focus on atrial fibrillation (AF), the commonest type of arrhythmia. The absence of a P-wave in an ECG is the hallmark indication of AF. Four ML models are trained to classify an ECG signal based on the presence or absence of the P-wave: multilayer perceptron (MLP), logistic regression, support vector machine, and random forest. The MLP is the best performing model with an accuracy of 89.61% and an F1 score of 88.68%. A BN representing AF risk factors is developed based on expert knowledge from the literature and evaluated using Pitchforth and Mengersen’s validation framework. The P-wave presence or absence as determined by the ML model is input into the BN. The PHA is evaluated using sample use cases to illustrate how the BN can explain the occurrence of AF using diagnostic reasoning. This gives the most likely AF risk factors for the individual},
  year = {2022},
  journal = {Proceedings of the 8th International Conference on Information and Communication Technologies for Ageing Well and e-Health - ICT4AWE},
  pages = {81-92},
  publisher = {SciTePress},
  address = {INSTICC},
  isbn = {978-989-758-566-1},
  doi = {https://doi.org/10.5220/0011046100003188},
}

2021

Mbonye, V. ., & Price, C. S. . (2021). Students’ use of on-campus wireless networks: Analysis by residence type. In 2021 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD). Durban, South Africa: IEEE. http://doi.org/https://doi.org/10.1109/icABCD51485.2021.9519327

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Universities supply free Wi-Fi to registered students on campus to access learning materials. Many issues could reduce the quality of students' Wi-Fi use, e.g., devices using different Wi-Fi standards than those used on campus, and numerous students accessing Wi-Fi through a single access point simultaneously. Understanding where, when and how students use Wi-Fi on campus can help IT administrators to provide an adequate Wi-Fi service. This pre-COVID study adopted a mixed method approach. Questionnaires were completed by a representative sample of 373 students on the Westville campus of the University of KwaZulu-Natal, South Africa. Two Information Communication Services (lCS) staff members were interviewed to gain insights into how the Wi-Fi was set up, and their perspectives on how students utilise the Wi-Fi. The questionnaire data were analysed statistically, and interview results were used to explain results. The students' most used venues, and the places they encountered poor and best Wi-Fi signal quality, are presented, along with the durations of use and problems encountered. When analysing these results by students' residence type, each category showed a different pattern of use. These results can help IT administrators understand where to improve the Wi-Fi quality, if necessary. It is particularly useful if some categories of students return to campus, while others do not, e.g. as COVID-19 lockdowns ease.

@{424,
  author = {V. Mbonye and C. Sue Price},
  title = {Students’ use of on-campus wireless networks: Analysis by residence type},
  abstract = {Universities supply free Wi-Fi to registered students on campus to access learning materials. Many issues could reduce the quality of students&#039; Wi-Fi use, e.g., devices using different Wi-Fi standards than those used on campus, and numerous students accessing Wi-Fi through a single access point simultaneously. Understanding where, when and how students use Wi-Fi on campus can help IT administrators to provide an adequate Wi-Fi service. This pre-COVID study adopted a mixed method approach. Questionnaires were completed by a representative sample of 373 students on the Westville campus of the University of KwaZulu-Natal, South Africa. Two Information Communication Services (lCS) staff members were interviewed to gain insights into how the Wi-Fi was set up, and their perspectives on how students utilise the Wi-Fi. The questionnaire data were analysed statistically, and interview results were used to explain results. The students&#039; most used venues, and the places they encountered poor and best Wi-Fi signal quality, are presented, along with the durations of use and problems encountered. When analysing these results by students&#039; residence type, each category showed a different pattern of use. These results can help IT administrators understand where to improve the Wi-Fi quality, if necessary. It is particularly useful if some categories of students return to campus, while others do not, e.g. as COVID-19 lockdowns ease.},
  year = {2021},
  journal = {2021 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD)},
  month = {5-6/08},
  publisher = {IEEE},
  address = {Durban, South Africa},
  isbn = {978-1-7281-8591-0},
  url = {https://ieeexplore.ieee.org/document/9519327},
  doi = {https://doi.org/10.1109/icABCD51485.2021.9519327},
}

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},
}

Mbonye, V. ., & Price, C. S. . (2019). A model to evaluate the quality of Wi-Fi perfomance: Case study at UKZN Westville campus. In 2nd International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD 2019). Danvers MA: IEEE.

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Understanding how satisfied users are with services is very important in the delivery of quality services and in improving them. While studies have investigated perceptions of Wi-Fi among students, there is still a gap in understanding the overall perception of quality of service in terms of the different factors that may affect Wi-Fi service quality. Brady & Cronin Jr’s service quality model proposes that outcome quality, physical environment quality and interaction quality affect service quality. Sub-constructs for the independent variables were generated, and Likert-scale items developed for each sub-construct, based on the literature. 373 questionnaires were administered to University of KwaZulu-Natal (UKZN) Westville campus students. Factor analysis was to confirm the sub-constructs. Multiple regression analysis was used to test the model’s ability to predict Wi-Fi service quality. Of the three independent constructs, the outcome quality mean had the highest value (4.53), and it was similar to how the students rated service quality (4.52). All the constructs were rated at above the neutral score of 4. In the factor analysis, two physical environment quality items were excluded, and one service quality item was categorised with the expertise sub-construct of interaction quality. Using multiple regression analysis, the model showed that the independent constructs predict service quality with an R2 of 59.5%. However, when models for individual most-used locations (the library and lecture venues) were conducted, the R2 improved. The model can be used to understand users’ perceptions of outcome quality, physical environment quality and interaction quality which influence the quality of Wi-Fi performance, and evaluate the Wi-Fi performance quality of different locations.

@{217,
  author = {V. Mbonye and C. Sue Price},
  title = {A model to evaluate the quality of Wi-Fi perfomance: Case study at UKZN Westville campus},
  abstract = {Understanding how satisfied users are with services is very important in the delivery of quality services and in improving them. While studies have investigated perceptions of Wi-Fi among students, there is still a gap in understanding the overall perception of quality of service in terms of the different factors that may affect Wi-Fi service quality.  Brady &amp; Cronin Jr’s service quality model proposes that outcome quality, physical environment quality and interaction quality affect service quality.  Sub-constructs for the independent variables were generated, and Likert-scale items developed for each sub-construct, based on the literature.  373 questionnaires were administered to University of KwaZulu-Natal (UKZN) Westville campus students.  Factor analysis was to confirm the sub-constructs.  Multiple regression analysis was used to test the model’s ability to predict Wi-Fi service quality.
Of the three independent constructs, the outcome quality mean had the highest value (4.53), and it was similar to how the students rated service quality (4.52).  All the constructs were rated at above the neutral score of 4.  In the factor analysis, two physical environment quality items were excluded, and one service quality item was categorised with the expertise sub-construct of interaction quality.  Using multiple regression analysis, the model showed that the independent constructs predict service quality with an R2 of 59.5%.  However, when models for individual most-used locations (the library and lecture venues) were conducted, the R2 improved.  The model can be used to understand users’ perceptions of outcome quality, physical environment quality and interaction quality which influence the quality of Wi-Fi performance, and evaluate the Wi-Fi performance quality of different locations.},
  year = {2019},
  journal = {2nd International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD 2019)},
  pages = {291-297},
  month = {05/08 - 06/08},
  publisher = {IEEE},
  address = {Danvers MA},
  isbn = {978-1-5386-9235-6},
}

2018

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

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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},
}

2014

Price, C. S. ., Moodley, D. ., & Bezuidenhout, C. . (2014). Using agent-based simulation to explore sugarcane supply chain transport complexities at a mill scale. In 43rd Annual Operations Research Society of South Africa Conference.

Abstract BibTex Entry BibTeX Download PDF

2014ORSSAConference_Price et al paper.pdf

The sugarcane supply chain (from sugarcane grower to mill) have particular challenges. One of these is that the growers have to deliver their cane to the mill before its quality degrades. The sugarcane supply chain typically consists of many growers and a mill. Growers deliver their cane daily during the milling season; the amount of cane they deliver depends on their farm size. Growers make decisions about when to harvest the cane, and the number and type of trucks needed to deliver their cane. The mill wants a consistent cane supply over the milling season. Growers are sometimes affected long queue lengths at the mill when they offload their cane. A preliminary agent-based simulation model was developed to understand this complex system. The model inputs a number of growers, and the amount of cane they are to deliver over the milling season. The number of trucks needed by each grower is determined by the trip, loading and unloading times and the anticipated waiting time at the mill. The anticipated waiting time was varied to determine how many trucks would be needed in the system to deliver the week’s cane allocation. As the anticipated waiting time increased, the number of trucks needed also increased, which in turn delayed the trucks when queuing at the mill. The growers’ anticipated waiting times never matched the actual waiting times. The research shows the promise of agent-based models as a sense-making approach to understanding systems where there are many individuals who have autonomous behaviour, and whose actions and interactions can result in unexpected system-level behaviour.

@{94,
author = {C. Sue Price and Deshen Moodley and C.N. Bezuidenhout},
title = {Using agent-based simulation to explore sugarcane supply chain transport complexities at a mill scale},
abstract = {The sugarcane supply chain (from sugarcane grower to mill) have particular challenges. One of these is that the growers have to deliver their cane to the mill before its quality degrades. The sugarcane supply chain typically consists of many growers and a mill. Growers deliver their cane daily during the milling season; the amount of cane they deliver depends on their farm size. Growers make decisions about when to harvest the cane, and the number and type of trucks needed to deliver their cane. The mill wants a consistent cane supply over the milling season. Growers are sometimes affected long queue lengths at the mill when they offload their cane.
A preliminary agent-based simulation model was developed to understand this complex system. The model inputs a number of growers, and the amount of cane they are to deliver over the milling season. The number of trucks needed by each grower is determined by the trip, loading and unloading times and the anticipated waiting time at the mill. The anticipated waiting time was varied to determine how many trucks would be needed in the system to deliver the week’s cane allocation. As the anticipated waiting time increased, the number of trucks needed also increased, which in turn delayed the trucks when queuing at the mill. The growers’ anticipated waiting times never matched the actual waiting times. The research shows the promise of agent-based models as a sense-making approach to understanding systems where there are many individuals who have autonomous behaviour, and whose actions and interactions can result in unexpected system-level behaviour.},
year = {2014},
journal = {43rd Annual Operations Research Society of South Africa Conference},
pages = {88-96},
month = {14/09-17/09},
isbn = {978-1-86822-656-6},
}