Modelling uncertain adaptive decisions: Application to KwaZulu-Natal sugarcane growers

Price, CS and Moodley, D and Pillay, AW (2019) Modelling uncertain adaptive decisions: Application to KwaZulu-Natal sugarcane growers, Proceedings of South African Forum for Artificial Intelligence Research, 4-6 December, 2019., Cape town, South Africa, 2540, 145-160, CEUR.

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A dynamic Bayesian decision network was developed to model the pre- harvest 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 vali- dated 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 pre- run 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.

Item Type: Conference paper
Subjects: Applied computing > Operations research > Industry and manufacturing > Supply chain management
Computing methodologies > Artificial intelligence > Knowledge representation and reasoning > Probabilistic reasoning
Computing methodologies > Artificial intelligence > Distributed artificial intelligence > Intelligent agents
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Date Deposited: 05 Mar 2020 11:45
Last Modified: 05 Mar 2020 11:45

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