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Jonathan St Clair Computer Science Honours 2003

Supporting harvest prediction. using. artificial intelligence techniques. Jonathan St Clair Computer Science Honours 2003. Jonathan St Clair STCJON003 jstclair@cs.uct.ac.za 10 th September 2003. Background. Jonathan St Clair STCJON003

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Jonathan St Clair Computer Science Honours 2003

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  1. Supporting harvestprediction using artificial intelligence techniques Jonathan St Clair Computer Science Honours 2003 Jonathan St Clair STCJON003 jstclair@cs.uct.ac.za 10th September 2003

  2. Background • Jonathan St Clair • STCJON003 • jstclair@cs.uct.ac.za 10th September 2003

  3. Overview • On going research done to better predict harvest figures • Often historical data is incomplete thus making prediction difficult • Jonathan St Clair • STCJON003 • jstclair@cs.uct.ac.za 10th September 2003

  4. Complex Adaptive Systems • Too many variables for management to optimise production for both short and long term production • Not possible for management to work through every possible scenario • Seasonal variations difficult to predict • Jonathan St Clair • STCJON003 • jstclair@cs.uct.ac.za 10th September 2003

  5. Objectives • To identify aspects which could be meaningfully enhanced by the use of AI techniques • To select the most promising opportunity within the prediction and planning of the farm and • Describe the environment and its challenges in detail. • Select the most appropriate AI technique/s and describe their application to the problem. • Illustrate how the farm management will benefit from this application of technology to the business.  • Jonathan St Clair • STCJON003 • jstclair@cs.uct.ac.za 10th September 2003

  6. Deliverables • Interim report describing area of application for AI (I&J) • Software design document (UCT & I&J) • Final report (UCT & I&J) • Software prototype (UCT & I&J) • Jonathan St Clair • STCJON003 • jstclair@cs.uct.ac.za 10th September 2003

  7. Impact • Enable management to quickly and reliably assess the impact of changing any of a number of variables • Increase the ability of the farm management to prepare themselves to meet a particular demand in the best possible way • Jonathan St Clair • STCJON003 • jstclair@cs.uct.ac.za 10th September 2003

  8. Success Factors • The software must be shown to endorse or contradict decisions made using the current management system • A number of test cases, of the farmers choosing, will be constructed to allow for the farmer to make judgements in the normal fashion • The AI system will be tested on the same cases and if it is shown that the system is consistently more correct, the system will be deemed successful • Jonathan St Clair • STCJON003 • jstclair@cs.uct.ac.za 10th September 2003

  9. Related Work • Robert M. Dorazio and Fred A. Johnson, Bayesian and Decision Theory – A Coherent Framework for Decision Making in Natural Resource Management.  • Andrew Wilson, Consumer Demand and the Future of the Supply Chain  • Anet Potgieter, “Complex Adaptive Systems, Emergence and Engineering: The Basics.”  • Anet Potgieter, “Bayesian Behaviour Networks as Hyperstructures” • Fred Johnson & Ken Williams, “Protocol and Practice in the Adaptive Management of Waterfowl Harvests”, http://www.consecol.org/vol3/iss1/art8/ • Nils J. Nilsson, “Artificial Intelligence: A new Synthesis”      ISBN 1-55860-535-5, 37 -55, 343 – 346  • Jonathan St Clair • STCJON003 • jstclair@cs.uct.ac.za 10th September 2003

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