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

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

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 10 th September 2003 jstclair@cs.uct.ac.za

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 10 th September 2003 jstclair@cs.uct.ac.za

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 10 th September 2003 jstclair@cs.uct.ac.za

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 10 th September 2003 jstclair@cs.uct.ac.za

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 10 th September 2003 jstclair@cs.uct.ac.za

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 10 th September 2003 jstclair@cs.uct.ac.za

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 10 th September 2003 jstclair@cs.uct.ac.za