Investigating Arbitration Strategies in an Animat Navigation System
This paper reports on recent experiments applying classifier systems to the problem of supporting both local and global navigation in a simulated animat. The basis of this research is a hybrid learning system that extends the classifier representation to enable environmental feedback to impinge directly upon the classifier population. The system applies a connectionist representation to the condition sets of classifiers which enables the direct encoding of classifier condition/fitness values onto network nodes. The goal of the system is to achieve domain objectives by calibrating classifier behaviour during the exploration of the domain and evolving new classifiers to exploit the domain by discovering goal states.
KeywordsClassifier Population Direct Encode Arbitration Strategy Autonomous Guide Vehicle Random Feature Selection
Unable to display preview. Download preview PDF.
- N. Ball. Cognitive Maps in Learning Classifier Systems. PhD thesis, University of Reading, Reading, UK., 1991.Google Scholar
- N. Ball. Organizing an Animat’s behavioural repertoires using Kohonen Feature Maps. MIT Press, 1994.Google Scholar
- N. Ball. Application of a neural network based classifier system to AGV obstacle avoidance, volume 1237, pages 1–12. Elsevier, 1996.Google Scholar
- J. Holland, K. Holyoak, R. Nisbett, and P. Thagard. Induction: Processes of Inference, Learning and Discovery. MIT Press, 1986.Google Scholar
- T. Kohonen. Self Organization and Associative Memory. Springer, 1984.Google Scholar
- P. Maes. Behaviour-based artificial intelligence. MIT Press, 1992.Google Scholar
- S. Wilson. The Animat Path to AI. MIT press, 1990.Google Scholar