This thesis studies the problem of planning and problem solving in an unpredictable environment by adapting previous experiences. We construct a single agent planning system CADDY and operate it in a simple golf world testbed. The study of CADDY combines the studies of probabilistic, spatial, and temporal reasoning, adapting and reusing plans, and the tradeoff between gains and costs based on various considerations.
The CADDY planning system operates in an uncertain and unpredictable environment. Despite limited perception, incomplete knowledge, and imperfect motion control, CADDY achieves its goal efficiently by finding a plan that is already known to work well in a similar situation and applying repair heuristics to improve it. The capability of adapting experiences makes CADDY a planning system with learning capability.
In this thesis, we discuss the structure of the CADDY planning system and the results of experimental tests of CADDY when we applied to a simulated golf world. We compare CADDY with several other research projects on probabilistic planners and planners which utilizes experiences. We also discuss how CADDY can be characterized in terms of theoretical work on plan feasibility. Finally, we point out possible directions of system extension and generalizations of the idea learned from CADDY to other problem domains. Currently CADDY is not directly applied to real-world problems, but it shows an interesting and promising direction of study. By combining the techniques of probabilistic reasoning, planning, and learning, the performance of planning on real-world domains can be improved dramatically.