Problem Solving and Action Planning Heuristic searching and knowledge-based problem solving are of constitutive character in artificial intelligence and areas of the psychology of reasoning. A high degree of theoretical and practical significance has been and is still being attached to improvements in search algorithms, e.g. in relation to uncertain information. Questions of the flexible availability of knowledge suitable for particular solutions to problems and of solution planning have arisen. This research deals with the exploration of large state spaces, e.g. as they occur in NP-hard search problems in AI, for example in one person games, but also in the verification of communication protocols, in automated theorem proving and in robot control. Beginning with a start state, a goal state is sought.
Transition rules prescribe how to get from one state to a possible successive state.
Therefore, we are particularly interested in duplicate elimination strategies and exploration methods which can function with a memory of a predetermined, definite size. Furthermore, we aim to examine strategies of learning heuristics and strategies of active search field changes, e.g. through interaction with the environment. Based on a qualitative conceptual representation and a quantitative representation of school physics knowledge on classical mechanics, the aim will be to verify, using empirical and cognitive modeling methods, in which conditions extensive and flexible solutions are made possible, and in which positive learning results can be expected . The modeling of problem solving behaviour will also be used for the development of instructional and testing materials. Action planning has become a great interest again in AI over the last few years due to the ability of recent planning algorithms to solve problems of a realistic size as well.