Assimilating Planning Domain Knowledge from Other Agents
Tim Grant · 2007
Mainstream research in planning assumes that input information is complete and correct. There are branches of research into plan generation with incomplete planning problems and with incomplete domain models. Approaches include gaining knowledge aimed at making the input information complete or building robust planners that can generate plans despite the incompleteness of the input. This paper addresses planning with complete and correct input information, but where the domain models are distributed over multiple agents. The emphasis is on domain model acquisition, i.e. the first approach. The research reported here adopts the view that the agents must share knowledge if planning is to succeed. This implies that a recipient must be able to assimilate the shared knowledge with its own. An algorithm for inducing domain models from example domain states is presented. The paper shows how the algorithm can be applied to knowledge assimilation and discusses the choice of representation for knowledge sharing. The algorithm has been implemented and applied successfully to eight domains. For knowledge assimilation it has been applied to date just to the blocks world.