KDMAS: A Multi-Agent System for Knowledge Discovery via Planning

Li Jin, Keith Decker

In the real world, there are some domain knowledge discovery problems that can be formulated into knowledge-based planning problems, such as chemical reaction process and biological pathway discovery problems. A view of these domain problems can be re-cast as a planning problem, such that initial and final states are known and processes can be captured as abstract operators that modify the environment. We believe that AI planning technology can provide a modeling formalism for this task such that hypotheses can be generated, tested, queried and qualitatively simulated to improve the domain knowledge and rules. Our approach is to build a general multi-agent system for knowledge discovery (KDMAS) via planning for any domain whose problems can be modeled as AI planning problems. The plans produced are hypotheses capturing relevant qualitative information regarding domain knowledge. We will use the biological pathway domain as a model to present our approach.

Subjects: 1.11 Planning; 7.1 Multi-Agent Systems


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