A Domain-Independent System for Case-Based Task Decomposition without Domain Theories

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 20

Analogical and Case-Based Reasoning

We propose using domain-independent task decomposition techniques for situations in which cases are the sole or the main source for domain knowledge. Our work is motivated by project planning domains, where hierarchical cases are readily available, but neither a planning domain theory nor case adaptation knowledge is available. We present DInCaD (Domain-Independent System for Case-Based Task Decomposition), a system that encompasses case retrieval, refinement, and reuse, following from the idea of reusing generalized cases to solve new problems. DInCaD consists of a case refinement procedure that reduces case over-generalization, and a similarity criterion that takes advantage of the refinement to improve case retrieval precision. We will analyze the properties of the system, and present an empirical evaluation.