Generalised Domain Model Acquisition from Action Traces

One approach to the problem of formulating domain models for planning is to learn the models from example action se-quences. The LOCM system demonstrated the feasibility of learning domain models from example action sequences only, with no observation of states before, during or after the plans. LOCM uses an object-centred representation, in which each object is represented by a single parameterised state machine. This makes it powerful for learning domains which fit within that representation, but there are some well-known domains which do not. This paper introduces LOCM2, a novel algorithm in which the domain representation of LOCM is generalised to allow multiple parameterised state machines to represent a single object. This extends the coverage of domains for which an adequate domain model can be learned. The LOCM2 algo-rithm is described and evaluated by testing domain learning from example plans from published results of past Interna-tional Planning Competitions