A great deal of emphasis in classical AI planning research has been placed on search-control issues in plan generation, while the issue of knowledge representation and acquisition of models for use with classical planning engines has been largely ignored. Work in knowledge-based planning, on the other hand, is often associated with `scruffy' AI, there being no standard representation languages with associated formal semantics for encoding domain models. In this paper we describe a method to create a planning domain model which preserves the domain independence, generality and `clean' properties of generative planners to which the model can be attached. Our method is based on lifting the level of domain representation from the literal-centred, to the object-centred. This object-centred method has the advantage that it naturally allows for the creation of a supporting tools environment to help in (i) the creation and validation of a precise planning model, and (ii) the speed-up of plan generation.
|Number of pages
|Proceedings of the International Conference on Tools with Artificial Intelligence
|Published - Dec 1996