Despite the advances made in the last decade in automated planning, no planner outperforms all the others in every known benchmark domain. This observation motivates the idea of selecting different planning algorithms for different domains. Moreover, the planners' performances are affected by the structure of the search space, which depends on the encoding of the considered domain. In many domains, the performance of a planner can be improved by exploiting additional knowledge, extracted in the form of macro-operators or entanglements. In this paper we propose ASAP, an automatic Algorithm Selection Approach for Planning that: (i) for a given domain initially learns additional knowledge, in the form of macro-operators and entanglements, which is used for creating different encodings of the given planning domain and problems, and (ii) explores the 2 dimensional space of available algorithms, defined as encodings -- planners couples, and then (iii) selects the most promising algorithm for optimising either the runtimes or the quality of the solution plans.

Our research concerns formal expressive, object centred languages and tools for use in engineering domains for planning applications. We extend our recent work on an object centred language for encoding precondition planning domains to a language called OCL/sub h/, designed for HTN planning. Domain encodings for HTN planners are particularly troublesome, because they tend to be used in knowledge based applications requiring a great deal of 'domain engineering', and the abstract operators central to an HTN model do not share the fairly clear declarative semantics of concrete pre- and post-condition operators. Central to our approach is the development, in parallel, of the abstract operator set and the hierarchical state specification of the objects that the operators manipulate. We also define and illustrate a transparency property, together with a transparency checking tool, which helps the developer to encode a clear planning model in OCL/sub h/. Our encoding of the Translog domain is used as an extended example to illustrate the approach.

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 an 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.