This paper postulates a rigorous method for the construction of classical planning domain models. We describe, with the help of a non-trivial example, a tool supported method for encoding such models. The method results in an `object-centred' specification of the domain that lifts the representation from the level of the literal to the level of the object. Thus, for example, operators are defined in terms of how they change the state of objects, and planning states are defined as amalgams of the objects' states. The method features two classes of tools: for initial capture and validation of the domain model; and for operationalising the domain model (a process we call compilation) for later planning. Here we focus on compilation tools used to generate macros and goal orders to be utilised at plan generation time. We describe them in depth, and evaluate empirically their combined benefits in plan-generation speed-up. The method's main benefit is in helping the modeller to pro...

Condition satisfaction in planning has received a great deal of experimental and formal attention. A "Truth Criterion" lies at the heart of many planners and is critical to their capabilities and performance. However, there has been little study of ways in which the search space of a planner incorporating such a Truth Criterion can be guided. The aim of this document is to give a description of the use of condition "type" information to inform the search of an AI planner and to guide the production of answers by a planner's truth criterion algorithm. The authors aim to promote discussion on the merits or otherwise of using such domain-dependent condition type restrictions as a means to communicate valuable information from the domain writer to a general purpose domain-independent planner 1 . Introduction to Condition Typing Research in AI planning has introduced a range of progressively more powerful techniques to address increasingly more realistic applications (Allen, Hendler & Ta...

Contract Number: F49620-92-C-0042