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...

In this paper we describe a number of obstacles hampering the application of planning technology to real-world problems, as encountered in two real-world planning projects at JPL: MVP - a planning system for automated generation of image processing procedures# and LMCOA - an intelligent system for assistance in antenna operations. First, we

Our research concerns formal, expressive, objectcentred languages and tools for use in engineering domains for planning applications. In this paper we extend our recent work on an object-centred language for encoding precondition planning domains to a language called OCL h , designed for HTN planning. Domain encodings for HTN planners are particularly troublesome, because they tend to be used in knowledged-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. In this paper we define and illustrate a transparency property, together with a transparency checking tool, which helps the developer to encode a clear planning model in OCL h . Our encoding of the Tr...

This paper describes a multistrategy learning approach for automatic acquisition of planning operators. The two strategies are: (i) learning operators by observing expert solution traces, and (ii) refining operators through practice in a learning-by-doing paradigm. During observation, OBSERVER uses the knowledge that is naturally observable when experts solve problems, without the need of explicit instruction or interrogation. During practice, OBSERVER generates its own learning opportunities by solving practice problems. The inputs to our learning system are: the description language for the domain, experts' problem solving traces, and practice problems to allow learningby -doing operator refinement. Given these inputs, our system automatically acquires the preconditions and effects (including conditional effects and preconditions) of the operators. Our approach has been fully implemented in a system called OBSERVER on top of a non-linear planner PRODIGY.We present empirical results t...

While work on AI planning systems dates back to the early 70s, applications of AI planning systems are few and far bet ween. This paper describes several major obstacles to the fielding of planning systems related to: plan representation and usage, operational contexts of planning systems, and knowledge acquisition and maintenance for planning knowledge. We use examples drawn from our experiences in applying planning and decision support technology to two procedural-reasoning application tasks at an automated image processing task and an antenna control 1. Introduction Why have so few actual planning applications been fielded? this paper we describe a number of issues hindering such efforts, derived from working in two applications involving procedural reasoning: automated image processing system (called MVP - for VICAR Planner) and a decision support system for antenna operations (called LMCOA - for Link Monitor and Control Operator Assistant). We categorize these issues into three ...