Abstract. Flexible support for crisis management can definitely be improved by making use of advanced planning capabilities. However, the complexity of the underlying domain often causes intractable efforts in modeling the domain as well as a huge search space to be explored by the system. A way to overcome these problems is to impose a structure not only according to tasks but also according to relationships between and properties of the objects involved, thereby using so-called decomposition axioms. We outline the prototype of a system that is capable of tackling planning for complex application domains. It is based on a well-founded combination of action and state abstractions. The paper presents the basic techniques and provides a formal semantic foundation of the approach. It introduces the planning system and illustrates its underlying principles by examples taken from the crisis management domain used in our ongoing project. 1

Specifying operator descriptions for planning domain models, especially using standard pre- and post condition symbolism, is a slow and painstaking process. This is because one is trying to capture what is essentially procedural knowledge in a declarative way in a language whose design is influenced by the construction of planning engines. The problem is acute if nonplanning experts are undertaking this task, and/or the operators are complex or hierarchical. In this paper we describe opmaker, a method in which the domain expert specifies the declarative structure of the domain (in terms of an object hierarchy, object descriptions etc) and provides training operator sequences. This input is made in the context of a tools environment supporting planner domain acquisition and modelling. opmaker then induces a set of parameterised operator descriptions from these examples, removing the need for the user to become involved in complex parameter manipulation within the underlying symbolic, logicbased language. We discuss the empirical evaluation of the implemented induction algorithm with the help of a range of domains, and draw conclusions for future work.

engineering process in the building of applied AI planning systems. GIPO embodies an object centred approach to planning domain modelling. There are two reasons for providing knowledge engineering support for AI planning: (i) to apply a planning system to a new domain to test the planning system itself (ii) to tackle the end-user problem for the engineer who might be a domain expert but need not necessarily have a specialist knowledge of AI planning. Our research is primarily aimed at developing a method and tools to meet the requirements of the latter case (ii), although the benefits can also be enjoyed by planning experts. 1.

: Recent successful applications of AI planning technology have highlighted the knowledge engineering of planning domain models as an important research area. We describe a prototype implementation of a translation algorithm between two languages used in planning representation: PDDL, a language used for communication of example domains between research groups, and OCL h , a language developed specifically for planning domain modelling. The translation algorithm has been used as part of OCL h 's tool support to import models expressed in PDDL to OCL h 's environment. In this paper we detail the translation algorithm between the two languages, and discuss the issues that it uncovers. The tool performs well when its output is measured against hand-crafted OCL h models, but more importantly, we show how it has helped uncover insecurities in PDDL encodings. 1 Introduction Despite many years of research into AI Planning and Scheduling, knowledge engineering for applications of AI Plannin...

Contents 1 Introduction 2 2 Domain Model Components 2 2.1 A Brief Description of the Components of OCL h . . . . . . . . . . . . . . . . 2 2.2 Some Key Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3 Guidelines for Writing OCL h 8 3.1 Natural Language Description . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.2 Identifying Sorts and Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.3 Relationships and Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.4 Substate Class Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.5 State Invariants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.6 Operator Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4 Task Specification 13 5 Development of an Hierarchical Domain Model 14 5.1 Sort Hierarchy . . . . . . . .

In order to make planning technology more accessible and usable the planning community may have to adopt standard notations for embodying symbolic models of planning domains.