Can AI planners solve practical problems?
Computational Intelligence
Why real-world planning is difficult: a tale of two applications
New directions in AI planning
Engineering and compiling planning domain models to promote validity and efficiency
Artificial Intelligence
Remote Agent: to boldly go where no AI system has gone before
Artificial Intelligence - Special issue: artificial intelligence 40 years later
Knowledge Representation in Planning: A PDDL to OCLh Translation
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
The automatic inference of state invariants in TIM
Journal of Artificial Intelligence Research
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Knowledge engineering in AI planning is the process that deals with the acquisition, validation and maintenance of planning domain models, and the selection and optimisation of appropriate planning machinery to work on them. Our aim is to research and develop rigorous methods for the acquisition, maintenance and validation of planning domain models. We aim to provide a tools environment suitable for use by domain experts in addition to experts in the field of AI planning. In this paper we describe such a method and illustrate it with screen-shots taken from an implemented Graphical Interface for Planning with Objects system called GIPO. The GIPO tools environment has been built to support an object centred approach to planning domain modelling. The principal innovation we present in this paper is a process of specifying domain operators that abstracts away much of the technical detail traditionally required in their specification. Such innovations we believe could ultimately open up the possibility of bringing planning technology to a wider public.