Reducing the Representational Distance Between Application Domain Experts and AI Planning Technology: A Compilation Approach

Peter Jarvis and Graham Winstanley

We present a compilation-based approach to reducing the representational distance between application domain experts and AI planning technology. The approach combines a representation designed to match the structure of human expertise in the construction industry with an established planning technique. The design of this representation is derived from a study carried out with experts in the industry. This study shows that expertise in the industry is centred on the components of a building and organised into a subcomponent structure. We demonstrate by encoding the results of this study into a HTN formalism that such formalisms fragment expert knowledge. This fragmentation leads to a large representational distance between expert and formalism, making the task of encoding and maintaining a planner knowledge base a complex one. Our solution is to provide a representation designed around the modelling requirements of the construction industry and then to compile HTN schemata from that representation. We argue that this union reduces the representational distance between expert and formalism, thus lowering the complexity of the knowledge encoding and maintenance tasks, whilst still exploiting powerful AI planning techniques. We conclude by proposing further investigations of this type with the aim of providing a library of domain-oriented formalisms from which a knowledge engineer may choose an appropriate representation for a given domain.


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