Formal knowledge engineering for planning: Pre and post-design analysis
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2020-03-25
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SILVA, J. R.
SILVA, J. M. Javier Martinez Silva
VAQUERO, T. S.
SILVA, J. M. Javier Martinez Silva
VAQUERO, T. S.
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Knowledge Engineering Tools and Techniques for AI Planning
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SILVA, J. R.; SILVA, J. M.; VAQUERO, T. S. Formal knowledge engineering for planning: Pre and post-design analysis. Knowledge Engineering Tools and Techniques for AI Planning, p. 47-65, 2020.
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© Springer Nature Switzerland AG 2020.The interest and scope of the area of autonomous systems have been steadily growing in the last 20 years. Artificial intelligence planning and scheduling is a promising technology for enabling intelligent behavior in complex autonomous systems. To use planning technology, however, one has to create a knowledge base from which the input to the planner will be derived. This process requires advanced knowledge engineering tools, dedicated to the acquisition and formulation of the knowledge base, and its respective integration with planning algorithms that reason about the world to plan intelligently. In this chapter, we shortly review the existing knowledge engineering tools and methods that support the design of the problem and domain knowledge for AI planning and scheduling applications (AI P&S). We examine the state-of-the-art tools and methods of knowledge engineering for planning & scheduling (KEPS) in the context of an abstract design process for acquiring, formulating, and analyzing domain knowledge. Planning quality is associated with requirements knowledge (pre-design) which should match properties of plans (post-design). While examining the literature, we analyze the design phases that have not received much attention, and propose new approaches to that, based on theoretical analysis and also in practical experience in the implementation of the system itSIMPLE.