Foundations for Generalized Planning in Unbounded Stochastic Domains

Vaishak Belle, Hector J. Levesque

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Generalized plans, such as plans with loops, are widely used in AI. Among other things, they are straightforward to execute, they allow action repetition, and they solve multiple problem instances. However, the correctness of such plans is non-trivial to define, making it difficult to provide a clear specification of what we should be looking for. Proposals in the literature, such as strong planning, are universally adopted by the community, but were initially formulated for finite state systems. There is yet to emerge a study on the sensitivity of such correctness notions to the structural assumptions of the underlying plan framework. In this paper, we are interested in the applicability and correctness of generalized plans in domains that are possibly unbounded, and/or stochastic, and/or continuous. To that end, we introduce a generic controller framework to capture different types of planning domains. Using this framework, we then study a number of termination and goal satisfaction criteria from first principles, relate them to existing proposals, and show plans that meet these criteria in the different types of domains.
Original languageEnglish
Title of host publicationPrinciples of Knowledge Representation and Reasoning: Proceedings of the Fifteenth International Conference, KR 2016, Cape Town, South Africa, April 25-29, 2016.
PublisherAAAI Press
Number of pages10
Publication statusPublished - 2016
Event15th International Conference on Principles of Knowledge Representation and Reasoning - Cape Town, South Africa
Duration: 25 Apr 201629 Apr 2016


Conference15th International Conference on Principles of Knowledge Representation and Reasoning
Abbreviated titleKR 2016
Country/TerritorySouth Africa
CityCape Town
Internet address


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