Abstraction through clustering: complexity reduction in automated planning domains

L. Dicken, P. Gregory, J. Levine

Research output: Contribution to journalArticle

Abstract

Automated planning is a very active area of research within artificial intelligence. Broadly this discipline deals with the methods by which an agent can independently determine the sequence of actions required to successfully achieve a set of objectives. In this paper, we will present work outlining a new approach to planning based on clustering techniques, in order to group states of the world together and use the fundamental structure of the world to lift out more abstract representations. We will show that this approach can limit the combinatorial explosion of a typical planning problem in a way that is much more intuitive and reusable than has previously been possible, and outline ways that this approach can be developed further.
Original languageEnglish
Pages (from-to)123 - 137
Number of pages15
JournalInternational Journal of Data Mining, Modelling and Management
Volume4
Issue number2
DOIs
Publication statusPublished - 2012

Keywords

  • clustering
  • automated planning
  • complexity reduction
  • planning domains
  • artificial intelligence
  • data modelling
  • data mining

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