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Extending and Tuning Heuristics for a Partial Order Causal Link Planner

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Mining Intelligence and Knowledge Exploration (MIKE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9468))

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Abstract

Recent literature reveals that different heuristic functions perform well in different domains due to the varying nature of planning problems. This nature is characterized by the degree of interaction between subgoals and actions. We take the approach of learning the characteristics of different domains in a supervised manner. In this paper, we employ a machine learning approach to combine different, possibly inadmissible, heuristic functions in a domain dependent manner. With the renewed interest in Partial Order Causal Link (POCL) planning we also extend the heuristic functions derived from state space approaches to POCL planning. We use Artificial Neural Network (ANN) for combining these heuristics. The goal is to allow a planner to learn the parameters to combine heuristic functions in a given domain over time in a supervised manner. Our experiments demonstrate that one can discover combinations that yield better heuristic functions in different planning domains.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Madras, Chennai, India

    Shashank Shekhar & Deepak Khemani

Authors
  1. Shashank Shekhar
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  2. Deepak Khemani
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Correspondence to Shashank Shekhar .

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Editors and Affiliations

  1. Norwegian Univ. of Science & Technology, Trondheim, Norway

    Rajendra Prasath

  2. Intl Inst of Info Tech Hyderabad, Hyderabad, India

    Anil Kumar Vuppala

  3. V.H.N.S.N.College (Autonomous), Virudhunagar, Tamil Nadu, India

    T. Kathirvalavakumar

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Shekhar, S., Khemani, D. (2015). Extending and Tuning Heuristics for a Partial Order Causal Link Planner. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_9

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  • DOI: https://doi.org/10.1007/978-3-319-26832-3_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26831-6

  • Online ISBN: 978-3-319-26832-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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