Extending and Tuning Heuristics for a Partial Order Causal Link Planner

  • Shashank Shekhar
  • Deepak Khemani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9468)


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.


Learning ANN POCL planning Graphplan Heuristic Admissibility PE-ANN VHPOP Satisficing planning 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology MadrasChennaiIndia

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