A hierarchical goal-based formalism and algorithm for single-agent planning

  • Authors:

  • Vikas Shivashankar;Ugur Kuter;Dana Nau;Ron Alford

  • Affiliations:

  • University of Maryland, College Park, Maryland;Smart Information Flow Technologies, Minneapolis, Minnesota;University of Maryland, College Park, Maryland;University of Maryland, College Park, Maryland

  • Venue:
  • Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
  • Year:
  • 2012

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Abstract

Plan generation is important in a number of agent applications, but such applications generally require elaborate domain models that include not only the definitions of the actions that an agent can perform in a given domain, but also information about the most effective ways to generate plans for the agent in that domain. Such models typically take a large amount of human effort to create. To alleviate this problem, we have developed a hierarchical goal-based planning formalism and a planning algorithm, GDP (Goal-Decomposition Planner), that combines some aspects of both HTN planning and domain-independent planning. For example, it allows the planning agent to use domain-independent heuristic functions to guide the application of both methods and actions. This paper describes the formalism, planning algorithm, correctness theorems, and the results of a large experimental study. The experiments show that our planning algorithm works as well as the well-known SHOP2 HTN planner, using domain models only about half the size of SHOP2's.