Controlling the learning process of real-time heuristic search

Masashi Shimbo*, Toru Ishida

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

68 Citations (Scopus)


Real-time search provides an attractive framework for intelligent autonomous agents, as it allows us to model an agent's ability to improve its performance through experience. However, the behavior of real-time search agents is far from rational during the learning (convergence) process, in that they fail to balance the efforts to achieve a short-term goal (i.e., to safely arrive at a goal state in the present problem solving trial) and a long-term goal (to find better solutions through repeated trials). As a remedy, we introduce two techniques for controlling the amount of exploration, both overall and per trial. The weighted real-time search reduces the overall amount of exploration and accelerates convergence. It sacrifices admissibility but provides a nontrivial bound on the converged solution cost. The real-time search with upper bounds insures solution quality in each trial when the state space is undirected. These techniques result in a convergence process more stable compared with that of the Learning Real-Time A* algorithm.

Original languageEnglish
Pages (from-to)1-41
Number of pages41
JournalArtificial Intelligence
Issue number1
Publication statusPublished - 2003 May
Externally publishedYes


  • Adaptive learning
  • Convergence process
  • Rational agent
  • Real-time heuristic search
  • Resource-boundedness

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence


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