Analyzing sensor states and internal states in the tartarus problem with tree state machines

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5 Citations (Scopus)

Abstract

The Tartarus problem is a box pushing task in a grid world environment. It is one of difficult problems for purely reactive agents to solve, and thus a memory-based control architecture is required. This paper presents a novel control structure, called tree state machine, which has an evolving tree structure for sensorimotor mapping and also encodes internal states. As a result, the evolutionary computation on tree state machines can quantify internal states and sensor states needed for the problem. Tree state machines with a dynamic feature of sensor states are demonstrated and compared with finite state machines and GP-automata. It is shown that both sensor states and memory states are important factors to influence the behavior performance of an agent.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsXin Yao, John A. Bullinaria, Jonathan Rowe, Peter Tino, Ata Kaban, Edmund Burke, Jose A. Lozano, Jim Smith, Juan J. Merelo-Guervos, Hans-Paul Schwefel
PublisherSpringer Verlag
Pages551-560
Number of pages10
ISBN (Print)3540230920, 9783540230922
DOIs
Publication statusPublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3242
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Kim, D. E. (2004). Analyzing sensor states and internal states in the tartarus problem with tree state machines. In X. Yao, J. A. Bullinaria, J. Rowe, P. Tino, A. Kaban, E. Burke, J. A. Lozano, J. Smith, J. J. Merelo-Guervos, & H-P. Schwefel (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 551-560). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3242). Springer Verlag. https://doi.org/10.1007/978-3-540-30217-9_56