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. © Springer-Verlag 2004.
CITATION STYLE
Kim, D. E. (2004). Analyzing sensor states and internal states in the tartarus problem with tree state machines. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3242, 551–560. https://doi.org/10.1007/978-3-540-30217-9_56
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