@inbook{aa71cf22ae6c4cbba4100574e1328f6d,
title = "Analyzing sensor states and internal states in the tartarus problem with tree state machines",
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.",
author = "Kim, {Dae Eun}",
year = "2004",
doi = "10.1007/978-3-540-30217-9_56",
language = "English",
isbn = "3540230920",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "551--560",
editor = "Xin Yao and Bullinaria, {John A.} and Jonathan Rowe and Peter Tino and Ata Kaban and Edmund Burke and Lozano, {Jose A.} and Jim Smith and Merelo-Guervos, {Juan J.} and Hans-Paul Schwefel",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
}