Analyzing Sensor States and Internal States in the Tartarus Problem with Tree State Machines

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@InProceedings{Kim:PPSN:2004,

• author = "DaeEun Kim",
• title = "Analyzing Sensor States and Internal States in the Tartarus Problem with Tree State Machines",
• booktitle = "Parallel Problem Solving from Nature - PPSN VIII",
• year = "2004",
• editor = "Xin Yao and Edmund Burke and Jose A. Lozano and Jim Smith and Juan J. Merelo-Guerv\'os and John A. Bullinaria and Jonathan Rowe and Peter Ti\v{n}o Ata Kab\'an and Hans-Paul Schwefel",
• volume = "3242",
• pages = "551--560",
• series = "LNCS",
• address = "Birmingham, UK",
• publisher_address = "Berlin",
• month = "18-22 " # sep,
• publisher = "Springer-Verlag",
• ISBN = "3-540-23092-0",
• keywords = "genetic algorithms, genetic programming",
• URL = "https://rdcu.be/dc0kn",
• DOI = "doi:10.1007/b100601",
• DOI = "doi:10.1007/978-3-540-30217-9_56",
• 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 behaviour performance of an agent.",
• notes = "PPSN-VIII",

}

Genetic Programming entries for DaeEun Kim

Citations