- author = "Jiming Liu and Shiwu Zhang",
- title = "Multi-phase Sumo Maneuver Learning",
- journal = "Robotica",
- volume = "22",
- number = "1",
- year = "2004",
- pages = "61--75",
- keywords = "genetic algorithms, genetic programming, Multi-phase genetic programming (MPGP), Autonomous robots, Sumo tasks, Maneuver learning, Evolutionary robotics",
-
DOI = "
doi:10.1017/S0263574703005356",
- bibsource = "DBLP, http://dblp.uni-trier.de",
- abstract = "In this paper, we demonstrate a multi-phase genetic programming (MPGP) approach to an autonomous robot learning task, where a sumo wrestling robot is required to execute specialised pushing manoeuvres in response to different opponents' postures. The sumo robot used has a very simple, minimalist hardware configuration. This example differs from the earlier studies in evolutionary robotics in that the former is carried out on-line during the performance of a robot, whereas the latter is concerned with the evolution of a controller in a simulated environment based on extended genetic algorithms. As illustrated in several sumo maneuver learning experiments, strategic manoeuvres with respect to some possible changes in the shape and size of an opponent can readily emerge from the on-line MPGP learning sessions.",
-
notes = "See also \cite{LIU:2004:IJPRAI}
Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong (P.R. of China).
Department of Precision Machinery & Precision Instrumentation, University of Science and Technology of China (P.R. of China)",
