- author = "Kohsuke Yanai and Hitoshi Iba",
- title = "Multi-agent Robot Learning by Means of Genetic Programming : Solving an Escape Problem",
- booktitle = "Evolvable Systems: From Biology to Hardware: 4th International Conference, ICES 2001",
- year = "2001",
- editor = "Yong Liu and Kiyoshi Tanaka and Masaya Iwata and Tetsuya Higuchi and Moritoshi Yasunaga",
- volume = "2210",
- series = "LNCS",
- pages = "192--203",
- address = "Tokyo, Japan",
- month = "3-5 " # oct,
- publisher = "Springer-Verlag",
- keywords = "genetic algorithms, genetic programming, bloat",
- ISBN = "3-540-42671-X",
- ISSN = "0302-9743",
-
DOI = "
doi:10.1007/3-540-45443-8_17",
- abstract = "We present the emergence of the cooperative behaviour for multiple robot agents by means of Genetic Programming (GP). For this purpose, we use several extended mechanisms of GP, i.e., (1) a co-evolutionary breeding strategy, (2) a controlling strategy of introns, which are non-executed code segments dependent upon the situation, and (3) a subroutine discovery technique. Our experimental domain is an escape problem. We have chosen the actual experimental settings so as to be close to a real world as much as possible. The validness of our approach is discussed with comparative experiments using other methods, i.e., Q-learning and Neural networks, which shows the superiority of GP-based multi-agent learning.",
-
notes = "CODEN = LNCSD9
Subroutine discovery, ADF, placed in competitive shared library. Escape problem turns out to be three Khepara mini-robots {"}pushing{"} all 3 buttons before going to exit. Buttons, exit etc all colour coded. GP Evolved in simulation but works on real robots.
Second problem simplified so can try Q-learning on it. \cite{Iba:1998:ISJ}.",
