- author = "Brian G. Woolley and Gilbert L. Peterson",
- title = "Genetic evolution of hierarchical behavior structures",
- booktitle = "GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation",
- year = "2007",
- editor = "Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener",
- volume = "2",
- isbn13 = "978-1-59593-697-4",
- pages = "1731--1738",
- address = "London",
-
URL = "
http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p1731.pdf",
-
DOI = "
doi:10.1145/1276958.1277296",
- publisher = "ACM Press",
- publisher_address = "New York, NY, USA",
- month = "7-11 " # jul,
- organisation = "ACM SIGEVO (formerly ISGEC)",
- keywords = "genetic algorithms, genetic programming, behaviour based robotics, evolutionary robotics, unified behaviour framework",
- abstract = "The development of coherent and dynamic behaviours for mobile robots is an exceedingly complex endeavour ruled by task objectives, environmental dynamics and the interactions within the behavior structure. This paper discusses the use of genetic programming techniques and the unified behaviour framework to develop effective control hierarchies using interchangeable behaviors and arbitration components. Given the number of possible variations provided by the framework, evolutionary programming is used to evolve the overall behaviour design. Competitive evolution of the behaviour population incrementally develops feasible solutions for the domain through competitive ranking. By developing and implementing many simple behaviours independently and then evolving a complex behaviour structure suited to the domain, this approach allows for the reuse of elemental behaviours and eases the complexity of development for a given domain. Additionally, this approach has the ability to locate a behaviour structure which a developer may not have previously considered, and whose ability exceeds expectations. The evolution of the behaviour structure is demonstrated using agents in the Robocode environment, with the evolved structures performing up to 122 percent better than one crafted by an expert.",
-
notes = "GECCO-2007 A joint meeting of the sixteenth
international conference on genetic algorithms
(ICGA-2007) and the twelfth annual genetic programming
conference (GP-2007).
ACM Order Number 910071",
