- author = "Thomas D. Haynes and Roger L. Wainwright",
- title = "A Simulation of Adaptive Agents in Hostile Environment",
- booktitle = "Proceedings of the 1995 ACM Symposium on Applied Computing",
- year = "1995",
- editor = "K. M. George and Janice H. Carroll and Ed Deaton and Dave Oppenheim and Jim Hightower",
- pages = "318--323",
- address = "Nashville, USA",
- publisher = "ACM Press",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-sac95.ps",
-
URL = "
http://citeseer.ist.psu.edu/2240.html",
-
DOI = "
doi:10.1145/315891.316007",
- size = "8 pages",
- abstract = "In this paper we use the genetic programming technique to evolve programs to control an autonomous agent capable of learning how to survive in a hostile environment. In order to facilitate this goal, agents are run through random environment configurations. Randomly generated programs, which control the interaction of the agent with its environment, are recombined to form better programs. Each generation of the population of agents is placed into the Simulator with the ultimate goal of producing an agent capable of surviving any environment. The environment that an agent is presented consists of other agents, mines, and energy. The goal of this research is to construct a program which when executed will allow an agent (or agents) to correctly sense, and mark, the presence of items (energy and mines) in any environment. The Simulator determines the raw fitness of each agent by interpreting the associated program. General programs are evolved to solve this problem. Different environmental setups are presented to show the generality of the solution. These environments include one agent in a fixed environment, one agent in a fluctuating environment, and multiple agents in a fluctuating environment cooperating together. The genetic programming technique was extremely successful. The average fitness per generation in all three environments tested showed steady improvement. Programs were successfully generated that enabled an agent to handle any possible environment.",
-
notes = "Agent has access to memory holding information on
locations it has already visited.
Agents are run through random environment configurations. Environment contains other agents, lethal mines and energy. Agents aims to sense and mark these. One example: multiple agents cooperating in a fluctating environment. GP generated an {"}agent to handle any possible enironment{"}.",
