Learning Ant Foraging Behaviors

author = "Liviu A. Panait and Sean Luke",

title = "Learning Ant Foraging Behaviors",

booktitle = "Artificial Life {XI} Ninth International Conference on the Simulation and Synthesis of Living Systems",

year = "2004",

editor = "Jordan Pollack and Mark Bedau and Phil Husbands and Takashi Ikegami and Richard A. Watson",

pages = "575--580",

address = "Boston, Massachusetts",

month = "12-15 " # sep,

publisher = "The MIT Press",

keywords = "genetic algorithms, genetic programming",

ISBN = "0-262-66183-7",

URL = "

http://www.cs.gmu.edu/~sean/papers/panait04learning.pdf",

URL = "

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6278777",

DOI = "

doi:10.7551/mitpress/1429.003.0097",

size = "6 pages",

abstract = "Insects are good at cooperatively solving many complex tasks. For example, foraging for food far away from a nest can be solved through relatively simple behaviours in combination with pheromones. As task complexity increases, however, it may become difficult to find individual agent rules which yield a desired emergent cooperative behavior, or to know if any such rules exist at all. For such tasks, machine learning techniques like evolutionary computation (EC) may prove a valuable approach to searching the space of possible rule combinations. This paper presents an application of genetic programming to search for foraging behaviours. The learnt foraging behaviours use only pheromone information to find the path to the nest and to the food source.",

notes = "ALIFE9",