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A probabilistic movement model for shortest path formation in virtual ant-like agents

Chibaya, Colin and Bangay, Shaun 2007, A probabilistic movement model for shortest path formation in virtual ant-like agents, in SAICSIT '07 : Riding the wave of technology : Proceedings of the 2007 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries, Association for Computing Machinery, New York, N.Y., pp. 9-18, doi: 10.1145/1292491.1292493.

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Title A probabilistic movement model for shortest path formation in virtual ant-like agents
Author(s) Chibaya, Colin
Bangay, Shaun
Conference name South African institute of computer scientists and information technologists. Conference (2007 : Sunshine Coast, South Africa)
Conference location Sunshine Coast, South Africa
Conference dates 30 Sep.-3 Oct. 2007
Title of proceedings SAICSIT '07 : Riding the wave of technology : Proceedings of the 2007 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries
Editor(s) [unknown]
Publication date 2007
Conference series South African Institute for Computer Scientists and Information Technologists Conference
Start page 9
End page 18
Total pages 204
Publisher Association for Computing Machinery
Place of publication New York, N.Y.
Keyword(s) emergent behaviour
search and return pheromone
dissipation
directionality
speed
quality
recruitment
Summary We propose a probabilistic movement model for controlling ant-like agents foraging between two points. Such agents are all identical, simple, autonomous and can only communicate indirectly through the environment. These agents secrete two types of pheromone, one to mark trails towards the goal and another to mark trails back to the starting point. Three pheromone perception strategies are proposed (Strategy A, B and C). Agents that use strategy A perceive the desirability of a neighbouring location as the difference between levels of attractive and repulsive pheromone in that location. With strategy B, agents perceive the desirability of a location as the quotient of levels of attractive and repulsive pheromone. Agents using strategy C determine the product of the levels of attractive pheromone with the complement of levels of repulsive pheromone. We conduct experiments to confirm directionality as emergent property of trails formed by agents that use each strategy. In addition, we compare path formation speed and the quality of the formed path under changes in the environment. We also investigate each strategy's robustness in environments that contain obstacles. Finally, we investigate how adaptive each strategy is when obstacles are eventually removed from the scene and find that the best strategy of these three is strategy A. Such a strategy provides useful guidelines to researchers in further applications of swarm intelligence metaphors for complex problem solving.
ISBN 9781595937759
Language eng
DOI 10.1145/1292491.1292493
Field of Research 080102 Artificial Life
Socio Economic Objective 890299 Computer Software and Services not elsewhere classified
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2007, ACM
Persistent URL http://hdl.handle.net/10536/DRO/DU:30039207

Document type: Conference Paper
Collection: School of Information Technology
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