NDLI: The effect of communication on the evolution of cooperative behavior in a multi-agent system

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The effect of communication on the evolution of cooperative behavior in a multi-agent system

Content Provider	ACM Digital Library
Author	Goings, Sherri Hiranuma, Naozumi Johnston, Emily P.M.
Abstract	A team of agents that cooperate to solve a problem together can handle many complex tasks that would not be possible without cooperation. While the benefit is clear, there are still many open questions in how best to achieve this cooperation. In this paper we focus on the role of communication in allowing agents to evolve effective cooperation for a prey capture task. Previous studies of this task have shown mixed results for the benefit of direct communication among predators, and we investigate potential explanations for these seemingly contradictory outcomes. We start by replicating the results of a study that found that agents with the ability to communicate actually performed worse than those without when each member of a team was evolved in a separate population [8]. The simulated world used for these experiments is very simple, and we hypothesize that communication would become beneficial in a similar but more complex environment. We test several methods of increasing the problem complexity, but find that at best communicating predators perform equally as well as those that cannot communicate. We thus propose that the representation may hinder the success of communication in this environment. The behavior of each predator is encoded in a neural network, and the networks with communication have 6 inputs as opposed to just 2 for the standard network, giving communicating networks more than twice as many links for which to evolve weights. Another study using a relatively similar environment but genetic programming as a representation finds that communication is clearly beneficial for prey capture [4]. We hypothesize that adding communication is less costly to these genetic programs as compared to the earlier neural networks and outline experiments to test this theory.
Starting Page	999
Ending Page	1006
Page Count	8
File Format	PDF
ISBN	9781450328814
DOI	10.1145/2598394.2605443
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-07-12
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Multiagent systems Simulation Neural networks Cooperation Genetic programming Evolutionary computation Communication
Content Type	Text
Resource Type	Article

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