The Influence of Collective Working Memory Strategies on Agent Teams

dc.contributor.advisorReggia, James A.en_US
dc.contributor.authorWinder, Ransom Kershawen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.description.abstractPast self-organizing models of collectively moving "particles" (simulated bird flocks, fish schools, etc.) typically have been based on purely reflexive agents that have no significant memory of past movements or environmental obstacles. These agent collectives usually operate in abstract environments, but as these domains take on a greater realism, the collective requires behaviors use not only presently observed stimuli but also remembered information. It is hypothesized that the addition of a limited working memory of the environment, distributed among the collective's individuals can improve efficiency in performing tasks. This is first approached in a more traditional particle system in an abstract environment. Then it is explored for a single agent, and finally a team of agents, operating in a simulated 3-dimensional environment of greater realism. In the abstract environment, a limited distributed working memory produced a significant improvement in travel between locations, in some cases improving performance over time, while in others surprisingly achieving an immediate benefit from the influence of memory. When strategies for accumulating and manipulating memory were subsequently explored for a more realistic single agent in the 3-dimensional environment, if the agent kept a local or a cumulative working memory, its performance improved on different tasks, both when navigating nearby obstacles and, in the case of cumulative memory, when covering previously traversed terrain. When investigating a team of these agents engaged in a pursuit scenario, it was determined that a communicating and coordinating team still benefited from a working memory of the environment distributed among the agents, even with limited memory capacity. This demonstrates that a limited distributed working memory in a multi-agent system improves performance on tasks in domains of increasing complexity. This is true even though individual agents know only a fraction of the collective's entire memory, using this partial memory and interactions with others in the team to perform tasks. These results may prove useful in improving existing methodologies for control of collective movements for robotic teams, computer graphics, particle swarm optimization, and computer games, and in interpreting future experimental research on group movements in biological populations.en_US
dc.format.extent7643609 bytes
dc.subject.pqcontrolledComputer Scienceen_US
dc.subject.pqcontrolledArtificial Intelligenceen_US
dc.subject.pqcontrolledComputer Scienceen_US
dc.subject.pquncontrolledswarm intelligenceen_US
dc.subject.pquncontrolledneural networksen_US
dc.subject.pquncontrolledagent teamsen_US
dc.subject.pquncontrolledworking memoryen_US
dc.subject.pquncontrolledpursuit domainen_US
dc.subject.pquncontrolledbiologically-inspired computingen_US
dc.titleThe Influence of Collective Working Memory Strategies on Agent Teamsen_US


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