A Framework for Influencing Massive Virtual Organizations

This work presents a framework by which a massive multiagent organization can be controlled and modified without resorting to micromanagement and without needing advanced knowledge of potentially complex organizations. In addition to their designated duties, agents in the proposed framework perform some method of determining optimal traits such as configurations, plans, knowledge bases and so forth. Traits follow survival of the fittest rules in which more successful traits overpower less successful ones. Subproblem partitions develop emergently as successful solutions are disseminated to and aggregated by unsuccessful agents. Provisions are provided to allow the administrator to guide the search process by injecting solutions known to work for a particular agent. The performance of the framework is evaluated via comparison to individual state-space search.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Campus Only Dissertation

Discipline thesis:degree_discipline

Computer Science

Year

2011

Author dc:creator

• McLaughlan, Brian Paul

Contributors dc:contributor

• Hexmoor, Henry

• Distributed search
• Guided search
• Human computer interaction
• Multiagent systems

Repository record dc:identifier

https://opensiuc.lib.siu.edu/dissertations/395

OAI identifier oai:identifier

oai:opensiuc.lib.siu.edu:dissertations-1395