SOAR: an architecture for general intelligence
Artificial Intelligence
Distributed problem solving techniques: A survey
IEEE Transactions on Systems, Man and Cybernetics
Unified theories of cognition
A computational approach to organizations and organizing
Simulating organizations
Team soar: a model for team decision making
Simulating organizations
The potential for the evolution of co-operation among Web agents
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Building Dynamic Agent Organizations in Cyberspace
IEEE Internet Computing
Personality Parameters and Programs
Creating Personalities for Synthetic Actors, Towards Autonomous Personality Agents
A Game-Theoretic Approach to the Socialization of Utility-Based Agents
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Team-Soar: a computational model for multilevel decision making
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Within an organizational context, an agent type reflects the behavioral tendency that a member might employ across tasks. Until now, however, the impacts of agent types on team performance are not well understood. To address this issue, this study examines the relationships of agent activeness and cooperativeness with team decision efficiency at different degrees of information redundancy by using a team model consisting of four AI agents. This study presents the team model called ''Team-Soar'' and describes how the model implements agent activeness at two levels (active and passive) and agent cooperativeness at three levels (cooperative, neutral, and selfish). Then a computational simulation experiment is described. Results of the simulation indicate that the impacts of the agent type depend on the amount of information to be processed and active style boosts the effects of agent cooperativeness on team efficiency. The results also indicate that active agents do not always contribute team efficiency more than passive agents.