Controlling cooperative problem solving in industrial multi-agent systems using joint intentions
Artificial Intelligence
Toward Team-Oriented Programming
ATAL '99 6th International Workshop on Intelligent Agents VI, Agent Theories, Architectures, and Languages (ATAL),
Adaptive Agent Integration Architectures for Heterogeneous Team Members
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Journal of Artificial Intelligence Research
High-Level Behavior Programming
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Towards flexible teamwork in behavior-based robots: extended abstract
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Flexible teamwork in behavior-based robots
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
An integrated development environment and architecture for soar-based agents
IAAI'07 Proceedings of the 19th national conference on Innovative applications of artificial intelligence - Volume 2
A model-based executive for commanding robot teams
ProMAS'05 Proceedings of the Third international conference on Programming Multi-Agent Systems
Learning from demonstration with swarm hierarchies
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Curing robot autism: a challenge
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Research in multi-agent systems has led to the development of many multi-agent control architectures. However, we believe that there is currently no known optimal structure for multi-agent control since the effectiveness of any particular architecture varies depending on the domain of the problem. Therefore, deployment of multi-agent teams would be significantly sped up by a development and deployment environment which would allow designers to easily modify the architecture. In this paper, we present a flexible team-oriented programming and execution architecture, MONAD, which integrates hierarchical behavior-based control, multi-agent coordination mechanisms, and agent-task allocation services. MONAD uses a novel scripting language that allows designers to easily modify the team structure, behavior hierarchy, applicability conditions, and arbitration methods, in pursuit of the best solution for a particular problem. We have evaluated the MONAD architecture within a well-accepted adversarial game environment, GameBots, to enable qualitative comparison of different control techniques. In this environment, we were able to rapidly design and test several teams of agents who used role, preference, or a combination of role and preference arbitration and observed that these different teams varied in their performance characteristics.