Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Agent-organized networks for dynamic team formation
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Making Allocations Collectively: Iterative Group Decision Making under Uncertainty
MATES '08 Proceedings of the 6th German conference on Multiagent System Technologies
Diversity of ability and cognitive style for group decision processes
Information Sciences: an International Journal
Hybrid BDI-POMDP framework for multiagent teaming
Journal of Artificial Intelligence Research
Consultation algorithm for computer shogi: move decisions by majority
CG'10 Proceedings of the 7th international conference on Computers and games
The Power of Diversity over Large Solution Spaces
Management Science
Computational voting theory: game-theoretic and combinatorial aspects
Computational voting theory: game-theoretic and combinatorial aspects
Leading ad hoc agents in joint action settings with multiple teammates
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Modeling and learning synergy for team formation with heterogeneous agents
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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Team formation is a critical step in deploying a multi-agent team. In some scenarios, agents coordinate by voting continuously. When forming such teams, should we focus on the diversity of the team or on the strength of each member? Can a team of diverse (and weak) agents outperform a uniform team of strong agents? We propose a new model to address these questions. Our key contributions include: (i) we show that a diverse team can overcome a uniform team and we give the necessary conditions for it to happen; (ii) we present optimal voting rules for a diverse team; (iii) we perform synthetic experiments that demonstrate that both diversity and strength contribute to the performance of a team; (iv) we show experiments that demonstrate the usefulness of our model in one of the most difficult challenges for Artificial Intelligence: Computer Go.