The interdisciplinary study of coordination
ACM Computing Surveys (CSUR)
Collaborative plans for complex group action
Artificial Intelligence
The impact of diversity on performance in multi-robot foraging
Proceedings of the third annual conference on Autonomous Agents
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Proceedings of the fifth international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement Learning in the Multi-Robot Domain
Autonomous Robots
Algorithm Selection using Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Selecting the Right Heuristic Algorithm: Runtime Performance Predictors
AI '96 Proceedings of the 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Behavioral diversity in learning robot teams
Behavioral diversity in learning robot teams
The Dynamic Selection of Coordination Mechanisms
Autonomous Agents and Multi-Agent Systems
Evolution of the GPGP/TÆMS Domain-Independent Coordination Framework
Autonomous Agents and Multi-Agent Systems
Towards flexible teamwork in behavior-based robots: extended abstract
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Machine learning for fast quadrupedal locomotion
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Robust agent teams via socially-attentive monitoring
Journal of Artificial Intelligence Research
The communicative multiagent team decision problem: analyzing teamwork theories and models
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Interference as a tool for designing and evaluating multi-robot controllers
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
A game theoretic approach to contracts in multiagent systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Socially intelligent reasoning for autonomous agents
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Applying Reinforcement Learning to Multi-robot Team Coordination
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Agent Decision Making for Dynamic Selection of Coordination Mechanisms
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Adaptive multi-robot coordination based on resource spending velocity
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Towards a framework for agent coordination and reorganization, AgentCoRe
COIN'07 Proceedings of the 2007 international conference on Coordination, organizations, institutions, and norms in agent systems III
Improving agent team performance through helper agents
CAVE'12 Proceedings of the First international conference on Cognitive Agents for Virtual Environments
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Many collaborative multi-robot application domains have limited areas of operation that cause spatial conflicts between robotic teammates. These spatial conflicts can cause the team's productivity to drop with the addition of robots. This phenomenon is impacted by the coordination methods used by the team-members, as different coordination methods yield radically different productivity results. However, selecting the best coordination method to be used by teammates is a formidable task. This paper presents techniques for creating adaptive coordination methods to address this challenge. We first present a combined coordination cost measure, CCC, to quantify the cost of group interactions. Our measure is useful for facilitating comparison between coordination methods, even when multiple cost factors are considered. We consistently find that as CCC values grow, group productivity falls. Using the CCC, we create adaptive coordination techniques that are able to dynamically adjust the efforts spent on coordination to match the number of perceived coordination conflicts in a group. We present two adaptation heuristics that are completely distributed and require no communication between robots. Using these heuristics, robots independently estimate their combined coordination cost (CCC), adjust their coordination methods to minimize it, and increase group productivity. We use simulated robots to perform thousands of experiment trials to demonstrate the efficacy of our approach. We show that using adaptive coordination methods create a statistically significant improvement in productivity over static methods, regardless of the group size.