Elementary decision theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Strategic negotiation in multiagent environments
Strategic negotiation in multiagent environments
Anticipating where to look: predicting the movements of mobile agents in complex terrain
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Exploiting focal points among alternative solutions: Two approaches
Annals of Mathematics and Artificial Intelligence
An environment for distributed collaboration among humans and software agents
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Coordination without communication: the case of the flocking problem
Discrete Applied Mathematics - Fun with algorithms 2 (FUN 2001)
Social coordination without communication in multi-agent territory exploration tasks
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Human-robot interaction: a survey
Foundations and Trends in Human-Computer Interaction
The DEFACTO system: training tool for incident commanders
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
An Introduction to MultiAgent Systems
An Introduction to MultiAgent Systems
Agent-human coordination with communication costs under uncertainty
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
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We consider an automated agent that needs to coordinate with a human partner when communication between them is not possible or is undesirable (tacit coordination games). Specifically, we examine situations where an agent and human attempt to coordinate their choices among several alternatives with equivalent utilities. We use machine learning algorithms to help the agent predict human choices in these tacit coordination domains. Experiments have shown that humans are often able to coordinate with one another in communication-free games, by using focal points, "prominent" solutions to coordination problems. We integrate focal point rules into the machine learning process, by transforming raw domain data into a new hypothesis space. We present extensive empirical results from three different tacit coordination domains. The Focal Point Learning approach results in classifiers with a 40---80% higher correct classification rate, and shorter training time, than when using regular classifiers, and a 35% higher correct classification rate than classical focal point techniques without learning. In addition, the integration of focal points into learning algorithms results in agents that are more robust to changes in the environment. We also present several results describing various biases that might arise in Focal Point based coordination.