Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
Online Multiagent Learning against Memory Bounded Adversaries
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Coordination and adaptation in impromptu teams
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
A decision-theoretic approach to coordinating multiagent interactions
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Learning against opponents with bounded memory
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
To teach or not to teach?: decision making under uncertainty in ad hoc teams
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Ad hoc teamwork for leading a flock
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Cooperating with a markovian ad hoc teammate
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Ad hoc coordination in multiagent systems with applications to human-machine interaction
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Teaching and leading an ad hoc teammate: Collaboration without pre-coordination
Artificial Intelligence
Multi-agent team formation: diversity beats strength?
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Hi-index | 0.00 |
The growing use of autonomous agents in practice may require agents to cooperate as a team in situations where they have limited prior knowledge about one another, cannot communicate directly, or do not share the same world models. These situations raise the need to design ad hoc team members, i.e., agents that will be able to cooperate without coordination in order to reach an optimal team behavior. This paper considers the problem of leading N-agent teams by an agent toward their optimal joint utility, where the agents compute their next actions based only on their most recent observations of their teammates' actions. We show that compared to previous results in two-agent teams, in larger teams the agent might not be able to lead the team to the action with maximal joint utility, thus its optimal strategy is to lead the team to the best possible reachable cycle of joint actions. We describe a graphical model of the problem and a polynomial time algorithm for solving it. We then consider other variations of the problem, including leading teams of agents where they base their actions on longer history of past observations, leading a team by more than one ad hoc agent, and leading a teammate while the ad hoc agent is uncertain of its behavior.