Collaborative plans for complex group action
Artificial Intelligence
Effects of misconception on reciprocative agents
AGENTS '98 Proceedings of the second international conference on Autonomous agents
The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Socially conscious decision-making
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Intention Reconcilation in the Context of Teamwork: An Initial Empirical Investigation
CIA '99 Proceedings of the Third International Workshop on Cooperative Information Agents III
Intention Reconciliation by Collaborative Agents
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Advantages of a leveled commitment contracting protocol
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A reference model for designing effective reputation information systems
Journal of Information Science
Decision as choice of potential intentions
Web Intelligence and Agent Systems
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Rational, autonomous agents must be able to revise their commitments in the light of new opportunities. They must decide when to default on commitments to the group in order to commit to potentially more valuable outside offers. The SPIRE experimental system allows the study of intention reconciliation in team contexts. This paper presents a new framework for SPIRE that allows for mathematical specification and provides a basis for the study of learning. Analysis shows that a reactive policy can be expected to perform as well as more complex policies that look ahead. We present an algorithm for learning when to default on group commitments based solely on observed values of group-related tasks and discuss the applicability of this algorithm in settings where multiple agents may be learning.