The complexity of Markov decision processes
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Timespace in the workplace: dealing with interruptions
CHI '95 Conference Companion on Human Factors in Computing Systems
Collaborative plans for complex group action
Artificial Intelligence
Communication decisions in multi-agent cooperation: model and experiments
Proceedings of the fifth international conference on Autonomous agents
A writer's collaborative assistant
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Multiagent teamwork: analyzing the optimality and complexity of key theories and models
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Self-adaptive multimodal-interruption interfaces
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Transition-independent decentralized markov decision processes
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A prototype infrastructure for distributed robot-agent-person teams
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The complexity of multiagent systems: the price of silence
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Learning and reasoning about interruption
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Decentralized Markov Decision Processes with Event-Driven Interactions
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Reasoning about joint beliefs for execution-time communication decisions
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A polynomial algorithm for decentralized Markov decision processes with temporal constraints
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Leveraging characteristics of task structure to predict the cost of interruption
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Exploiting locality of interaction in factored Dec-POMDPs
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Human-Computer Interaction
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Dynamic programming for partially observable stochastic games
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
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The communicative multiagent team decision problem: analyzing teamwork theories and models
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Communication-based decomposition mechanisms for decentralized MDPs
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Optimal and approximate Q-value functions for decentralized POMDPs
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Memory-bounded dynamic programming for DEC-POMDPs
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Offline Planning for Communication by Exploiting Structured Interactions in Decentralized MDPs
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Modeling User Perception of Interaction Opportunities for Effective Teamwork
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Decentralized MDPs with sparse interactions
Artificial Intelligence
Human-agent teamwork in dynamic environments
Computers in Human Behavior
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Coordinate: probabilistic forecasting of presence and availability
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
A multi-agent systems "turing challenge"
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Information sharing for care coordination
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This paper studies information exchange in collaborative group activities involving mixed networks of people and computer agents. It introduces the concept of ''nearly decomposable'' decision-making problems to address the complexity of information exchange decisions in such multi-agent settings. This class of decision-making problems arise in settings which have an action structure that requires agents to reason about only a subset of their partners@? actions - but otherwise allows them to act independently. The paper presents a formal model of nearly decomposable decision-making problems, NED-MDPs, and defines an approximation algorithm, NED-DECOP that computes efficient information exchange strategies. The paper shows that NED-DECOP is more efficient than prior collaborative planning algorithms for this class of problem. It presents an empirical study of the information exchange decisions made by the algorithm that investigates the extent to which people accept interruption requests from a computer agent. The context for the study is a game in which the agent can ask people for information that may benefit its individual performance and thus the group@?s collaboration. This study revealed the key factors affecting people@?s perception of the benefit of interruptions in this setting. The paper also describes the use of machine learning to predict the situations in which people deviate from the strategies generated by the algorithm, using a combination of domain features and features informed by the algorithm. The methodology followed in this work could form the basis for designing agents that effectively exchange information in collaborations with people.