Coherent cooperation among communicating problem solvers
IEEE Transactions on Computers
Intention is choice with commitment
Artificial Intelligence
Extending a blackboard architecture for approximate processing
Real-Time Systems
Algorithms for Scheduling Imprecise Computations
Computer - Special issue on real-time systems
Open information systems semantics for distributed artificial intelligence
Artificial Intelligence
Social conceptions of knowledge and action: DAI foundations and open systems semantics
Artificial Intelligence
The Analysis of Quantitative Coordination Relationships
The Analysis of Quantitative Coordination Relationships
Solving time-dependent planning problems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
An approach to analyzing the need for meta-level communication
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Incomplete information and deception in multi-agent negotiation
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Multi-agent role allocation: issues, approaches, and multiple perspectives
Autonomous Agents and Multi-Agent Systems
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Coordinating teams in uncertain environments: a hybrid BDI-POMDP approach
ProMAS'04 Proceedings of the Second international conference on Programming Multi-Agent Systems
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Formal approaches to specifying how the mental state of an agent entails that it perform particular actions put the agent at the center of analysis. For some questions and purposes, it is more realistic and convenient for the center of analysis to be the task environment, domain, or society of which agents will be a part. This paper presents such a task environment-oriented modeling framework that can work hand-in-hand with more agent-centered approaches. Our approach features careful attention to the quantitative computational interrelationships between tasks, to what information is available (and when) to update an agent's mental state, and to the general structure of the task environment rather than single-instance examples. A task environment model can be used for both analysis and simulation; it avoids the methodological problems of relying solely on single-instance examples, and provides concrete, meaningful characterizations with which to state general theories. This paper will give an example of a model in the context of cooperative distributed problem solving, but our framework is used for analyzing centralized and parallel control as well.