Decision theory in expert systems and artificial intelligence
International Journal of Approximate Reasoning
A comparative analysis of partial order planning and task reduction planning
ACM SIGART Bulletin
Collaborative plans for complex group action
Artificial Intelligence
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
User modeling in the design of interactive interface agents
UM '99 Proceedings of the seventh international conference on User modeling
Machine Learning
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
Models of attention in computing and communication: from principles to applications
Communications of the ACM
SHOP: Simple Hierarchical Ordered Planner
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Resource-aware exploration of the emergent dynamics of simulated systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Evolution of the GPGP/TÆMS Domain-Independent Coordination Framework
Autonomous Agents and Multi-Agent Systems
Bridging the gap between planning and scheduling
The Knowledge Engineering Review
Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Solving Distributed Constraint Optimization Problems Using Cooperative Mediation
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Agent interaction in distributed POMDPs and its implications on complexity
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Sharing experiences to learn user characteristics in dynamic environments with sparse data
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Estimating information value in collaborative multi-agent planning systems
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Distributed management of flexible times schedules
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Optimal multi-agent scheduling with constraint programming
IAAI'07 Proceedings of the 19th national conference on Innovative applications of artificial intelligence - Volume 2
Journal of Artificial Intelligence Research
Towards adjustable autonomy for the real world
Journal of Artificial Intelligence Research
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
On modeling multiagent task scheduling as a distributed constraint optimization problem
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Determining the value of information for collaborative multi-agent planning
Autonomous Agents and Multi-Agent Systems
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In this paper we investigate methods for measuring the expected utility from communicating information in multi-agent planning and scheduling problems. We consider an environment where human teammates can potentially add information to relax constraint information. As these problems are NP-complete, no polynomial algorithms exist for evaluating the impact of either adding or relaxing a certain constraint will have on the global problem. We present a general approach based on a notion we introduce called problem tightness. Distributed agents use this notion to identify those problems which are not overly constrained and, therefore, will not benefit from additional information that would relax those constraints. Finally, agents apply traditional machine learning methods based on their specific local problem attributes to attempt to identify which of the constrained problems will most benefit from added information. We evaluated this approach within a distributed c-TAEMS scheduling domain and found that this approach was effective overall.