Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Artificial Intelligence - Special issue on knowledge representation
Semantics for hierarchical task-network planning
Semantics for hierarchical task-network planning
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Communications of the ACM
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Constraint-Based Method for Project Scheduling with Time Windows
Journal of Heuristics
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms
IEEE Transactions on Knowledge and Data Engineering
Task Allocation in the RoboCup Rescue Simulation Domain: A Short Note
RoboCup 2001: Robot Soccer World Cup V
Algorithms for a temporal decoupling problem in multi-agent planning
Eighteenth national conference on Artificial intelligence
Gaining efficiency and flexibility in the simple temporal problem
TIME '96 Proceedings of the 3rd Workshop on Temporal Representation and Reasoning (TIME'96)
Constraint Processing
On the intersection of AI and OR
The Knowledge Engineering Review
The Knowledge Engineering Review
Bridging the gap between planning and scheduling
The Knowledge Engineering Review
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
A distributed framework for solving the Multiagent Plan Coordination Problem
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Robust Controllability of Temporal Constraint Networks under Uncertainty
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Artificial Intelligence in Medicine
Asynchronous Forward-checking for DisCSPs
Constraints
Distributed management of flexible times schedules
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Evaluating hybrid constraint tightening for scheduling agents
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Temporal dynamic controllability revisited
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Development of iterative real-time scheduler to planner feedback
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Abstract reasoning for planning and coordination
Journal of Artificial Intelligence Research
Path consistency on triangulated constraint graphs
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Dynamic control of plans with temporal uncertainty
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
A scalable method for multiagent constraint optimization
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Planning with sharable resource constraints
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Asynchronous aggregation and consistency in distributed constraint satisfaction
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Adopt: asynchronous distributed constraint optimization with quality guarantees
Artificial Intelligence - Special issue: Distributed constraint satisfaction
TIME '09 Proceedings of the 2009 16th International Symposium on Temporal Representation and Reasoning
Introduction to planning in multiagent systems
Multiagent and Grid Systems - Planning in multiagent systems
Optimal temporal decoupling in multiagent systems
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Distributed coordination of mobile agent teams: the advantage of planning ahead
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
An implementation of the contract net protocol based on marginal cost calculations
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
BnB-ADOPT: an asynchronous branch-and-bound DCOP algorithm
Journal of Artificial Intelligence Research
Distributed algorithms for solving the multiagent temporal decoupling problem
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
The complexity of decentralized control of Markov decision processes
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Drake: an efficient executive for temporal plans with choice
Journal of Artificial Intelligence Research
Distributed approaches for solving constraint-based multiagent scheduling problems
Distributed approaches for solving constraint-based multiagent scheduling problems
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This research focuses on building foundational algorithms for scheduling agents that assist people in managing their activities in environments where tempo and complex activity interdependencies outstrip people's cognitive capacity. We address the critical challenge of reasoning over individuals' interacting schedules to efficiently answer queries about how to meet scheduling goals while respecting individual privacy and autonomy to the extent possible. We formally define the Multiagent Simple Temporal Problem for naturally capturing and reasoning over the distributed but interconnected scheduling problems of multiple individuals. Our hypothesis is that combining bottom-up and top-down approaches will lead to effective solution techniques. In our bottom-up phase, an agent externalizes constraints that compactly summarize how its local subproblem affects other agents' subproblems, whereas in our top-down phase an agent proactively constructs and internalizes new local constraints that decouple its subproblem from others'. We confirm this hypothesis by devising distributed algorithms that calculate summaries of the joint solution space for multiagent scheduling problems, without centralizing or otherwise redistributing the problems. The distributed algorithms permit concurrent execution to achieve significant speedup over the current art and also increase the level of privacy and independence in individual agent reasoning. These algorithms are most advantageous for problems where interactions between the agents are sparse compared to the complexity of agents' individual problems.