Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
Negotiating task decomposition and allocation using partial global planning
Distributed Artificial Intelligence (Vol. 2)
Constraint satisfaction techniques for spatial planning
Intelligent CAD systems III
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
ACM Transactions on Information Systems (TOIS) - Special issue: selected papers from the conference on office information systems
Backtracking techniques for the job shop scheduling constraint satisfaction problem
Artificial Intelligence - Special volume on planning and scheduling
Resource Configuration and Allocation-A Case Study of Constrained Heuristic Search
IEEE Expert: Intelligent Systems and Their Applications
A Constraint-Based Approach to Assigning System Components to Tasks
IEA/AIE '98 Proceedings of the 11th international conference on Industrial and engineering applications of artificial intelligence and expert systems: methodology and tools in knowledge-based systems
Dynamic Flexible Constraint Satisfaction
Applied Intelligence
Coordinating product, process, and supply chain decisions: A constraint satisfaction approach
Engineering Applications of Artificial Intelligence
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In multi-component systems, individual components must be assigned to thetasks that they are to perform. In many applications, there are severalpossible task decompositions that could be used to achieve the task, andthere are limited resources available throughout the system. We present atechnique for making task assignments under these conditions. Constraintsatisfaction is used to assign components to particular tasks. Heuristics suggest a task decomposition forwhich an assignment can be found efficiently. We have applied our techniqueto the problem of task assignment in systems of underwater robots andinstrument platforms working together to collect data in the ocean.