This research presents a unified approach to representing and solving the multiagent task assignment problem for complex problem domains using ideas central to multiagent task allocation, project scheduling, constraint satisfaction, and coalition formation, forming the basis of the constrained multiagent task scheduling (CMTS) problem. The CMTS descriptor represents a wide range of classical and modern problems, such as job shop scheduling, the traveling salesman problem, vehicle routing, and cooperative multi-object tracking. Problems using the CMTS representation are solvable by a suite of algorithms ranging from simple random scheduling to state-of-the-art biologically inspired approaches incorporating evolutionary algorithms, dynamic coalition formation, auctioning, and behavior-based robotics to highlight different solution generation strategies.

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