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Allocation and planning with a collection of tasks and a group of agents isan important problem in multiagent systems. One commonly faced bottleneck isscalability, as in general the multiagent model increases exponentially in sizewith the number of agents. We consider the combination of random taskassignment and multiagent planning under multiple-objective constraints, andshow that this problem can be decentralised to individual agent-task models. Wepresent an algorithm of point-oriented Pareto computation, which checks whethera point corresponding to given cost and probability thresholds for our formalproblem is feasible or not. If the given point is infeasible, our algorithmfinds a Pareto-optimal point which is closest to the given point. We providethe first multi-objective model checking framework that simultaneously uses GPUand multi-core acceleration. Our framework manages CPU and GPU devices as aload balancing problem for parallel computation. Our experiments demonstratethat parallelisation achieves significant run time speed-up over sequentialcomputation.

Multi-Objective Task Assignment and Multiagent Planning with Hybrid GPU-CPU Acceleration