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

Allocation and planning with a collection of tasks group agents is an important problem in multiagent systems. One commonly faced bottleneck scalability, as general the model increases exponentially size number agents. We consider combination random task assignment under multiple-objective constraints, show that this can be decentralised to individual agent-task models. present algorithm point-oriented Pareto computation, which checks whether point corresponding given cost probability thresholds for our formal feasible or not. If infeasible, finds Pareto-optimal closest point. provide first multi-objective checking framework simultaneously uses GPU multi-core acceleration. Our manages CPU devices load balancing parallel computation. experiments demonstrate parallelisation achieves significant run time speed-up over sequential