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