1,585 research outputs found
Learning in Real-Time Search: A Unifying Framework
Real-time search methods are suited for tasks in which the agent is
interacting with an initially unknown environment in real time. In such
simultaneous planning and learning problems, the agent has to select its
actions in a limited amount of time, while sensing only a local part of the
environment centered at the agents current location. Real-time heuristic search
agents select actions using a limited lookahead search and evaluating the
frontier states with a heuristic function. Over repeated experiences, they
refine heuristic values of states to avoid infinite loops and to converge to
better solutions. The wide spread of such settings in autonomous software and
hardware agents has led to an explosion of real-time search algorithms over the
last two decades. Not only is a potential user confronted with a hodgepodge of
algorithms, but he also faces the choice of control parameters they use. In
this paper we address both problems. The first contribution is an introduction
of a simple three-parameter framework (named LRTS) which extracts the core
ideas behind many existing algorithms. We then prove that LRTA*, epsilon-LRTA*,
SLA*, and gamma-Trap algorithms are special cases of our framework. Thus, they
are unified and extended with additional features. Second, we prove
completeness and convergence of any algorithm covered by the LRTS framework.
Third, we prove several upper-bounds relating the control parameters and
solution quality. Finally, we analyze the influence of the three control
parameters empirically in the realistic scalable domains of real-time
navigation on initially unknown maps from a commercial role-playing game as
well as routing in ad hoc sensor networks
Augmented neural networks and problem-structure based heuristics for the bin-packing problem
In this paper, we apply the Augmented-neural-networks (AugNN) approach for solving the classical bin-packing problem (BPP). AugNN is a metaheuristic that combines a priority- rule heuristic with the iterative search approach of neural networks to generate good solutions fast. This is the first time this approach has been applied to the BPP. We also propose a decomposition approach for solving harder BPP, in which sub problems are solved using a combination of AugNN approach and heuristics that exploit the problem structure. We discuss the characteristics of problems on which such problem-structure based heuristics could be applied. We empirically show the effectiveness of the AugNN and the decomposition approach on many benchmark problems in the literature. For the 1210 benchmark problems tested, 917 problems were solved to optimality and the average gap between the obtained solution and the upper bound for all the problems was reduced to under 0.66% and computation time averaged below 33 seconds per problem. We also discuss the computational complexity of our approach
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