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On Backtracking in Real-time Heuristic Search
Real-time heuristic search algorithms are suitable for situated agents t...
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Avoiding and Escaping Depressions in Real-Time Heuristic Search
Heuristics used for solving hard real-time search problems have regions ...
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Mean-based Heuristic Search for Real-Time Planning
In this paper, we introduce a new heuristic search algorithm based on me...
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Case-Based Subgoaling in Real-Time Heuristic Search for Video Game Pathfinding
Real-time heuristic search algorithms satisfy a constant bound on the am...
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Influence-Optimistic Local Values for Multiagent Planning --- Extended Version
Recent years have seen the development of methods for multiagent plannin...
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Real-time tree search with pessimistic scenarios
Autonomous agents need to make decisions in a sequential manner, under p...
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Towards Real-Time Search Planning in Subsea Environments
We address the challenge of computing search paths in real-time for subs...
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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.
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