Learning in Real-Time Search: A Unifying Framework

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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 agent's 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
Vadim Bulitko, Greg Lee
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where JAIR
Authors Vadim Bulitko, Greg Lee
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