Abstract
Anytime heuristic search algorithms are widely applied where best-first search algorithms such as A* require large or often unacceptable amounts of time and memory. Anytime algorithms produce a solution quickly and iteratively improve the solution quality. In this paper, we propose novel anytime heuristic search algorithms with a common underlying strategy called Column Search. The proposed algorithms are complete and guarantee to produce an optimal solution. Experimental results on sliding-tile puzzle problem, traveling salesman problem, and robotic arm trajectory planning problem show the efficacy of proposed methods compared to state-of-the-art anytime heuristic search algorithms.
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Vadlamudi, S.G., Gaurav, P., Aine, S., Chakrabarti, P.P. (2012). Anytime Column Search. In: Thielscher, M., Zhang, D. (eds) AI 2012: Advances in Artificial Intelligence. AI 2012. Lecture Notes in Computer Science(), vol 7691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35101-3_22
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DOI: https://doi.org/10.1007/978-3-642-35101-3_22
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