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This work presents an iterative anytime heuristic search algorithm called Anytime Window A* (AWA*) where node expansion is localized within a sliding window comprising of levels of the search tree/graph. The search starts in depth-first mode and gradually proceeds towards A* by incrementing the window size. An analysis on a uniform tree model provides some(More)
Incremental heuristic searches try to reuse their previous search efforts whenever these are available. As a result, they can often solve a sequence of similar planning problems much faster than planning from scratch. State-of-the-art incremental heuristic searches such as LPA*, D* and D* Lite all work by propagating cost changes to all the states on the(More)
The performance of heuristic search based planners depends heavily on the quality of the heuristic function used to focus the search. These algorithms work fast and generate high-quality solutions, even for high-dimensional problems, as long as they are given a well-designed heuristic function. On the other hand, their performance can degrade considerably(More)
This paper presents a heuristic-search algorithm called Memory-bounded Anytime Window A∗ (MAWA∗), which is complete, anytime, and memory bounded. MAWA∗ uses the window-bounded anytime-search methodology of AWA∗ as the basic framework and combines it with the memory-bounded A∗ -like approach to handle restricted memory situations. Simple and efficient(More)
Parameter control of evolutionary algorithms (EAs) poses special challenges as EA uses a population and requires many parameters to be controlled for an effective search. Quality improvement is dependent on several factors, such as, fitness estimation, population diversity and convergence rate. A widely practiced approach to identify a good set of(More)