Linear-space best-first search

We describe how to convert the heuristic search algorithm A* into an anytime algorithm that finds a sequence of improved solutions and eventually converges to an optimal solution. The approach we adopt uses weighted heuristic search to find an approximate solution quickly, and then continues the weighted search to find improved solutions as well as to improve a bound on the suboptimality of the current solution. When the time available to solve a search problem is limited or uncertain, this creates an anytime heuristic search algorithm that allows a flexible tradeoff between search time and solution quality. We analyze the properties of the resulting Anytime A* algorithm, and consider its performance in three domains; sliding-tile puzzles, STRIPS planning, and multiple sequence alignment. To illustrate the generality of this approach, we also describe how to transform the memory-efficient search algorithm Recursive Best-First Search (RBFS) into an anytime algorithm.

/pdf/anytime-heuristic-search-20u3t5sqge.pdf

Anytime heuristic search

Scalable Load Balancing Techniques for Parallel Computers

Iterative-deepening searches mimic a breadth-first node expansion with a series of depth-first searches that operate with successively extended search horizons. They have been proposed as a simple way to reduce the space complexity of best-first searches like A* from exponential to linear in the search depth. But there is more to iterative-deepening than just a reduction of storage space. As the authors show, the search efficiency can be greatly improved by exploiting previously gained node information. The information management techniques considered here owe much to their counterparts from the domain of two-player games, namely the use of fast-execution memory functions to guide the search. The authors' methods not only save node expansions, but are also faster and easier to implement than previous proposals. >

Enhanced iterative-deepening search

The assessment of bidirectional heuristic search has been incorrect since it was first published more than a quarter of a century ago. For quite a long time, this search strategy did not achieve the expected results, and there was a major misunderstanding about the reasons behind it. Although there is still wide-spread belief that bidirectional heuristic search is afflicted by the problem of search frontiers passing each other, we demonstrate that this conjecture is wrong. Based on this finding, we present both a new generic approach to bidirectional heuristic search and a new approach to dynamically improving heuristic values that is feasible in bidirectional search only. These approaches are put into perspective with both the traditional and more recently proposed approaches in order to facilitate a better overall understanding. Empirical results of experiments with our new approaches show that bidirectional heuristic search can be performed very efficiently and also with limited memory. These results suggest that bidirectional heuristic search appears to be better for solving certain difficult problems than corresponding unidirectional search. This provides some evidence for the usefulness of a search strategy that was long neglected. In summary, we show that bidirectional heuristic search is viable and consequently propose that it be reconsidered.

/pdf/bidirectional-heuristic-search-reconsidered-1vcl339g9q.pdf

Bidirectional heuristic search reconsidered

We present a comparison of three well known heuristic search algorithms: best-first search (BFS), iterative-deepening (ID), and depth-first branch-and-bound (DFBB). We develop a model to analyze the time and space complexity of these three algorithms in terms of the heuristic branching factor and solution density. Our analysis identifies the types of problems on which each of the search algorithms performs better than the other two. These analytical results are validated through experiments on different problems. We also present a new algorithm, DFS*, which is a hybrid of iterative deepening and depth-first branch-and-bound, and show that it outperforms the other three algorithms on some problems.

Nageshwara Rao Vempaty

Papers

Depth-first vs best-first search

Depth-First Versus Best-First Search.