We propose two new online methods for estimating the size of a backtracking search tree. The first method is based on a weighted sample of the branches visited by chronologi-cal backtracking. The second is a recursive method based on assuming that the unexplored part of the search tree

We propose two new online methods for estimating the size of a backtracking search tree. The first method is based on a weighted sample of the branches visited by chronological backtracking. The second is a recursive method based on assuming that the unexplored part of the search tree

Abstract. Much work has been done on describing tractable classes of constraint networks. Most of the known tractable examples are described by either restricting the structure of the networks, or their language. In-deed, for both structural or language restrictions very strong dichotomy results have been proven and in both cases it is likely that all practical examples have already been discovered. As such it is timely to consider tractability which cannot be described by language or structural restrictions. This is the focus of the work here. In this paper we investigate a novel reason for tractability: having at least one variable ordering for which the number of partial solutions to the first n variables is bounded by a polynomial in n. We show that the presence of sufficient functional constraints can guar-antee this property and we investigate the complexity of finding good variable orderings based on different notions of functionality. What is more we identify a completely novel reason for tractability based on so called Turan sets.

In this paper we introduce Stratified Sampling with Du-plicate Detection (SSDD), an algorithm for estimating the number of state expansions performed by heuris-tic search algorithms seeking solutions in state spaces represented by undirected graphs. SSDD is general and can be applied to estimate

to data objects of non-zero size located m multi-dimensional spaces In this paper we describe a dynamic index structure called an R-tree which meets this need, and give algorithms for searching and updating it. We present the results of a series of tests which indicate that the structure performs well

presents a more sophisticated search algorithm for optimal (and anytime) winner determination, including structural improvements that reduce search tree size, faster data structures, and optimizations at search nodes based on driving toward, identifying and solving tractable special cases. We also uncover

A new and conceptually simple data structure, called a suffix array, for on-line string searches is intro-duced in this paper. Constructing and querying suffix arrays is reduced to a sort and search paradigm that employs novel algorithms. The main advantage of suffix arrays over suffix trees

The complexities of various search algorithms are considered in terms of time, space, and cost of solution path. It is known that breadth-first search requires too much space and depth-first search can use too much time and doesn't always find a cheapest path. A depth-first iteratiw

A new access meth d, called M-tree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion

Field texture models and generates textures through a deterministic searching process. We accelerate this synthesis process using tree-structured vector quantization.