Bounded suboptimal search algorithms are usually faster than optimal ones, but they can still run out of memory on large problems. This paper makes three contributions. First, we show how solution length estimates, used by the current stateof-the-art linear-space bounded suboptimal search algorithm Iterative Deepening EES, can be used to improve unboundedspace suboptimal search. Second, we conv...

Power-supply, a surprisingly simple and new general paradigm for the development of self-stabilizing algorithms in diierent models, is introduced. The paradigm is exempliied by developing simple and eecient self-stabilizing algorithms for leader election and either BFS or DFS spanning tree constructions, in strongly-connected unidirectional and bi-directional dynamic networks (synchronous and a...

The selection of entries to be included/excluded in Branch and Bound algorithms is usually done on the basis of cost values. We consider the class of Depth First Search algorithms, and we propose to use upper tolerances to guide the search for optimal solutions. In spite of the fact that it needs time to calculate tolerances, our computational experiments for Asymmetric Traveling Salesman Probl...

This paper presents history-dependent scheduling, a new technique for reducing the search space in the verification of cryptographic protocols. This technique allows the detection of some “non-minimal” interleavings during a depth-first search, and was implemented in TRUST, our cryptographic protocol verifier. We give some experimental results showing that our method can greatly increase the ef...

This paper presents an optimal strategy for solving the 4 peg-7 color Mastermind MM(4,7) in the expected case (4.676) along with optimal strategies or upper bounds for other values. The program developed is using a depth-first branch and bound algorithm relying on tight upper bound, dynamic lower bound evaluation and guess equivalence to prune symmetric tree branches.

The class of closed patterns is a well known condensed representations of frequent patterns, and have recently attracted considerable interest. In this paper, we propose an efficient algorithm LCM (Linear time Closed pattern Miner) for mining frequent closed patterns from large transaction databases. The main theoretical contribution is our proposed prefix-preserving closure extension of closed...

The efficiency of A* searching depends on the quality of the lower bound estimates of the solution cost. Pattern databases enumerate all possible subgoals required by any solution, subject to constraints on the subgoal size. Each subgoal in the database provides a tight lower bound on the cost of achieving it. For a given state in the search space, all possible subgoals are looked up in the pat...

In this paper, we describe a system for automatic and interactive content-based retrieval of video that integrates features, models, and semantics. The novelty of the approach lies in the (1) semi-automatic construction of models of scenes, events, and objects from feature descriptors, and (2) integration of content-based and model-based querying in the search process. We describe several appro...

Following the seminal idea of John W. Tukey (1975), statistical data depth is a function that determines centrality of an arbitrary point w.r.t. a data cloud or a probability measure. During the last decades, data depth rapidly developed to a powerful machinery proving to be useful in various fields of science. Recently, implementing the idea of depth in the functional setting attracted a lot o...

Factoring is believed to be a diflcult task. Although factoring is of interest in its own right, the security of RSA cryptography, among other cryptographic systems, is dependent on the diflculty of factoring the product of two large primes. We show how to cast the problem of factoring integers as a state-based search to which the techniques of AI may be applied. Using small primes as operators...