Breadth-first heuristic search

Recent work shows that the memory requirements of A* and related graph-search algorithms can be reduced substantially by only storing nodes that are on or near the search frontier, using special techniques to prevent node regeneration, and recovering the solution path by a divide-and-conquer technique. When this approach is used to solve graph-search problems with unit edge costs, we show that a breadth-first search strategy can be more memory-efficient than a best-first strategy. We also show that a breadth-first strategy allows a technique for preventing node regeneration that is easier to implement and can be applied more widely. The breadth-first heuristic search algorithms introduced in this paper include a memory-efficient implementation of breadth-first branch-and-bound search and a breadth-first iterative-deepening A* algorithm that is based on it. Computational results show that they outperform other systematic search algorithms in solving a range of challenging graph-search problems.     

Leveraging personal metadata for Desktop search: The Beagle++ system

Search on PCs has become less efficient than searching the Web due to the increasing amount of stored data. In this paper we present an innovative Desktop search solution, which relies on extracted metadata, context information as well as additional background information for improving Desktop search results. We also present a practical application of this approach—the extensible Beagle⁺⁺ toolbox. To prove the validity of our approach, we conducted a series of experiments. By comparing our results against the ones of a regular Desktop search solution – Beagle – we show an improved quality in search and overall performance.     

Strategies in heuristic search

Real-valued heuristic functions have been extensively used as a means for constraining search in combinatorially large problem spaces. In this paper we look at an alternative approach, called strategic search, in which heuristic information is expressed in the form of problem specific strategies. A strategy is considered to be a (possibly partial) function which for a given state in some problem domain returns sequences of states over the problem domain. The strategy is intended to guide one towards a goal state, but there is no guarantee of success. Strategic search generates a search graph by following some strategy or set of strategies, backtracking to previous choice points when the current strategy fails. We first examine algorithms for performing strategic search using both deterministic and non-deterministic (multiple) strategies. Some examples are given which indicate that strategic search can outperform standard heuristic search methods. We then describe some algorithms which are shown to be admissible when the strategy satisfies certain conditions. The construction of strategies is also considered, and means for acquiring strategic knowledge both from analogous problems and from example execution traces are described. Finally, we indicate how these techniques can be extended to hierarchically organized problem spaces and show how meta-level strategies can be used to guide the application of object level strategies.     

Neural networks and heuristic search

This surveys the recent developments of applying neural networks to heuristic search. Special focus is given to three categories of applications: combinatorial optimization, rule-based inference, and modeling assistance. The avenues for research point to additional opportunities and some of the mathematical problems that remain to be solved.     

A divide-and-conquer local search heuristic for data visualization

Data visualization techniques have become important tools for analyzing large multidimensional data sets and providing insights with respect to scientific, economic, and engineering applications. Typically, these visualization applications are modeled and solved using nonlinear optimization techniques. In this paper, we propose a discretization of the data visualization problem that allows us to formulate it as a quadratic assignment problem. However, this formulation is computationally difficult to solve optimally using an exact approach. Consequently, we investigate the use of a local search technique for the data visualization problem. The space in which the data points are to be embedded can be discretized using an n×n$n\times n$ lattice. Conducting a local search on this n×n$n\times n$ lattice is computationally ineffective. Instead, we propose a divide-and-conquer local search approach that refines the lattice at each step. We show that this approach is much faster than conducting local search on the entire n×n$n\times n$ lattice and, in general, it generates higher quality solutions. We envision two uses of our divide-and-conquer local search heuristic: (1) as a stand-alone approach for data visualization, and (2) to provide a good approximate starting solution for a nonlinear algorithm.     

Kernel search: A general heuristic for the multi-dimensional knapsack problem

In this paper we apply the kernel search framework to the solution of the strongly NP-hard multi-dimensional knapsack problem (MKP). Kernel search is a heuristic framework based on the identification of a restricted set of promising items (kernel) and on the exact solution of ILP sub-problems. Initially, the continuous relaxation of the MKP, solved on the complete set of available items, is used to identify the initial kernel. Then, a sequence of ILP sub-problems are solved, where each sub-problem is restricted to the present kernel and to a subset of other items. Each ILP sub-problem may find better solutions with respect to the previous one and identify further items to insert into the kernel. The kernel search was initially proposed to solve a complex portfolio optimization problem. In this paper we show that the method has general key features that make it appropriate to solve other combinatorial problems using binary variables to model the decisions to select or not items. We adapt the kernel search to the solution of MKP and show that the method is very effective and efficient with respect to known problem-specific approaches. Moreover, the best known values of some MKP benchmark problems from the MIPLIB library have been improved.     

A comparative study of two search strategies for connected wordrecognition: dynamic programming and heuristic search

A most successful approach to recognizing continuous speech is to model the recognition problem as one of finding an optimal path through a finite state network. A comparison of two search strategies for finding the optimal path, dynamic programming and heuristic search, is presented. The comparison is based on theoretical considerations and experimental tests on a digit string task     

A branching heuristic for SAT solvers based on complete implication graphs

Science China Information Sciences - Given a set of radio broadcast programs, the radio broadcast scheduling problem is to allocate a set of devices to transmit the programs to achieve the optimal...     

Using heuristic search for finding deadlocks in concurrent systems

Model checking is a formal technique for proving the correctness of a system with respect to a desired behavior. This is accomplished by checking whether a structure representing the system (typically a labeled transition system) satisfies a temporal logic formula describing the expected behavior. Model checking has a number of advantages over traditional approaches that are based on simulation and testing: it is completely automatic and when the verification fails it returns a counterexample that can be used to pinpoint the source of the error. Nevertheless, model checking techniques often fail because of the state explosion problem: transition systems grow exponentially with the number of components. The aim of this paper is to attack the state explosion problem that may arise when looking for deadlocks in concurrent systems described through the calculus of communicating systems. We propose to use heuristics-based techniques, namely the A* algorithm, both to guide the search without constructing the complete transition system, and to provide minimal counterexamples. We have realized a prototype tool to evaluate the methodology. Experiments we have conducted on processes of different size show the benefit from using our technique against building the whole state space, or applying some other methods.     

Document image decoding by heuristic search

This correspondence describes an approach to reducing the computational cost of document image decoding by viewing it as a heuristic search problem. The kernel of the approach is a modified dynamic programming (DP) algorithm, called the iterated complete path (ICP) algorithm, that is intended for use with separable source models. A set of heuristic functions are presented for decoding formatted text with ICP. Speedups of 3-25 over DP have been observed when decoding text columns and telephone yellow pages using ICP and the proposed heuristics     

Bidirectional heuristic search with limited resources

Heuristic search strategies have useful applicable problem solving in AI. It has been observed that bidirectional heuristic search algorithms can be potentially than their unidirectional counterparts. However, the problem with bidirectional search in practice is to make the two search fronts (forward and backward) meet in the middle. De Champeaux suggested a front-to-front algorithm [3] that overcomes this problem. But the disadvantage of that algorithm is that it is computationally very expensive. In this paper, we suggest a new front-to-front algorithm that is computationally much less expensive. Our algorithm does not guarantee optimality always, but its solution quality and execution time can be controlled by some external parameters. Finally, we present some experimental results on a generic state space problem, viz. 15-puzzle, showing the effectiveness of our algorithm.     

A random search heuristic for a multi-objective production planning

Real world production planning is involved in optimizing different objectives while considering a spectrum of parameters, decision variables, and constraints of the corresponding cases. This comes from the fact that production managers desire to utilize from an ideal production plan by considering a number of objectives over a set of technological constraints. This paper presents a new multi-objective production planning model which is proved to be NP-Complete. So a random search heuristic is proposed to explore the feasible solution space with the hope of finding the best solution in a reasonable time while extracting a set of Pareto-optimal solutions. Then each Pareto-optimal solution is considered as an alternative production plan in the hand of production manager. Both the modeling and the solution processes are carried out for a real world problem and the results are reported briefly. Also, performance of the proposed problem-specific heuristic is verified by comparing it with a multi-objective genetic algorithm on a set randomly generated test data.     

A heuristic search algorithm for path determination with learning

We present and analyze an algorithm, adaptive A*(AA*), for finding a least-cost-path from start node to goal node set in a directed graph. Arc costs are assumed to be scalar-valued, and the cost of each path is the sum of the concomitant arc costs. Search is guided by: 1) a collection of real-valued functions on the node set, which is a generalization of the heuristic function associated with A*; 2) a set of predetermined optimal paths; and 3) a set of paths in the graph that are considered desirable but may or may not be optimal. The knowledge representations described in (1) and (3) can be useful in describing knowledge acquired from humans. The knowledge representation described in (2) can be used to automate knowledge acquisition, so that A* exhibits a form of machine learning. Additionally, the collection of real-valued functions on the node set can be useful in describing bounds on the perfect heuristic function, i.e., the solution of the related dynamic program. A numerical analysis, using a specialization of AA* applied to a model of the Cleveland, OH, road network demonstrated significant performance improvement relative to A*     

Multiobjective heuristic search in road maps

This article considers the application of exact multiobjective techniques to search in large size realistic road maps. In particular, the NAMOA^∗ algorithm is successfully applied to several road networks from the DIMACS shortest path implementation challenge with two objectives. An efficient heuristic function previously proposed by Tung and Chew is evaluated. Heuristic values are precalculated with search. The precalculation effort is shown to pay off during the multiobjective search stage. An improvement to the calculation procedure is also proposed, resulting in added improved time performance in many problem instances.     

Energy-saving light positioning using heuristic search

A new definition is given to the problem of light positioning in a closed environment, aiming at obtaining, for a global illumination radiosity solution, the position and emission power for a given number of lights that provide a desired illumination at a minimum total emission power. Such a desired illumination is expressed using minimum and/or maximum values of irradiance allowed, resulting in a combinatory optimization problem. A pre-process computes and stores irradiances for a pre-established set of light positions by means of a radiosity random walk. The reuse of photon paths makes this pre-process reasonably cheap. Different heuristic search algorithms, combined to linear programming, are discussed and compared, from the simplest hill climbing strategies to the more sophisticated population-based and hybrid approaches. The paper shows how the presented approaches make it possible to obtain a good solution to the problem at a reasonable cost.     

Using knowledge generated in heuristic search for nonchronological backtracking

Problem solvers that use heuristics to guide choices often run into untenable situations that can be characterized as overconstrained. When this happens, the problem must be able to identify the right culprit from among its heuristic choices in order to avoid a potentially explosive search. In this paper, we present a solution to this for a certain class of problems where the justifications associated with choice points involve an explicit assessment of the pros and cons of choosing each alternative relative to its competitors. We have designed a problem solver that accumulates such knowledge about the pros and cons of alternative selections at choice points during heuristic search, which it updates in light of an evolving problem situation. Whenever untenable situations arise, this preserved knowledge is used to determine the most appropriate backtracking point. By endowing the backtracker with access to this domain-specific knowledge, a highly contextual approach to reasoning in backtracking situations can be achieved.     

A search heuristic for just-in-time scheduling in parallel machines

In recent years the Just-in-Time (JIT) production philosophy as been adopted by many companies around the world. This has motivated the study of scheduling models that embrace the essential components of JIT systems. In this paper, we present a search heurustic for the weighted earliness penalty problem with deadlines in parallel identical machines. Our approach combines elements of the solution methods known as greedy randomized adaptive search procedure (GRASP) and tabu search. It also uses a branch-and-bound post-processor to optimize individually the sequence of the jobs assigned to each machine.