J. L. Pérez de la Cruz

Multicriteria heuristic search

Abstract This paper extends the multicriteria decision paradigm to the heuristic search domain in a systematic way. A useful formal definition of multicriteria heuristic graph search problems is provided. Then the fundamental issues in multicriteria search are described in detail and a general framework is developed. Several new procedures are presented and analyzed for the usual multicriteria decision rules: multiobjective, multiattribute, goal satisfaction, and lexicographic search.

Path recovery in frontier search for multiobjective shortest path problems

Frontier search is a best-first graph search technique that allows significant memory savings over previous best-first algorithms. The fundamental idea is to remove from memory already explored nodes, keeping only open nodes in the search frontier. However, once the goal node is reached, additional techniques are needed to recover the solution path. This paper describes and analyzes a path recovery procedure for frontier search applied to multiobjective shortest path problems. Differences with the scalar case are outlined, and performance is evaluated over a random problem set.

A comparison of heuristic best-first algorithms for bicriterion shortest path problems

A variety of algorithms have been proposed to solve the bicriterion shortest path problem. This article analyzes and compares the performance of three best-first (label-setting) algorithms that accept heuristic information to improve efficiency. These are NAMOA∗, MOA∗, and Tung & Chew’s algorithm (TC). A set of experiments explores the impact of heuristic information in search efficiency, and the relative performance of the algorithms. The analysis reveals that NAMOA∗ is the best option for difficult problems. Its time performance can benefit considerably from heuristic information, though not in all cases. The performance of TC is similar but somewhat worse. However, the time performance of MOA∗ is found to degrade considerably with the use of heuristic information in most cases. Explanations are provided for these phenomena.

A memory-efficient search strategy for multiobjective shortest path problems

The paper develops vector frontier search, a new multiobjective search strategy that achieves an important reduction in space requirements over previous proposals. The complexity of a resulting multiobjective frontier search algorithm is analyzed and its performance is evaluated over a set of random problems.

Comparison of heuristics in multiobjective a * search

The paper reconsiders the importance of monotonicity and consistency properties on the efficiency of multiobjective A* search. Previous works on the MOA* algorithm (Multi-objective A*) concluded that the importance of the monotone property of heuristics was not as important as in A*. The recent development of an alternative algorithm (NAMOA*), gives a chance to review these results. The paper presents a formal analysis on the comparison of heuristics in NAMOA* and concludes that the properties of consistency and monotonicity are of fundamental importance in search efficiency.

A heuristic search algorithm with lexicographic goals

This paper describes a new general algorithm for graph search problems with additive lexicographic goals. The use of lexicographic goals in the formulation of search problems provides greater control and expressive power over the properties of solution paths. The algorithm, called METAL-A N * can be used to find the set of all solutions to a goal problem. The fundamental concepts of the algorithm are explained and a simple example is used to trace its behaviour. Sufficient conditions that guarantee the completeness and admissibility of METAL-A N * are also presented. r 2002 Elsevier Science Ltd. All rights reserved.

Highway design by constraint specification

Abstract This paper describes the application of AI techniques to the problem of designing civil engineering objects like highways or roads. These design processes must cope with a complex environment, and constraints and alternatives are not predefined, but chosen during the process of designing. Several abstract models of design are presented and their applicability to road design is discussed. Finally, a partially new model is defined and applied to this problem. Outputs and inputs of an implemented prototype are shown.

A comparison of multiobjective depth-first algorithms

Many real world problems involve several, usually conflicting, objectives. Multiobjective analysis deals with these problems locating trade-offs between different optimal solutions. Regarding graph search problems, several algorithms based on best-first and depth-first approaches have been proposed to return the set of all Pareto optimal solutions. This article presents a detailed comparison between two representatives of multiobjective depth-first algorithms, PIDMOA* and MO-DF-BnB. Both of them extend previous single-objective search algorithms with linear-space requirements to the multiobjective case. Experimental analyses on their time performance over tree-shaped search spaces are presented. The results clarify the fitness of both algorithms to parameters like the number or depth of goal nodes.

A New Approach to Iterative Deepening Multiobjective A

Multiobjective search is a generalization of the Shortest Path Problem where several (usually conflicting) criteria are optimized simultaneously. The paper presents an extension of the single-objective IDA* search algorithm to the multiobjective case. The new algorithm is illustrated with an example, and formal proofs are presented on its termination, completeness, and admissibility. The algorithm is evaluated over a set of random tree search problems, and is found to be more efficient than IDMOA*, a previous extension of IDA* to the multiobjective case.

An expert system for identifying steels and cast irons

Abstract This paper presents an application of Knowledge Engineering techniques to the problem of identifying a steel or cast iron from a microphotograph. The essential aim of the implemented system is to help metallography students in the task of learning the concepts relevant for identifying and classifying steels and cast irons. The system has been developed and implemented by means of Knowledge Engineering tools, and all the goals set up at the beginning of the project have been reached.