Lawrence Mandow

Multiobjective A * search with consistent heuristics

The article describes and analyzes NAMOA*, an algorithm for multiobjective heuristic graph search problems. The algorithm is presented as an extension of A*, an admissible scalar shortest path algorithm. Under consistent heuristics A* is known to improve its efficiency with more informed heuristics, and to be optimal over the class of admissible algorithms in terms of the set of expanded nodes and the number of node expansions. Equivalent beneficial properties are shown to prevail in the new algorithm.

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.

Parallel Label-Setting Multi-objective Shortest Path Search

We present a parallel algorithm for finding all Pareto optimal paths from a specified source in a graph. The algorithm is label-setting, i.e., it only performs work on distance labels that are optimal. The main result is that the added complexity when going from one to multiple objectives is completely parallelizable. The algorithm is based on a multiobjective generalization of a priority queue. Such a Pareto queue can be efficiently implemented for two dimensions. Surprisingly, the parallel biobjective approach yields an algorithm performing asymptotically less work than the previous sequential algorithms. We also discuss generalizations for d ≥ 3 objective functions and for single target search.

Design with shape grammars and reinforcement learning

Shape grammars are a powerful and appealing formalism for automatic shape generation in computer-based design systems. This paper presents a proposal complementing the generative power of shape grammars with reinforcement learning techniques. We use simple (naive) shape grammars capable of generating a large variety of different designs. In order to generate those designs that comply with given design requirements, the grammar is subject to a process of machine learning using reinforcement learning techniques. Based on this method, we have developed a system for architectural design, aimed at generating two-dimensional layout schemes of single-family housing units. Using relatively simple grammar rules, we learn to generate schemes that satisfy a set of requirements stated in a design guideline. Obtained results are presented and discussed.

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.

Sindi: an intelligent assistant for highway design

Abstract We present a system to support highway engineers in the tasks of preliminary highway design. The system is based on the ideas of heuristic search and explicit preference setting. The system has been implemented and installed in a Spanish engineering firm.

Multiobjective shortest path problems with lexicographic goal-based preferences

Multiobjective shortest path problems are computationally harder than single objective ones. In particular, execution time is an important limiting factor in exact multiobjective search algorithms. This paper explores the possibility of improving search performance in those cases where the interesting portion of the Pareto front can be initially bounded. We introduce a new exact label-setting algorithm that returns the subset of Pareto optimal paths that satisfy a set of lexicographic goals, or the subset that minimizes deviation from goals if these cannot be fully satisfied. Formal proofs on the correctness of the algorithm are provided. We also show that the algorithm always explores a subset of the labels explored by a full Pareto search. The algorithm is evaluated over a set of problems with three objectives, showing a performance improvement of up to several orders of magnitude as goals become more restrictive.

Building Software Agents from Software Components

The widespread use of the Internet has favored the development of distributed multi-agent systems. The development of agent-based applications is carried out with Agent-Oriented Software Engineering methods, techniques and tools. Although there are several different platforms and methodologies for software agents design, the lack of flexible agent architectures makes the development of multi-agent systems a tiresome and hard task. Current agent architectures provided by these platforms and methodologies do not offer enough flexibility for the development of flexible software agents, placing little emphasis on reuse. This paper presents a software agent development approach using a component-based architecture that promotes building agents from reusable software components. The basis of our approach is the use of component-based software development concepts and the separation of concerns principle to separate agent functionality into independent entities increasing the maintainability and adaptability of the agent to new environments and demands. This architecture simplifies the software agent development process, reducing it to the description of the agents’ software components and interaction protocols using XML documents. The power of Java and Jess technologies has been exploited in the implementation of our compositional model of software agents.

Layered shape grammars

Abstract In this article we propose a computer-aided conceptual design system to assist modelling at the early stages of design. More precisely, we address the problem of providing the designer with design alternatives that can be used as starting points of the design process. To guide the generation of such alternatives according to a given set of design requirements, the designer can express both visual knowledge in the form of basic geometric transformation rules, and also logic constraints that guide the modelling process. Our approach is based on the formalism of shape grammars, and supplements the basic algorithms with procedures that integrate logic design constraints and goals. Additionally, we introduce a layered scheme for shape grammars that can greatly reduce the computational cost of shape generation. Shape grammars, constraints, goals and layers can be handled through a graphic environment. We illustrate the functionalities of ShaDe through two use cases taken from the architectural design and video games domains, and also evaluate the performance of the system.

An Intelligent Tutor for a Web-Based Chess Course

Web-based intelligent tutoring systems try to fill the gap between human teachers and printed textbooks as distance learning aids. Actually, intelligent tutoring systems research is concerned with the development of computer tools that show adaptive capabilities in the domain of tutoring, where the students progress is autonomously monitored and guided according to some tutoring strategy. This paper provides details on the analysis, design and implementation of such a system. STIA (Sistema Tutor en Internet de Ajedrez) is a fully implemented Web-based tool developed to provide adaptive guidance and help while learning chess basics. In STIA the task of the tutor is to guide the student efficiently through the course material, according to the tutoring strategy defined by the course authors. This is achieved in two ways. First, it imposes limits on the portion of course material the students can access. This prevents them from getting lost in a flood of information. Second, the tutor evaluates each students knowledge through a set of problems, and according to result recommends reviewing theory, solving more problems or advancing through the course.