Abraham Sánchez López

An approximation method for the P-median problem: A bioinspired tabu search and variable neighborhood search partitioning approach

The bioinspired systems are presented as a set of models that are based on the behavior of some biological systems and how act. These models can be expressed in data mining and operations research where clustering is a recurrent technique used to solve the P-median and territorial design problems. At this point, we have solved the P-median problem with a partitioning approach with bioinspired aspects and Variable Neighborhood Search (VNS). In this work we have improved the basic VNS search strategy and we present a bioinspired partitioning algorithm with optimization by Tabu Search (TS). This clusteringpartitioning problem under a bioinspired connotation has been proposed after observing some characteristics in common between clustering and human behavior during conflict situations, where some characteristics have been modeled accordingly. Finally we present our progress from our VNS implementation to our TS proposal.

A Topological SPLAM Approach for Robust Exploration

This paper describes a simultaneous planning localization and mapping (SPLAM) approach, where a mobile robot explores the environment efficiently and also considers the requisites of the simultaneous localization and mapping algorithm. The method is based on the randomized incremental generation of a data structure called the Random Exploration Graph (REG), which represents a roadmap of the explored area with an associated safe region. The REG approach includes a frontier control to carry a registry over the nodes that have not been fully explored. A continuous localization procedure based on B-Splines features of the safe region is integrated in the scheme. Simulations and experiments on the Pioneer P3DX robot show the performance of this approach.

An Architecture for Cognitive Modeling to Support Real-Time Adaptation and Motivational Responses in Video Games

Currently, there are tremendous advances in gaming technologies to improve physical realism of environments and characters. However, game characters are still lacking in the cognitive realism, thus video games and game development tools need to provide functionality to support real-time adaptation and appropriate response to internal motivations. In this paper we propose an architecture for cognitive modeling based on well-known cognitive architectures like ACT-R and Soar. It describes a methodology for developing a behavioral system and it emphasizes an ethological sensing modeling to simulate more realistic behaviors. This methodology serves to develop a component-based system that could improve the ability to create intelligent virtual agents. In this phase of implementation, we present preliminary results of modeling behavior of a fish character in an undersea world using a game engine.

On the Use of Ant Colony Optimization for Video Games

Traditionally, games and video games have provided a framework for the study of Artificial Intelligence approaches. The main objective of this work is to verify whether the optimization method based on ant colonies can be applied to the development of a competitive agent in the environment of videogames in real time. One of the important characteristics of the presented work is the optimization calculations to void losing the game fluidity. In addition, another aim of the work is to obtain an architecture for the development of educational type videogames to encourage inexperienced users to interact with artificial intelligence algorithms. The game allows the user to experience the changes in the algorithm given certain changes in the parameters and variables used by the Ant Colony Optimization algorithm itself.

TSP in Partitioning with Tabu Search

Solving Territorial Design problems implies grouping territorial units into k of groups with compactness and/or contiguity restrictions. However each group formed is often treated in accordance to a conflict of interest; one of them is the routing problem. In this work grouping geographical units is also known as classification by partitions, which is a well-known high complexity problem. This complexity requires reaching approximated partitioning solutions of the territory in a reasonable computing time; therefore we have chosen the tabu search metaheuristic because it has achieved very efficient results in several optimization problems. Once tabu search has returned a solution, we apply an exact algorithm to the elements of the partition, which solves a routing problem.

Ant Colony Optimization for Solving the Quadratic Assignment Problem

Many real-world problems in logistics, transport, and manufacturing can be modeled as combinatorial optimization problems. In this work, a hybrid variant of meta-heuristic algorithm ant colony optimization (ACO) is used. Different variants of ant colony optimization have been applied to the quadratic assignment problem (QAP). In this paper a hybrid approach is proposed, which is combination of Ant system and other meta-heuristic approaches to take benefits of both methods. This hybrid approach is accompanied by a local search technique. Moreover, a comparative analysis is done using QAPLIB.