José Negrete Martínez

Net of multi-agent expert systems with emergent control

Abstract An aperture to expertise as a social outcome is to minimize supervised control: control of an expert system society and control of an agents society in multi-agent expert systems. The present paper starts this aperture with autonomous behavioral agents within Expert Systems that exhibit bottom-up emergent control. The behavior-agents are either reasoning agents or communicating agents. The reasoning agents have a blackboard Domain Working Memory or a Meta Working Memory. The Expert Systems communicate with each other in a net for the solution of a problem. A practical development has been made in the medical domain of interconsultation that shows expedient problem-solving behavior.

Redsiex: a cooperative network of expert systems with blackboard architectures

The blackboard architecture, originally developed for the system that permits the comprehension of language, HEARSAY II, has later been used in a great variety of domains and in various environments for the construction of systems. From the classic architecture of HEARSAY II, many applications, generalizations, extensions and refinements have been developed. In this paper we present REDSIEX, (RED de SIstemas EXpertos) which is a network of expert systems within a blackboard architecture, for the cooperation solution of distributed problems. The REDSIEX system inherits various of the elements defined by the architecture of HEARSAY II and incorporates new components and organization. These produce a very characteristic and exclusive global work style in the solution of problems, within a conceptual framework of emergent control. The main structural and functional characteristics of REDSIEX are discussed.

A Model for Combination of External and Internal Stimuli in the Action Selection of an Autonomous Agent

This paper proposes a model for combination of external and internal stimuli for the action selection in an autonomous agent, based in an action selection mechanism previously proposed by the authors. This combination model includes additive and multiplicative elements, which allows to incorporate new properties, which enhance the action selection. A given parameter α, which is part of the proposed model, allows to regulate the degree of dependence of the observed external behaviour from the internal states of the entity.

Evolutionary computation solutions to the circle packing problem

In this work, we present an evolutionary omputation-based solution to the circle packing problem (ECPP). The circle packing problem consists of placing a set of circles into a larger containing circle without overlaps: a problem known to be NP-hard. Given the impossibility to solve this problem efficiently, traditional and heuristic methods have been proposed to solve it. A naïve representation for chromosomes in a population-based heuristic search leads to high probabilities of violation of the problem constraints, i.e., overlapping. To convert solutions that violate constraints into ones that do not (i.e., feasible solutions), in this paper we propose two repair mechanisms. The first one considers every circle as an elastic ring and overlaps create repulsion forces that lead the circles to positions where the overlaps are resolved. The second one forms a Delaunay triangulation with the circle centers and repairs the circles in each triangle at a time, making sure repaired triangles are not modified later on. Based on the proposed repair heuristics, we present the results of the solution to the CPP problem to a set of unit circle problems (whose exact optimal solutions are known). These benchmark problems are solved using genetic algorithms, evolutionary strategies, particle swarm optimization, and differential evolution. The performance of the solutions is compared to those known solutions based on the packing density. We then perform a series of experiments to determine the performance of ECPP with non-unitary circles. First, we compare ECPP’s results to those of a public competition, which stand as the world record for that particular instance of the non-unitary CPP. On a second set of experiments, we control the variance of the size of the circles. In all experiments, ECPP yields satisfactory near-optimal solutions.

Solving a Scholar Timetabling Problem Using a Genetic Algorithm - Study Case: Instituto Tecnologico De Zitacuaro

The Scholar Timetabling Problem consists of fixing a sequence of meetings between lecturers, classrooms and schedule to a set of groups and courses in a given period of time, satisfying a set of different constraints, where each course, lecturer, classroom, and time have special features, this problem is known to be NP-hard. Given the impossibility to solve this problem optimally, traditional and metaheuristic methods have been proposed to provide near-optimal solutions. This paper shows the implementation of a Genetic Algorithm (GA) using a real coding to solve the Scholar Timetabling Problem. A naive representation for chromosomes in a population-based heuristic search leads to high probabilities of violation of the problem constraints. To convert solutions that violate constraints (unfeasible solutions) into ones that do not (feasible solutions), we propose a repair mechanism. Based on the proposed mechanism, we present a possible solution to the Scholar Timetabling Problem applied to a real school (Instituto Tecnologico de Zitacuaro). Here we present experimental results based on different types of GA configurations to solve this problem and present the best GA configuration to solve the study case.