An adaptive multi-agent system for self-organizing continuous optimization

Cette thèse présente une nouvelle approche pour la distribution de processus d'optimisation continue dans un réseau d'agents coopératifs. Dans le but de résoudre de tels problèmes, le domaine de l'optimisation multidisciplinaire a été proposé. Les méthodes d'optimisation multidisciplinaire proposent de distribuer le processus d'optimisation, généralement en reformulant le problème original d'une manière qui réduit les interconnexions entre les disciplines. Cependant, ces méthodes présentent des désavantages en ce qui concerne la difficulté de les appliquer correctement, ainsi que leur manque de flexibilité. En se basant sur la théorie des AMAS (Adaptive Multi-Agent Systems), nous proposent une représentation générique à base d'agents des problèmes d'optimisation continue. A partir de cette représentation, nous proposons un comportement nominal pour les agents afin d'exécuter le processus d'optimisation. Nous identifions ensuite certaines configurations spécifiques qui pourraient perturber le processus, et présentons un ensemble de comportements coopératifs pour les agents afin d'identifier et de résoudre ces configurations problématiques. Enfin, nous utilisons les mécanismes de coopération que nous avons introduit comme base à des patterns de résolution coopérative de problèmes. Ces patterns sont des recommandations de haut niveau pour identifier et résoudre des configurations potentiellement problématiques qui peuvent survenir au sein de systèmes de résolution collective de problèmes. Ils fournissent chacun un mécanisme de résolution coopérative pour les agents, en utilisant des indicateurs abstraits qui doivent être instanciés pour le problème en cours.In an effort to tackle such complex problems, the field of multidisciplinary optimization methods was proposed. Multidisciplinary optimization methods propose to distribute the optimization process, often by reformulating the original problem is a way that reduce the interconnections between the disciplines. However these methods present several drawbacks regarding the difficulty to correctly apply them, as well as their lack of flexibility. Based on the AMAS (Adaptive Multi-Agent Systems) theory, we propose a general agent-based representation of continuous optimization problems. From this representation we propose a nominal behavior for the agents in order to do the optimization process. We then identify some specific configurations which would disturb this nominal optimization process, and present a set of cooperative behaviors for the agents to identify and solve these problematic configurations. At last, we use the cooperation mechanisms we introduced as the basis for more general Collective Problem Solving Patterns. These patterns are high-level guideline to identify and solve potential problematic configurations which can arise in distributed problem solving systems. They provide a specific cooperative mechanism for the agents, using abstract indicators that are to be instantiated on the problem at hand

Parallel memetic algorithms for the problem of workforce distribution in dynamis multi-agent system

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 20/09/2013Esta tesis describe un novedoso enfoque para resolver el problema de distribución de carga de trabajo en sistemas multi-agente dinámicos basados en arquitecturas de pizarra, enfocándose especialmente en un escenario real: el call center multitarea. Para abordar este tipo de entornos dinámicos, tradicionalmente se han aplicado diversas heurísticas voraces que permiten dar una solución en tiempo real. Básicamente, dichas heurísticas realizan planificaciones continuamente, considerando el estado del sistema en cada momento. Como las decisiones se toman de forma voraz sin hacer una planificación óptima, la distribución de la carga de trabajo puede ser pobre a medio y/o largo plazo. El uso de algoritmos meméticos paralelos nos puede permitir encontrar soluciones mucho más precisas. Para aplicar este tipo de algoritmos, introducimos el concepto de ventana temporal adaptativa. De esta forma, el tamaño de la ventana temporal depende del nivel de dinamismo del sistema en un instante dado. Este trabajo propone una serie de herramientas para determinar el dinamismo del sistema de forma automática, así como un novedoso módulo de predicción basado en una red neuronal y un potente método de búsqueda basado en meta-algoritmos meméticos paralelos para poder lidiar con entornos dinámicos complejos. Para concluir, comparamos nuestro enfoque con otras técnicas del estado del arte en un entorno de producción real (Telefónica) obteniendo mejores resultados que el resto de técnicas actuales. También se proporciona un estudio exhaustivo de cada uno de los módulos.Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Optimization of nonlinear function with planar regions using supernova

Nowadays, optimization is begun to be use in different fields, e.g. preference algorithms. These new challenges need a robustness meta heuristics to solve them. Supernova meta heuristic that emules the descent behavior of the gradients and share the same weakness of them. They get stuck planar regions and hardly find the needle minimum. The main objective of this works is to improve the performance of the original version of supernova for the problematic topologies mention above. First, a review of how to these problems are solved in the literature is presented. Second, A criterion to determine planar regions is described . Third, a strategy to choose the parameters agree with the topology of the function is implemented. Supernova 2.0 was tested using the set of benchmarks functions proposed in CEC2013. The new version is significantly better than the original version, no significantly better than SPSO2011 and significantly inferior with SADE. Although, the results are applied to Supernova, most of the strategies can be applied to other methods.Doctorad

Interaction and Intelligent Behavior

We introduce basic behaviors as primitives for control and learning in situated, embodied agents interacting in complex domains. We propose methods for selecting, formally specifying, algorithmically implementing, empirically evaluating, and combining behaviors from a basic set. We also introduce a general methodology for automatically constructing higher--level behaviors by learning to select from this set. Based on a formulation of reinforcement learning using conditions, behaviors, and shaped reinforcement, out approach makes behavior selection learnable in noisy, uncertain environments with stochastic dynamics. All described ideas are validated with groups of up to 20 mobile robots performing safe--wandering, following, aggregation, dispersion, homing, flocking, foraging, and learning to forage

Specification-driven design

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil Engineering, 1990.Includes bibliographical references (leaves 144-151).by Nayel Salah el-Schafei.Ph.D

Using symmmetries to solve asymmetric problems

This dissertation describes two projects in which the treatment of a difficult and asymmetric problem is simplified by using symmetries of basic building blocks of the problem. In the first part of this dissertation we address the problem of determining the effective interaction between ions in metallic systems. Our work applies more generally to systems where effective interactions between massive particles can be calculated to take into account, in an average way, the effect of lighter particles present in the system. We find an equality relating the (asymmetric) effective interaction of two massive particles and the (symmetric) effect of a single massive particle on the density of the light particles. We show how this relation can be used to improve upon the precision of effective potentials calculated by perturbative approaches for an assortment of systems including hydrogen in metallic environment. In the second part of this dissertation we discuss constraint satisfaction problems. We provide multiple examples of constraint satisfaction problems occurring in various scientific areas. In many cases the individual constraints are highly symmetric, while the resulting constraint satisfaction problem is not; there is no symmetry common to all the constraints. We describe divide and concur, a new approach to solve constraint problems, which is based on projections to the individual constraint sets. The definition of efficient projection operators are facilitated by symmetries of the constraint sets. We show that this method is competitive with the state-of-the-art on standard benchmark problems, and in the process establish a number of records in finite disk packing problems. Many applications of the divide and concur approach are still to be explored, and we provide the reader with tools to do so, including promising applications and a list of constraint sets together with efficient projection operators.NSERC Fellowship, FQRNT Fellowship, National Science Foundation Grant DMR-0426568, National Science Foundation Grant DMR-0601461

An Algorithm for Evolving Protocol Constraints

Centre for Intelligent Systems and their ApplicationsWe present an investigation into the design of an evolutionary mechanism for multiagent protocol constraint optimisation. Starting with a review of common population based mechanisms we discuss the properties of the mechanisms used by these search methods. We derive a novel algorithm for optimisation of vectors of real numbers and empirically validate the efficacy of the design by comparing against well known results from the literature. We discuss the application of an optimiser to a novel problem and remark upon the relevance of the no free lunch theorem. We show the relative performance of the optimiser is strong and publish details of a new best result for the Keane optimisation problem. We apply the final algorithm to the multi-agent protocol optimisation problem and show the design process was successful

Pseudo-contractions as Gentle Repairs

Updating a knowledge base to remove an unwanted consequence is a challenging task. Some of the original sentences must be either deleted or weakened in such a way that the sentence to be removed is no longer entailed by the resulting set. On the other hand, it is desirable that the existing knowledge be preserved as much as possible, minimising the loss of information. Several approaches to this problem can be found in the literature. In particular, when the knowledge is represented by an ontology, two different families of frameworks have been developed in the literature in the past decades with numerous ideas in common but with little interaction between the communities: applications of AGM-like Belief Change and justification-based Ontology Repair. In this paper, we investigate the relationship between pseudo-contraction operations and gentle repairs. Both aim to avoid the complete deletion of sentences when replacing them with weaker versions is enough to prevent the entailment of the unwanted formula. We show the correspondence between concepts on both sides and investigate under which conditions they are equivalent. Furthermore, we propose a unified notation for the two approaches, which might contribute to the integration of the two areas

New Foundation in the Sciences: Physics without sweeping infinities under the rug

It is widely known among the Frontiers of physics, that “sweeping under the rug” practice has been quite the norm rather than exception. In other words, the leading paradigms have strong tendency to be hailed as the only game in town. For example, renormalization group theory was hailed as cure in order to solve infinity problem in QED theory. For instance, a quote from Richard Feynman goes as follows: “What the three Nobel Prize winners did, in the words of Feynman, was to get rid of the infinities in the calculations. The infinities are still there, but now they can be skirted around . . . We have designed a method for sweeping them under the rug. [1] And Paul Dirac himself also wrote with similar tune: “Hence most physicists are very satisfied with the situation. They say: Quantum electrodynamics is a good theory, and we do not have to worry about it any more. I must say that I am very dissatisfied with the situation, because this so-called good theory does involve neglecting infinities which appear in its equations, neglecting them in an arbitrary way. This is just not sensible mathematics. Sensible mathematics involves neglecting a quantity when it turns out to be small—not neglecting it just because it is infinitely great and you do not want it!”[2] Similarly, dark matter and dark energy were elevated as plausible way to solve the crisis in prevalent Big Bang cosmology. That is why we choose a theme here: New Foundations in the Sciences, in order to emphasize the necessity to introduce a new set of approaches in the Sciences, be it Physics, Cosmology, Consciousness etc