Aprendizaje de estrategias de decisión utilizando redes neuronales artificiales en juegos repetitivos no cooperativos en el ámbito de la economía evolucionista

El presente proyecto de investigación muestra el aprendizaje de estrategias de decisión utilizando redes neuronales artifiiales en juegos repetitivos no cooperativos, especficamente, se modelaron los juegos no cooperativos: dilema del prisionero, juego de la gallina y caza del ciervo. En la configuración de los juegos se presentan varios escenarios a saber: competencias entre agentes cuyos programas corresponde con estrategias de juegos usadas en competencias de juego no cooperativos, competencia entre agentes cuyo programa corresponde con una red neuronal obtenida a través de procesos de neuroevolución, y por último, competencia entre agentes cuyo programa corresponde con redes neuronales que se adaptan en línea. Con fin de tener un esquema de especificación unificado, adicionalmente, se planteó el desarrollo de un laboratorio computacional en el ámbito de la economía computacional basado en agentes, dicho laboratorio permite la especificación de modelos de simulación usando un lenguaje desarrollado denominado UNALCOL. El lenguaje tiene una serie de características entre las cuales se encuentran: un entorno integrado de desarrollo que facilita las tareas de programación y una plataforma de simulación para los modelos especifiados. Un elemento importante de dicho lenguaje es que permite la integración con librerías externas para soportar el proceso de toma de decisiones. Los resultados del proceso de investigación indican que pueden ser especifiados juegos no cooperativos en UNALCOL, lo anterior, dado el correcto funcionamiento de las simulaciones realizadas con los juegos dilema del prisionero, juego de la gallina y caza del ciervo. Adicionalmente, el proceso de evolución de las redes neuronales (perceptron multicapa, red de base radial) desarrollado con el fin de adaptar estrategias de aprendizaje en los agentes cuando compiten en los juegos no cooperativos, son comparables a los resultados obtenidos en la literatura, usando algoritmos genéticos y enjambres de partículas. Por último, el proceso de evolución de estrategias en línea, basado en redes neuronales, integrado a los agentes cuando compite con otros contrincantes garantiza el cambio de la estrategia de juego con el fín de maximizar el puntaje obtenido.Abstract. This research project studies the learning of decision making strategies using artificial neural networks in Repetitive, Non-Cooperative games. In this particular case, the following non-cooperative games were modeled: Prisoner's Dilemma, Chicken Game and Stag Hunt. In each game setup the following scenarios can be seen: competition between agents whose programming corresponds to a Neural Network obtained through Neuroevolution procedures and also, competition between agents whose programming corresponds to Neural Networks which adapt online. In order to obtain a unified specification diagram, development of a computational laboratory dealing with agent based computational economy was proposed. The experiments performed through this laboratory will allow specification of simulation models using a previously developed language called UNALCOL. This language has the following characteristics: an integrated development environment which facilitates programming tasks, and a simulation platform for specified models. An important characteristic of this language is that it allows integration with external libraries to support the decision making process. The research process' results indicate that Non-cooperative games can be specified in UNALCOL as long as the simulations made with Prisoner's Dilemma, Chicken Game and Stag Hunt are functioning properly. Additionally, the neural network evolutionary process (Multilayered perceptron, radial basis network) developed in order to adapt learning strategies in the agents when they compete in Non-cooperative games is compatible with the results obtained in textbooks using genetic algorithms and particle swarms. Finally, the evolutionary process of online strategies based on Neural Networks, integrated to agents when they compete against each other guarantees game strategy changes in order to maximize the final score.Maestrí

An Evaluation of Performance Enhancements to Particle Swarm Optimisation on Real-World Data

Swarm Computation is a relatively new optimisation paradigm. The basic premise is to model the collective behaviour of self-organised natural phenomena such as swarms, flocks and shoals, in order to solve optimisation problems. Particle Swarm Optimisation (PSO) is a type of swarm computation inspired by bird flocks or swarms of bees by modelling their collective social influence as they search for optimal solutions. In many real-world applications of PSO, the algorithm is used as a data pre-processor for a neural network or similar post processing system, and is often extensively modified to suit the application. The thesis introduces techniques that allow unmodified PSO to be applied successfully to a range of problems, specifically three extensions to the basic PSO algorithm: solving optimisation problems by training a hyperspatial matrix, using a hierarchy of swarms to coordinate optimisation on several data sets simultaneously, and dynamic neighbourhood selection in swarms. Rather than working directly with candidate solutions to an optimisation problem, the PSO algorithm is adapted to train a matrix of weights, to produce a solution to the problem from the inputs. The search space is abstracted from the problem data. A single PSO swarm optimises a single data set and has difficulties where the data set comprises disjoint parts (such as time series data for different days). To address this problem, we introduce a hierarchy of swarms, where each child swarm optimises one section of the data set whose gbest particle is a member of the swarm above in the hierarchy. The parent swarm(s) coordinate their children and encourage more exploration of the solution space. We show that hierarchical swarms of this type perform better than single swarm PSO optimisers on the disjoint data sets used. PSO relies on interaction between particles within a neighbourhood to find good solutions. In many PSO variants, possible interactions are arbitrary and fixed on initialisation. Our third contribution is a dynamic neighbourhood selection: particles can modify their neighbourhood, based on the success of the candidate neighbour particle. As PSO is intended to reflect the social interaction of agents, this change significantly increases the ability of the swarm to find optimal solutions. Applied to real-world medical and cosmological data, this modification is and shows improvements over standard PSO approaches with fixed neighbourhoods