Investigating the Effects of Learning Speeds on Xpilot Agent Evolution

In this paper we present a comparison of the effects of varying play speeds on a genetic algorithm in the space combat game Xpilot. Xpilot-AI, an Xpilot add-on designed for testing learning systems, is used to evolve the controller for an Xpilot combat agent at varying frames per second to determine an optimal speed for learning. The controller is a rule-based system modified to work with a genetic algorithm that learns numeric parameters for the agent’s rule base. The goal of this research is to increase the quality and speed of standard learning algorithms in Xpilot as well as determine a suitable speed for employing Punctuated Anytime Learning (PAL) in the Xpilot-AI environment. PAL is the learning component of an overall system of autonomous agent control with real-time learning

Evolving Expert Agent Parameters for Capture the Flag Agent in Xpilot

Xpilot is an open source, 2d space combat game. Xpilot-AI allows a programmer to write scripts that control an agent playing a game of Xpilot. It provides a reasonable environment for testing learning systems for autonomous agents, both video game agents and robots. In previous work, a wide range of techniques have been used to develop controllers that are focused on the combat skills for an Xpilot agent. In this research, a Genetic Algorithm (GA) was used to evolve the parameters for an expert agent solving the more challenging problem of capture the flag

Fitness Biasing for Evolving an Xpilot Combat Agent

In this paper we present an application of Fitness Biasing, a type of Punctuated Anytime Learning, for learning autonomous agents in the space combat game Xpilot. Fitness Biasing was originally developed as a means of linking the model to the actual robot in evolutionary robotics. We use fitness biasing with a standard genetic algorithm to learn control programs for a video game agent in real-time. Xpilot-AI, an Xpilot add-on designed for testing learning systems, is used to evolve the controller in the background while periodic checks in normal game play are used to compensate for errors produced by running the system at a high frame rate. The resultant learned controllers are comparable to our best hand-coded Xpilot-AI bots, display complex behavior that resemble human strategies, and are capable of adapting to a changing enemy in real-time

Punctuated Anytime Learning and the Xpilot-AI Combat Environment

In this paper, research is presented on an application of Punctuated Anytime Learning with Fitness Biasing, a type of computational intelligence and evolutionary learning, for real-­time learning of autonomous agents controllers in the space combat game Xpilot. Punctuated Anytime Learning was originally developed as a means of effective learning in the field of evolutionary robotics. An analysis was performed on the game environment to determine optimal environmental settings for use during learning, and Fitness Biasing is employed using this information to learn intelligent behavior for a video game agent controller in real-­time. Xpilot-­AI, an Xpilot add-­on designed for testing learning systems, is used alongside evolutionary learning techniques to evolve optimal behavior in the background while periodic checks in normal game play are used to compensate for errors produced by running the system at a high frame rate. The resultant learned controllers are comparable to our best hand-­coded Xpilot-­AI agents, display complex behavior that resemble human strategies, and are capable of adapting to a changing enemy in real-­time. The work presented in this paper is also general enough to further the development of Punctuated Anytime Learning in evolutionary robotic systems

An analysis of connectivity

Recent evidence in biology indicates crossmodal, which is to say information sharing between the different senses, influences in the brain. This helps to explain such phenomenon as the McGurk effect, where even though a person knows that he is seeing the lip movement “GA” and is hearing the sound “BA”, the person usually can’t help but think that they are hearing the sound “DA”. The McGurk effect is an example of where the visual sense influences the perception of the audio sense. These discoveries transition old feedforward models of the brain to ones that rely on feedback connections and, more recently, crossmodal connections. Although we have many software systems that rely on some form of intelligence, i.e. person recognition software, speech to text software, etc, very few take advantage of crossmodal influences. This thesis provides an analysis of the importance of connections between explicit modalities in a recurrent neural network model. Each modality is represented as an individual recurrent neural network. The connections between the modalities and the modalities themselves are trained by applying a genetic algorithm to generate a population of the full model to solve certain types of classification problems. The main contribution of this work is to experimentally show the relative importance of feedback and crossmodal connections. From this it can be argued that the utilization of crossmodal information at an earlier stage of decision making can boost the accuracy and reliability of intelligent systems

“Modelo dinámico del robot Xpilot-AI” “Xpilot-AI Bot’s dynamic model”

Determinar el modelo dinámico del robot X Pilot AIEl control de robots es un gran campo de aplicación de la ingeniería. Estos pueden ser encontrados tanto en sistemas físicos como informáticos. Sin embargo, existen diferentes plataformas que no necesariamente son de código abierto, por lo que acceder a una licencia puede ser muy significativo. Xpilot presenta una librería de código abierto que trabaja con diferentes lenguajes de programación de código abierto y una manera fácil de aprendizaje de diferentes tipos de habilidades para los estudiantes, especialmente para aquellos que desean aprender a programar diferentes estrategias de control. Por otra parte, Xpilot AI, permite programar bots de manera que los mismos puedan realizar diferentes tipos de acciones en el juego, en muchos de los casos se hace utilidad de IA (Inteligencia Artificial). Además, Xpilot-AI ofrece una amplia gama de bots para lograr comprender como funcionan los diferentes comandos. El problema de moverse desde un punto A hasta un punto B, es el que se presenta en el actual trabajo. Para la resolución de este problema se hace utilidad de diferentes herramientas informáticas y matemáticas, así como también la información de muchas fuentes bibliográficas. Para que el performance del bot sea el ´optimo, se considera una máquina de estados que contiene todas las condiciones para que el mismo se desempeñe en su entorno informático y pueda lograr con su cometido. Estas condiciones hacen que el robot actúe de diferente manera frente a las situaciones que existen dentro del juego. Además, se utiliza la plataforma Raspberry Pi 3 modelo B, para montar un servidor al que el bot es capaz de conectarse de manera remota. Dicho servidor también es distribuido por Xpilot y está disponible para su libre descarga desde su página web. Este servidor puede utilizarse en un mismo terminal de requerirse el caso.Ingenierí

Enhancing player experience in computer games: A computational Intelligence approach.

Ph.DDOCTOR OF PHILOSOPH

Machine Learning for Architectural Design Space Exploration and Resource Control

Machine learning has enabled significant advancements in diverse fields, yet, with a few exceptions, has had limited impact on computer architecture. Recent work, however, has begun to explore broader application to design, optimization, and simulation. Notably, machine-learning-based strategies often surpass prior state-of-the-art analytical, heuristic, and human-expert approaches. This thesis first reviews existing work applying machine learning to architecture, ranging from simulation and run-time optimization, to individual component design involving the memory system, branch predictors, networks-on-chip, and GPUs. Next, the thesis presents a novel deep-reinforcement-learning framework for design space exploration. Finally, the thesis introduces an innovative strategy for resource optimization with multiple co-scheduled workloads. Taken together, these works present a promising future for machine-learning-based architectural design