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

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