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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Parker, G. Fritzsche, P. |
| Copyright Year | 2011 |
| Description | Author affiliation: Computer Science, Connecticut College, New London, USA (Parker, G.; Fritzsche, P.) |
| Abstract | 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. |
| Starting Page | 2561 |
| Ending Page | 2566 |
| File Size | 532534 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781457706523 |
| ISSN | 1062922X |
| e-ISBN | 9781457706530 |
| DOI | 10.1109/ICSMC.2011.6084062 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-10-09 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Games Marine vehicles Genetic algorithms Real time systems Servers Learning systems Radar tracking Autonomous system control Xpilot Xpilot-AI Punctuated Anytime Learning Fitness Biasing Genetic Algorithm Real-time Learning Video Game Learning On-line learning |
| Content Type | Text |
| Resource Type | Article |
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