Research Mentor(s)
Deneke, Wesley
Description
VikingBot is an automated AI that plays StarCraft by using a combination of machine learning and artificial intelligence. High level strategies are planned using the Brown-UMBC Reinforcement Learning and Planning (BURLAP), library which implements planning algorithms and provides interfaces for defining a domain and models of that domain for planning. For the planning, we used the BURLAP implementation of the sparse sampling algorithm because the time complexity is independent of the size of the state space, and we have to plan quickly in real time. SARSA reinforcement learning is used for a machine learning model that controls combat units. Various other helping functions are distributed to agent classes that aid in the AI in the different areas of the game. These agents are categorized as strategy, economy, combat, and intelligence. By using these parts in tandem VikingBot aims to use less training resources and still be able to play games at a high enough level to beat a human player.
Document Type
Event
Start Date
18-5-2020 12:00 AM
End Date
22-5-2020 12:00 AM
Department
Computer Science
Genre/Form
student projects, posters
Subjects – Topical (LCSH)
StarCraft; Video games; Artificial intelligence
Type
Image
Keywords
Machine Learning, AI, StarCraft
Rights
Copying of this document in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this document for commercial purposes, or for financial gain, shall not be allowed without the author’s written permission.
Language
English
Format
application/pdf
Included in
VikingBot: The StarCraft Artificial Intelligence
VikingBot is an automated AI that plays StarCraft by using a combination of machine learning and artificial intelligence. High level strategies are planned using the Brown-UMBC Reinforcement Learning and Planning (BURLAP), library which implements planning algorithms and provides interfaces for defining a domain and models of that domain for planning. For the planning, we used the BURLAP implementation of the sparse sampling algorithm because the time complexity is independent of the size of the state space, and we have to plan quickly in real time. SARSA reinforcement learning is used for a machine learning model that controls combat units. Various other helping functions are distributed to agent classes that aid in the AI in the different areas of the game. These agents are categorized as strategy, economy, combat, and intelligence. By using these parts in tandem VikingBot aims to use less training resources and still be able to play games at a high enough level to beat a human player.