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https://www.um.edu.mt/library/oar/handle/123456789/82025
Title: | A continuous information gain measure to find the most discriminatory problems for AI benchmarking |
Authors: | Stephenson, Matthew Anderson, Damien Khalifa, Ahmed Levine, John Renz, Jochen Togelius, Julian Salge, Christoph |
Keywords: | Computer games -- Design Multiagent systems Artificial intelligence |
Issue Date: | 2020 |
Publisher: | Institute of Electrical and Electronics Engineers |
Citation: | Stephenson, M., Anderson, D., Khalifa, A., Levine, J., Renz, J., Togelius, J., & Salge, C. (2020). A continuous information gain measure to find the most discriminatory problems for AI benchmarking. 2020 IEEE Congress on Evolutionary Computation (CEC), Glasgow. |
Abstract: | This paper introduces an information-theoretic method for selecting a subset of problems which gives the most information about a group of problem-solving algorithms. This method was tested on the games in the General Video Game AI (GVGAI) framework, allowing us to identify a smaller set of games that still gives a large amount of information about the abilities of different game-playing agents. This approach can be used to make agent testing more efficient. We can achieve almost as good discriminatory accuracy when testing on only a handful of games as when testing on more than a hundred games, something which is often computationally infeasible. Furthermore, this method can be extended to study the dimensions of the effective variance in game design between these games, allowing us to identify which games differentiate between agents in the most complementary ways. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/82025 |
Appears in Collections: | Scholarly Works - InsDG |
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File | Description | Size | Format | |
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A_Continuous_Information_Gain_Measure_to_Find_the_Most_Discriminatory_Problems_for_AI_Benchmarking_2020.pdf Restricted Access | 925.07 kB | Adobe PDF | View/Open Request a copy |
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