This paper introduces an information-theoretic method for selecting a subset of problems which gives the most information about a group of problem-solving ...

This information gain measure can then be used to identify a benchmark set of games that provides us with the maximum information about our agents. While it is ...

A Continuous Information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarking ; be relative to the agents available. However, we believe the.

May 18, 2020 · Abstract—This paper introduces an information-theoretic method for selecting a subset of problems which gives the most.

This paper introduces an information-theoretic method for selecting a subset of problems which gives the most information about a group of problem-solving ...

Sep 3, 2020 · Dive into the research topics of 'A continuous information gain measure to find the most discriminatory problems for AI benchmarking'.

Data, Results and Code associated with the paper "A Continuous Information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarking" ...

A continuous information gain measure to find the most discriminatory problems for ai benchmarking. M Stephenson, D Anderson, A Khalifa, J Levine, J Renz, J ...

Artificial IntelligenceGame AIProcedural Content ... A continuous information gain measure to find the most discriminatory problems for ai benchmarking.

Title: A Continuous Information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarking. Authors: Matthew Stephenson, Damien Anderson ...