Name
Abstract | ||
|---|---|---|
Evaluating agents in decision-making applications requires assessing their skill and predicting their behaviour. Both are well developed in Poker-like situations, but less so in more complex game and model domains. This paper addresses both tasks by using Bayesian inference in a benchmark space of reference agents. The concepts are explained and demonstrated using the game of chess but the model applies generically to any domain with quantifiable options and fallible choice. Demonstration applications address questions frequently asked by the chess community regarding the stability of the rating scale, the comparison of players of different eras and/or leagues, and controversial incidents possibly involving fraud. The last include alleged under-performance, fabrication of tournament results, and clandestine use of computer advice during competition. Beyond the model world of games, the aim is to improve fallible human performance in complex, high-value tasks. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-12993-3_10 | ACG |
Keywords | Field | DocType |
alleged under-performance,bayesian inference,bayesian modelling,fallible choice,demonstration applications address question,chess community,complex game,model world,fallible human performance,model domain,poker-like situation,rating scale,human performance | Tournament,Bayesian inference,Simulation,Computer science,Rating scale,Artificial intelligence,World championship,Machine learning,Bayesian probability | Conference |
Volume | ISSN | ISBN |
6048 | 0302-9743 | 3-642-12992-7 |
Citations | PageRank | References |
7 | 0.84 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guy Haworth | 1 | 10 | 4.90 |
| Ken Regan | 2 | 7 | 0.84 |
| Giuseppe Di Fatta | 3 | 529 | 39.23 |