  Item response theory (IRT) can be applied to the analysis of the evaluation
of results from AI benchmarks. The two-parameter IRT model provides two
indicators (difficulty and discrimination) on the side of the item (or AI
problem) while only one indicator (ability) on the side of the respondent (or
AI agent). In this paper we analyse how to make this set of indicators dual, by
adding a fourth indicator, generality, on the side of the respondent.
Generality is meant to be dual to discrimination, and it is based on
difficulty. Namely, generality is defined as a new metric that evaluates
whether an agent is consistently good at easy problems and bad at difficult
ones. With the addition of generality, we see that this set of four key
indicators can give us more insight on the results of AI benchmarks. In
particular, we explore two popular benchmarks in AI, the Arcade Learning
Environment (Atari 2600 games) and the General Video Game AI competition. We
provide some guidelines to estimate and interpret these indicators for other AI
benchmarks and competitions.
