Inferring Capabilities from Task Performance with Bayesian Triangulation

• URL: http://arxiv.org/abs/2309.11975v1
• Date: Thu, 21 Sep 2023 11:19:26 GMT
• Title: Inferring Capabilities from Task Performance with Bayesian Triangulation
• Authors: John Burden, Konstantinos Voudouris, Ryan Burnell, Danaja Rutar, Lucy Cheke, Jos\'e Hern\'andez-Orallo
• Abstract summary: We describe a method to infer the cognitive profile of a system from diverse experimental data. These features must be triangulated in complex ways to be able to infer capabilities from non-populational data. We showcase the potential for capability-oriented evaluation.
• Score: 11.418934051317411
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As machine learning models become more general, we need to characterise them in richer, more meaningful ways. We describe a method to infer the cognitive profile of a system from diverse experimental data. To do so, we introduce measurement layouts that model how task-instance features interact with system capabilities to affect performance. These features must be triangulated in complex ways to be able to infer capabilities from non-populational data -- a challenge for traditional psychometric and inferential tools. Using the Bayesian probabilistic programming library PyMC, we infer different cognitive profiles for agents in two scenarios: 68 actual contestants in the AnimalAI Olympics and 30 synthetic agents for O-PIAAGETS, an object permanence battery. We showcase the potential for capability-oriented evaluation.

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