Inferring Case-Based Reasoners’ Knowledge to Enhance Interactivity

Abstract

When interacting with a human user, an artificial intelligence needs to have a clear model of the human’s behaviour to make the correct decisions, be it recommending items, helping the user in a task or teaching a language. In this paper, we explore the feasibility of modelling the human as a case-based reasoning agent through the question of how to infer the state of a CBR agent from interaction data. We identify the main parameters to be inferred, and propose a Bayesian belief update as a possible way to infer both the parameters of the agent and the content of their case base. We illustrate our ideas with the simple application of an agent learning grammar rules throughout a sequence of observations.