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Inferring Case-Based Reasoners’ Knowledge to Enhance Interactivity
Publikationstyp
Conference Paper
Date Issued
2021-09
Sprache
English
Author(s)
Volume
12877 LNAI
Start Page
171
End Page
185
Citation
29th International Conference on Case-Based Reasoning (ICCBR 2021)
Contribution to Conference
Publisher DOI
Scopus ID
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.
Subjects
Bayesian Inference for CBR
Machine learning for CBR
User modelling