Inferring Case-Based Reasoners’ Knowledge to Enhance Interactivity

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A4 Artikkeli konferenssijulkaisussa
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Date

2021

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Department of Computer Science

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Language

en

Pages

15

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Case-Based Reasoning Research and Development - 29th International Conference, ICCBR 2021, Proceedings, pp. 171-185, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; Volume 12877 LNAI

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.

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Publisher Copyright: © 2021, Springer Nature Switzerland AG.

Keywords

Bayesian Inference for CBR, Machine learning for CBR, User modelling

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DOI

10.1007/978-3-030-86957-1_12

Citation

Murena, P A & Al-Ghossein, M 2021, Inferring Case-Based Reasoners’ Knowledge to Enhance Interactivity . in A A Sánchez-Ruiz & M W Floyd (eds), Case-Based Reasoning Research and Development - 29th International Conference, ICCBR 2021, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12877 LNAI, Springer, pp. 171-185, International Conference on Case-Based Reasoning, Virtual, Online, 13/09/2021 . https://doi.org/10.1007/978-3-030-86957-1_12

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https://urn.fi/URN:NBN:fi:aalto-2021110410009

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[article-cris] Perustieteiden korkeakoulu / SCI