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Case Based Reasoning as a Model for Cognitive Artificial Intelligence

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Case-Based Reasoning Research and Development (ICCBR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11156))

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Abstract

Cognitive Systems understand the world through learning and experience. Case Based Reasoning (CBR) systems naturally capture knowledge as experiences in memory and they are able to learn new experiences to retain in their memory. CBR’s retrieve and reuse reasoning is also knowledge-rich because of its nearest neighbour retrieval and analogy-based adaptation of retrieved solutions. CBR is particularly suited to domains where there is no well-defined theory, because they have a memory of experiences of what happened, rather than why/how it happened. CBR’s assumption that ‘similar problems have similar solutions’ enables it to understand the contexts for its experiences and the ‘bigger picture’ from clusters of cases, but also where its similarity assumption is challenged. Here we explore cognition and meta-cognition for CBR through self-reflection and introspection of both memory and retrieve and reuse reasoning. Our idea is to embed and exploit cognitive functionality such as insight, intuition and curiosity within CBR to drive robust, and even explainable, intelligence that will achieve problem-solving in challenging, complex, dynamic domains.

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Notes

  1. 1.

    We use the term ‘understanding’ in the sense of ‘interpret in order to give meaning’ for the system involved.

  2. 2.

    EPSRC FIT Priority www.epsrc.ac.uk/research/ourportfolio/themes/ict/introduction/crossictpriorities/futureintelligenttechnologies/.

  3. 3.

    https://www.ibm.com/watson/services/knowledge-studio/.

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Acknowledgments

Susan Craw wishes to thank Stewart Massie for very helpful discussions of cognition and curiosity during the early stages of developing a research proposal on cognitive CBR that underpins this paper. Agnar Aamodt wishes to thank Enric Plaza for discussions of CBR, analogy, and cognition relevant to this paper, in preparing an invited talk at ICCBR 2017.

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Authors and Affiliations

  1. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, UK

    Susan Craw

  2. Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway

    Agnar Aamodt

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  1. Susan Craw
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  2. Agnar Aamodt
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Correspondence to Susan Craw .

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Editors and Affiliations

  1. Wright State University, Dayton, OH, USA

    Michael T. Cox

  2. Mälardalen University, Västeras, Sweden

    Peter Funk

  3. Mälardalen University, Västeras, Sweden

    Shahina Begum

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Craw, S., Aamodt, A. (2018). Case Based Reasoning as a Model for Cognitive Artificial Intelligence. In: Cox, M., Funk, P., Begum, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2018. Lecture Notes in Computer Science(), vol 11156. Springer, Cham. https://doi.org/10.1007/978-3-030-01081-2_5

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  • DOI: https://doi.org/10.1007/978-3-030-01081-2_5

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