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DyBaNeM: Bayesian Episodic Memory Framework for Intelligent Virtual Agents

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Book cover Intelligent Virtual Agents (IVA 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8108))

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Abstract

Episodic Memory (EM) abilities are important for many types of intelligent virtual agents (IVAs). However, the few IVA–EM systems implemented to date utilize indexed logs of events as the underlying memory representation, which makes it hard to model some crucial facets of human memory, including hierarchical organization of episodes, reconstructive memory retrieval, and encoding of episodes with respect to previously learnt schemata. Here, we present a new general framework for EM modeling, DyBaNeM, which capitalizes on bayesian representation and, consequently, enables modeling these (and other) features easily. By means of a proof-of-concept implementation, we demonstrate that our approach to EM modeling is promising, at least for domains of moderate complexity.

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Authors and Affiliations

  1. Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic

    Rudolf Kadlec & Cyril Brom

Authors
  1. Rudolf Kadlec
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  2. Cyril Brom
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Editor information

Editors and Affiliations

  1. MACS, Heriot-Watt University, Riccarton, EH14 4AS, Edinburgh, UK

    Ruth Aylett

  2. Austrian Research Institute for Artificial Intelligence (OFAI), 1010, Vienna, Austria

    Brigitte Krenn

  3. CNRS-LTCI, Telecom-ParisTech, 75014, Paris, France

    Catherine Pelachaud

  4. School of Informatics, The University of Edinburgh, EH8 9LW, Edinburgh, UK

    Hiroshi Shimodaira

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Kadlec, R., Brom, C. (2013). DyBaNeM: Bayesian Episodic Memory Framework for Intelligent Virtual Agents. In: Aylett, R., Krenn, B., Pelachaud, C., Shimodaira, H. (eds) Intelligent Virtual Agents. IVA 2013. Lecture Notes in Computer Science(), vol 8108. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40415-3_2

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  • DOI: https://doi.org/10.1007/978-3-642-40415-3_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40414-6

  • Online ISBN: 978-3-642-40415-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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