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  Cognitive User Modeling Computed by a Proposed Dialogue Strategy Based on an Inductive Game Theory

Asai, H., Koshizen, T., Watanabe, M., Tsujino, H., & Aihara, K. (2005). Cognitive User Modeling Computed by a Proposed Dialogue Strategy Based on an Inductive Game Theory. In B. Apolloni, A. Ghosh, F. Alpaslan, L. Jain, & S. Patnaik (Eds.), Machine Learning and Robot Perception (pp. 325-351). Berlin, Germany: Springer.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D6F1-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-3ABB-8
Genre: Book Chapter

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 Creators:
Asai, H, Author
Koshizen, T, Author
Watanabe, M1, Author              
Tsujino, H, Author
Aihara, K, Author
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: This paper advocates the concept of user modeling (UM), which involves dialogue strategies. We focus on human-machine collaboration, which is endowed with human-like capabilities and in this regard, UM could be related to cognitive modeling, which deals with issues of perception, behavioral decision and selective attention by humans. In our UM, approximating a pay-off matrix or function will be the method employed in order to estimate user’s pay-offs, which is basically calculated by user’s action. Our proposed computation method allows dialogue strategies to be determined by maximizing mutual expectations of the pay-off matrix. We validated the proposed computation using a social game called “Iterative Prisoner’s Dilemma (IPD)” that is usually used for modeling social relationships based on reciprocal altruism. Furthermore, we also allowed the pay-off matrix to be used with a probability distribution function. That is, we assumed that a person’s pay-off could fluctuate over time, but that the fluctuation could be utilized in order to avoid dead reckoning in a true pay-off matrix. Accordingly, the computational structure is reminiscent of the regularization implicated by the machine learning theory. In a way, we are convinced that the crucial role of dialogue strategies is to enable user models to be smoother by approximating probabilistic pay-off functions. That is, their user models can be more accurate or more precise since the dialogue strategies induce the on-line maintenance of models. Consequently, our improved computation allowing the pay-off matrix to be treated as a probabilistic density function has led to better performance, Because the probabilistic pay-off function can be shifted in order to minimize error between approximated and true pay-offs in others. Moreover, our results suggest that in principle the proposed dialogue strategy should be implemented to achieve maximum mutual expectation and uncertainty reduction regarding pay-offs for others. Our work also involves analogous correspondences on the study of pattern regression and user modeling in accordance with machine learning theory.

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 Dates: 2005
 Publication Status: Published in print
 Pages: -
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 Rev. Method: -
 Identifiers: DOI: 10.1007/11504634_8
BibTex Citekey: 5806
 Degree: -

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Title: Machine Learning and Robot Perception
Source Genre: Book
 Creator(s):
Apolloni, B, Editor
Ghosh, A, Editor
Alpaslan, F, Editor
Jain, LC, Editor
Patnaik, S, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 325 - 351 Identifier: ISBN: 978-3-540-26549-8

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Title: Studies in Computational Intelligence
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Pages: - Volume / Issue: 7 Sequence Number: - Start / End Page: - Identifier: -