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

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.