Preana: Game Theory Based Prediction with Reinforcement Learning

In this article, we have developed a game theory based prediction tool, named Preana, based on a promising model developed by Professor Bruce Beuno de Mesquita. The first part of this work is dedicated to exploration of the specifics of Mesquita’s algorithm and reproduction of the factors and features that have not been revealed in literature. In addition, we have developed a learning mechanism to model the players’ reasoning ability when it comes to taking risks. Preana can predict the outcome of any issue with multiple steak-holders who have conflicting interests in economic, business, and political sciences. We have utilized game theory, expected utility theory, Median voter theory, probability distribution and reinforcement learning. We were able to reproduce Mesquita’s reported results and have included two case studies from his publications and compared his results to that of Preana. We have also applied Preana on Irans 2013 presidential election to verify the accuracy of the prediction made by Preana

quantification of coarse aggregate angularity by a newly developed auto grader machine

The physical properties of aggregates have a direct correlation to the performance of a pavement. Stiffness, fatigue response, shear resistance and permanent deformation are some of the distresses for which aggregate form, texture and angularity have an influence. Angularity is an important property of aggregate shape, more angular are the particles there will be better interlocking, inter friction and greater mechanical stability, hence better pavement distress resistance. A debate has a risen over several methods to capture this physical property either directly or indirectly such as aggregate imaging system (AIMS), Un compacted void content of coarse aggregate (AASHTO T326), University of Illinois Aggregate Image Analyzer (UIAIA) and Indian manual coarse aggregate angularity test. Some are costly some are laborious and time consuming; hence there is a need for better methods that are cost effective, accurate, rapid in measuring aggregate angularity. The research conducted in this study introduces cost effective Aggregate Auto-grader and evaluates the effective set of time and speed for this automated machine to obtain minimum percentage air voids between aggregates (estimation of perfect interlocking) by shaking sample of coarse aggregates in orbital motion. In addition to measure accuracy of automate Aggregate Auto-grader test results are compared to other manual coarse aggregate angularity test. The trend followed by results of aggregate Auto-grader is as same as the manual test, hence based on results a new equation is proposed for obtaining coarse aggregate angularity by Aggregate Auto-grade machine which has more accuracy, reputability and reproducibility compare to the manual test

LINGUISTIC ENTRAINMENT IN MULTI-PARTY SPOKEN DIALOGUES

Entrainment is the propensity of speakers to begin behaving like one another in conversations. Evidence of entrainment has been found in multiple aspects of speech, including acoustic-prosodic and lexical. More interestingly, the strength of entrainment has been shown to be associated with numerous conversational qualities, such as social variables. These two characteristics make entrainment an interesting research area for multiple disciplines, such as natural language processing and psychology. To date, mainly simple methods such as unweighted averaging have been used to move from pairs to groups, and the focus of prior multi-party work has been on text rather than speech (e.g., Wikipedia, Twitter, online forums, and corporate emails). The focus of this research, unlike previous studies, is multi-party spoken dialogues. The goal of this work is to develop, validate, and evaluate multi-party entrainment measures that incorporate characteristics of multi-party interactions, and are associated with measures of team outcomes. In this thesis, first, I explore the relation between entrainment on acoustic-prosodic and lexical features and show that they correlate. In addition, I show that a multi-modal model using entrainment features from both of these modalities outperforms the uni-modal model at predicting team outcomes. Moreover, I present enhanced multi-party entrainment measures which utilize dynamics of entrainment in groups for both global and local settings. As for the global entrainment, I present a weighted convergence based on group dynamics. As the first step toward the development of local multi-party measures, I investigate whether local entrainment occurs within a time-lag in groups using a temporal window approach. Next, I propose a novel approach to learn a vector representation of multi-party local entrainment by encoding the structure of the presented multi-party entrainment graphs. The positive results of both the global and local settings indicate the importance of incorporating entrainment dynamics in groups. Finally, I propose a novel approach to incorporate a team-level factor of gender-composition to enhance multi-party entrainment measures. All of the proposed works are in the direction of enhancing multi-party entrainment measures with the focus on spoken dialogues although they can also be employed on text-based communications