Toward Affective Dialogue Modeling using Partially Observable Markov Decision Processes

We propose a novel approach to developing a dialogue model which is able to take into account some aspects of the user’s emotional state and acts appropriately. The dialogue model uses a Partially Observable Markov Decision Process approach with observations composed of the observed user’s emotional state and action. A simple example of route navigation is explained to clarify our approach and preliminary results & future plans are briefly discussed

On combining the facial movements of a talking head

We present work on Obie, an embodied conversational agent framework. An embodied conversational agent, or talking head, consists of three main components. The graphical part consists of a face model and a facial muscle model. Besides the graphical part, we have implemented an emotion model and a mapping from emotions to facial expressions. The animation part of the framework focuses on the combination of different facial movements temporally. In this paper we propose a scheme of combining facial movements on a 3D talking head

Gaze Behavior, Believability, Likability and the iCat

The iCat is a user-interface robot with the ability to express a range of emotions through its facial features. This paper summarizes our research whether we can increase the believability and likability of the iCat for its human partners through the application of gaze behaviour. Gaze behaviour serves several functions during social interaction such as mediating conversation flow, communicating emotional information and avoiding distraction by restricting visual input. There are several types of eye and head movements that are necessary for realizing these functions. We designed and evaluated a gaze behaviour system for the iCat robot that implements realistic models of the major types of eye and head movements found in living beings: vergence, vestibulo ocular reflexive, smooth pursuit movements and gaze shifts. We discuss how these models are integrated into the software environment of the iCat and can be used to create complex interaction scenarios. We report about some user tests and draw conclusions for future evaluation scenarios

A Reinforcement Learning Agent for Minutiae Extraction from Fingerprints

In this paper we show that reinforcement learning can be used for minutiae detection in fingerprint matching. Minutiae are characteristic features of fingerprints that determine their uniqueness. Classical approaches use a series of image processing steps for this task, but lack robustness because they are highly sensitive to noise and image quality. We propose a more robust approach, in which an autonomous agent walks around in the fingerprint and learns how to follow ridges in the fingerprint and how to recognize minutiae. The agent is situated in the environment, the fingerprint, and uses reinforcement learning to obtain an optimal policy. Multi-layer perceptrons are used for overcoming the difficulties of the large state space. By choosing the right reward structure and learning environment, the agent is able to learn the task. One of the main difficulties is that the goal states are not easily specified, for they are part of the learning task as well. That is, the recognition of minutiae has to be learned in addition to learning how to walk over the ridges in the fingerprint. Results of successful first experiments are presented

Corporate Governance and Acquisitions: Acquirer Wealth Effects in the Netherlands

We examine 865 acquisitions by Dutch industrial firms over the period 1993–2004. Theoretical work based on principal–agent problems predicts that managers of exchange-listed corporations may pursue acquisitions even when these do not add value for the shareholders. Corporate governance structures serve to constrain managers in their acquisition activity. In this chapter we measure the shareholder wealth effects of acquisitions and the factors that determine these wealth effects, including the governance characteristics of corporations. Firms in the Netherlands are interesting from the perspective of corporate governance, because the managerial board has a relatively strong position vis-à-vis shareholders. Several takeover defenses commonly used in the Netherlands not only limit shareholder influence during takeover battles, but also in absence of such fights. On the other hand, ownership is relatively concentrated, which may provide shareholders with the incentives and power to monitor the management. The average abnormal stock return following acquisition announcements is 1.1%, which is a significant positive effect. There is only a significant negative impact of the so-called structured regime, a situation where several shareholder rights are delegated to the supervisory board. This result suggests that governance improves acquisition decisions.The Netherlands;Corporate governance;Event study;Mergers & acquisitions