Conversational systems and robots that use reinforcement learning for policy optimization in large domains often face the problem of limited scalability. This problem has been addressed either by using function approximation techniques that estimate the approximate true value function of a policy or by using a hierarchical decomposition of a learning task into subtasks. We present a novel appro...

This paper introduces the understanding system HAM-RPM, which simulates a dialogue partner conversing about a real-life domain. After outlining the system's overall structure, we discuss three of its distinguishing features: The first is its organisation of spatial data in a redundant multiple data base, inspired by certain aspects characteristic of visual search in humans. A new algorithm for ...

We present a prototype of a novel interactive food recommender for groups of users that supports groups in planning their meals through a conversational process based on critiquing. The system comprises two novel elements: a user interface and interaction design based on tagging and critiquing, and a utility function incorporating healthiness and diet compliance factors.

In this paper we survey the role of humor in human-to-human interaction with the aim to see whether it is useful for embodied conversational agents to integrate humor capabilities in their internal model of intelligence, emotions and interaction (verbal and nonverbal) capabilities. For that reason we shortly survey the current state of the art of research in embodied conversational agents, affe...

Many online human activities leave digital traces that are recorded in natural-language format. The exploitation of this unprecedented resource under a computational framework can bring a phase transition in our understanding of human social behavior and shape the future of social media systems. This thesis describes a computational approach to an intriguing aspect of conversational behavior, l...