Decision Making for Affective Agents in Assistive Environments

In this paper, we discuss the Decision Making and Learning ability of Affective Agents to make human-like decisions. This work is in the context of Assistive Living Environments (ALE) applications, where an agent is capable of assisting a human in physical and cognitive rehabilitation through multimodal and adaptive interaction. The goal of this research is to investigate what role multimodality plays in producing a natural and effective interaction using Reinforcement Learning. We propose a hierarchical decision making framework for affective agents doing complex tasks. This framework incorporates an internal reward mechanism to make the learning more efficient.This paper was presented at the Doctoral Consortium of the IVA 2015 conferenc