Affect control processes: Intelligent affective interaction using a partially observable Markov decision process

Affect control processes: Intelligent affective interaction using a partially observable Markov decision process

This paper describes a novel method for building affectively intelligent human-interactive agents. The method is based on a key sociological insight that has been developed and extensively verified over the last twenty years, but has yet to make an impact in artificial intelligence. The insight is that resource bounded humans will, by default, act to maintain affective consistency. Humans have ...

Toward affective dialogue management using partially observable Markov decision processes

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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 ...

Partially observable Markov decision processes

For reinforcement learning in environments in which an agent has access to a reliable state signal, methods based on the Markov decision process (MDP) have had many successes. In many problem domains, however, an agent suffers from limited sensing capabilities that preclude it from recovering a Markovian state signal from its perceptions. Extending the MDP framework, partially observable Markov...

Dynamic Decision Model using Partially Observable Markov Decision Process