Affective-Cognitive learning and decision making: a motivational reward framework for affective agents

Authors:
Hyungil Ahn;Rosalind W. Picard
Affiliations:
MIT Media Lab, Cambridge, MA;MIT Media Lab, Cambridge, MA
Venue:
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Year:
2005

Citing 2
Cited 11

Affective computing

Affective computing
Affective Learning — A Manifesto

BT Technology Journal

Empathic tutoring software agents using real-time eye tracking

Proceedings of the 2006 symposium on Eye tracking research & applications
User model in multiplayer mixed reality entertainment applications

Proceedings of the 2006 ACM SIGCHI international conference on Advances in computer entertainment technology
User model in multiplayer mixed reality entertainment applications

Proceedings of the 2006 ACM SIGCHI international conference on Advances in computer entertainment technology
Virtual reality and mixed reality for virtual learning environments

Computers and Graphics
Experience-based learning mechanism for neural controller adaptation: application to walking biped robots

IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
An emotionally biased ant colony algorithm for pathfinding in games

Expert Systems with Applications: An International Journal
Intelligent user profiling

Artificial intelligence
Design of autonomous navigation system based on affective cognitive learning and decision-making

ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
A study of adaptive locomotive behaviors of a biped robot: patterns generation and classification

SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
An emotion model for virtual agents with evolvable motivation

Transactions on edutainment VI
Affective and cognitive design for mass personalization: status and prospect

Journal of Intelligent Manufacturing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present a new computational framework of affective-cognitive learning and decision making for affective agents, inspired by human learning and recent neuroscience and psychology. In the proposed framework ‘internal reward from cognition and emotion’ and ‘external reward from the external world’ serve as motivation in learning and decision making. We construct this model, integrating affect and cognition, with the aim of enabling machines to make smarter and more human-like decisions for better human-machine interactions.