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Effect of emotional feedback in a decision-making system for an autonomous agent
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2014 (English)In: Advances in Artificial Intelligence - IBERAMIA 2014: Proceedings / [ed] Ana L.C. Bazzan, Karim Pichara, Springer, 2014, 613-624 p.Conference paper, Published paper (Refereed)
Abstract [en]

The point of view of Isaac Asimov is unlikely in a close future, but machines that develop tasks in a sensible manner are already a fact. In light of this remark, recent research tries to understand the requirements and design options that imply providing an autonomous agent with means for detecting emotions. If we think about of exporting this model to machines, it is possible that they become capable to evolve emotionally according to such models and would take part in the society more or less cooperatively, according to the perceived emotional state. The main purpose of this research is the implementation of a decision model affected by emotional feedback in a cognitive robotic assistant that can capture information about the world around it. The robot will use multi-modal communication to assist the societal participation of persons deprived of conventional modes of communication. The aim is a machine that can predict what the user will do next and be ready to give the best possible assistance, taking in account the emotional factor. The results indicate the benefits and importance of emotional feedback in the closed loop human-robot interaction framework. Cognitive agents are shown to be capable of adapting to emotional information from humans.

Place, publisher, year, edition, pages
Springer, 2014. 613-624 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 8864
Keyword [en]
Affective Computing, Artificial Neural Network, Facial Expression Recognition, Detection of Emotional Information, Adversarial Risk Analysis, Broaden and Build Theory
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-108681DOI: 10.1007/978-3-319-12027-0_49ISBN: 978-3-319-12026-3 (print)ISBN: 978-3-319-12027-0 (print)OAI: oai:DiVA.org:su-108681DiVA: diva2:760059
Conference
14th Ibero-American Conference on AI, Santiago de Chile, Chile, November 24-27, 2014
Available from: 2014-11-03 Created: 2014-11-03 Last updated: 2015-12-14Bibliographically approved
In thesis
1. Decisional-Emotional Support System for a Synthetic Agent: Influence of Emotions in Decision-Making Toward the Participation of Automata in Society
Open this publication in new window or tab >>Decisional-Emotional Support System for a Synthetic Agent: Influence of Emotions in Decision-Making Toward the Participation of Automata in Society
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Emotion influences our actions, and this means that emotion has subjective decision value. Emotions, properly interpreted and understood, of those affected by decisions provide feedback to actions and, as such, serve as a basis for decisions. Accordingly, "affective computing" represents a wide range of technological opportunities toward the implementation of emotions to improve human-computer interaction, which also includes insights across a range of contexts of computational sciences into how we can design computer systems to communicate and recognize the emotional states provided by humans. Today, emotional systems such as software-only agents and embodied robots seem to improve every day at managing large volumes of information, and they remain emotionally incapable to read our feelings and react according to them. From a computational viewpoint, technology has made significant steps in determining how an emotional behavior model could be built; such a model is intended to be used for the purpose of intelligent assistance and support to humans. Human emotions are engines that allow people to generate useful responses to the current situation, taking into account the emotional states of others. Recovering the emotional cues emanating from the natural behavior of humans such as facial expressions and bodily kinetics could help to develop systems that allow recognition, interpretation, processing, simulation, and basing decisions on human emotions. Currently, there is a need to create emotional systems able to develop an emotional bond with users, reacting emotionally to encountered situations with the ability to help, assisting users to make their daily life easier. Handling emotions and their influence on decisions can improve the human-machine communication with a wider vision. The present thesis strives to provide an emotional architecture applicable for an agent, based on a group of decision-making models influenced by external emotional information provided by humans, acquired through a group of classification techniques from machine learning algorithms. The system can form positive bonds with the people it encounters when proceeding according to their emotional behavior. The agent embodied in the emotional architecture will interact with a user, facilitating their adoption in application areas such as caregiving to provide emotional support to the elderly. The agent's architecture uses an adversarial structure based on an Adversarial Risk Analysis framework with a decision analytic flavor that includes models forecasting a human's behavior and their impact on the surrounding environment. The agent perceives its environment and the actions performed by an individual, which constitute the resources needed to execute the agent's decision during the interaction. The agent's decision that is carried out from the adversarial structure is also affected by the information of emotional states provided by a classifiers-ensemble system, giving rise to a "decision with emotional connotation" included in the group of affective decisions. The performance of different well-known classifiers was compared in order to select the best result and build the ensemble system, based on feature selection methods that were introduced to predict the emotion. These methods are based on facial expression, bodily gestures, and speech, with satisfactory accuracy long before the final system.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2015. 146 p.
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 15-019
Keyword
Affective Computing; Machine Learning; Adversarial Risk Analysis; Broaden and Build Theory; Facial Expression Recognition; Speech Emotion Recognition; Detection of Emotional Information; Emotional self-regulation
National Category
Human Computer Interaction
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-122084 (URN)978-91-7649-291-8 (ISBN)
Public defence
2015-12-14, room L70, NOD Building, Borgarfjordsgatan 12, Kista, 13:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 8: Accepted.

Available from: 2015-11-20 Created: 2015-10-23 Last updated: 2015-11-30Bibliographically approved

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