Abstract
With the proliferation of information and communication technologies, especially with recent developments in Artificial Intelligence, social robots at home and the workplace are no longer being treated as lifeless and emotionless, leading to proposals which aim at incorporating affective elements within agents. Advances in areas such as affective decision-making and affective computing drive this interest. Our motivation in this paper is to use affection as a basic element within a decision-making process to facilitate robotic agents providing more seemingly human responses. We use earlier research in cognitive science and psychology to provide a model for an autonomous agent that makes decisions partly influenced by affective factors when interacting with humans and other agents. The factors included are emotions, mood, personality traits, and activation sets in relation with impulsive behavior. We describe several simulations with our model to study and compare its performance when facing various types of users. Through them, we essentially showcase that our model allows for a powerful agent design mechanism regulating its behavior and provides greater decision-making adaptivity when compared to emotionless agents and simpler emotional models. We conclude describing potential uses of our model in several application areas.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Note that the agent does not actually need to store all values, but just needs to update the average value in memory when the corresponding action is used.
References
Ashton MC, Lee K (2007) Empirical, theoretical, and practical advantages of the hexaco model of personality structure. Personal Soc Psychol Rev 11(2):150–166
Ashton MC, Lee K, De Vries RE (2014) The HEXACO honesty-humility, agreeableness, and emotionality factors: a review of research and theory. Personal Soc Psychol Rev 18(2):139–152
Bechara A, Damasio H, Tranel D, Damasio AR (1997) Deciding advantageously before knowing the advantageous strategy. Science 275(5304):1293–1295
Breazeal C (2003) Emotion and sociable humanoid robots. Int J Hum Comput Stud 59(1–2):119–155
Bruce A, Nourbakhsh I, Simmons R (2002) The role of expressiveness and attention in human-robot interaction. In: Proceedings 2002 IEEE international conference on robotics and automation (Cat. No. 02CH37292), vol 4. IEEE, pp 4138–4142
Clark HH, Brennan SE (1991) Grounding in communication. In: Resnick L, Levine J, Teasley S (eds) Perspectives on socially shared cognition. American Psychological Association, Washington, pp 127–149
Clore GL (1992) Cognitive phenomenology: Feelings and the construction of judgment. Constr Soc Judgm 10:133–163
Cytowic RE (2008) The man who tasted shapes. MIT Press, Cambridge
De Vries RE (2013) The 24-item brief HEXACO inventory (BHI). J Res Personal 47(6):871–880
Ekman PE, Davidson RJ (1994) The nature of emotion: fundamental questions. Oxford University Press, Oxford
El-Nasr MS, Yen J, Ioerger TR (2000) Flame: fuzzy logic adaptive model of emotions. Auton Agents Multiagent Syst 3(3):219–257
Ellingsen DM, Leknes S, Lseth G, Wessberg J, Olausson H (2016) The neurobiology shaping affective touch: expectation, motivation, and meaning in the multisensory context. Front Psychol 6:1986
Esteban PG, Insua DR (2014) Supporting an autonomous social agent within a competitive environment. Cybern Syst 45(3):241–253
Fehr-Duda H, Epper T, Bruhin A, Schubert R (2011) Risk and rationality: the effects of mood and decision rules on probability weighting. J Econ Behav Organ 78(1–2):14–24
Frijda NH (1988) The laws of emotion. Am Psychol 43(5):349–358
Goldberg LR (1990) An alternative description of personality: the big-five factor structure. J Personal Soc Psychol 59(6):1216–1229
Esteban PG, Insua DR (2017) A model for an affective non-expensive utility-based decision agent. IEEE Trans Affect Comput 10(4):498–509. https://doi.org/10.1109/TAFFC.2017.2737979
Gratch J, Marsella S (2004) A domain-independent framework for modeling emotion. Cognit Syst Res 5(4):269–306
Kahneman D (2011) Thinking, fast and slow, vol 1. Farrar, Straus and Giroux, New York
Kirby R, Forlizzi J, Simmons R (2010) Affective social robots. Robot Auton Syst 58(3):322–332
Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22(1):79–86
Leidelmeijer K (1991) Emotions: an experimental approach. Tilburg University Press, Tilburg
Lemaignan S, Warnier M, Sisbot EA, Clodic A, Alami R (2017) Artificial cognition for social human-robot interaction: an implementation. Artif Intell 247:45–69
Lintas A, Chi N, Lauzon NM, Bishop SF, Sun N, Tan H, Laviolette SR (2012) Inputs from the basolateral amygdala to the nucleus accumbens shell control opiate reward magnitude via differential dopamine D1 or D2 receptor transmission. Eur J Neurosci 35:279–290
Loewenstein G, Lerner JS (2003) The role of affect in decision making. Handb Affect Sci 3:619–642
Lopes LL (1987) Between hope and fear: the psychology of risk. In: Advances in experimental social psychology, vol 20. Elsevier, Amsterdam, pp 255–295
Mellers BA (2000) Choice and the relative pleasure of consequences. Psychol Bull 126(6):910–924
Mellers BA, Schwartz A, Ho K, Ritov I (1997) Decision affect theory: emotional reactions to the outcomes of risky options. Psychol Sci 8(6):423–429
Mesrobian SK, Bader M, Götte L, Villa AE, Lintas A (2015) Imperfect decision making and risk taking are affected by personality. In: Decision making: uncertainty, imperfection, deliberation and scalability. Springer, Berlin, pp 145–184
Moshkina LV (2011) An integrative framework of time-varying affective robotic behavior. PhD thesis
Ortony A, Clore GL, Collins A (1990) The cognitive structure of emotions. Cambridge University Press, Cambridge
Pereira G, Prada R, Santos PA (2016) Integrating social power into the decision-making of cognitive agents. Artif Intell 241:1–44
Picard RW (1997) Affective computing. MIT Press, Cambridge
Price DD, Barrell JE, Barrell JJ (1985) A quantitative-experiential analysis of human emotions. Motiv Emot 9(1):19–38
Rázuri JG, Esteban PG, Insua DR (2013) An adversarial risk analysis model for an autonomous imperfect decision agent. In: Decision making and imperfection. Springer, Berlin, pp 163–187
Reid AA, González-Vallejo C (2009) Emotion as a tradeable quantity. J Behav Decis Mak 22(1):62–90
Rios Insua D, Banks D, Rios J (2016) Modeling opponents in adversarial risk analysis. Risk Anal 36(4):742–755
Scherer KR (2000) Psychological models of emotion. Neuropsychol Emot 137(3):137–162
Scholtz J (2003) Theory and evaluation of human robot interactions. In: Proceedings of the 36th annual Hawaii international conference on system sciences, p 10
Tiinanen T, Törmänen J, Hämäläinen RP, Saarinen E (2016) PoSITeams—positive systems intelligent teams, an agent-based simulator for studying group behaviour. In: Proceedings of the 59th annual meeting of the ISSS-2015, Berlin, vol 1
Acknowledgements
The research has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 713673. Si Liu has received the financial support through the “la Caixa” INPhINIT Fellowship Grant for Doctoral studies at Spanish Research Centres of Excellence. The project that gave rise to these results received the support of a fellowship from la Caixa Foundation (ID 100010434). The fellowship code is LCF/BQ/IN17/11620052. The work of David Rios Insua is supported by the Spanish Ministry of Economy and Innovation program MTM2017-86875-C3-1-R and the AXA-ICMAT Chair on Adversarial Risk Analysis. We thank all the colleagues and researchers who gave us suggestions and help, including the referees.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Liu, S., Ríos Insua, D. An affective decision-making model with applications to social robotics. EURO J Decis Process 8, 13–39 (2020). https://doi.org/10.1007/s40070-019-00109-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40070-019-00109-1