A Flexible Scheme to Model the Cognitive Influence on Emotions in Autonomous Agents

A Flexible Scheme to Model the Cognitive Influence on Emotions in Autonomous Agents

Sergio Castellanos (Sonora Institute of Technology, Ciudad Obregón, Mexico) and Luis-Felipe Rodríguez (Sonora Institute of Technology, Ciudad Obregón, Mexico)
DOI: 10.4018/IJCINI.2018100105

Abstract

Autonomous agents (AAs) are designed to embody the natural intelligence by incorporating cognitive mechanisms that are applied to evaluate stimuli from an emotional perspective. Computational models of emotions (CMEs) implement mechanisms of human information processing in order to provide AAs for a capability to assign emotional values to perceived stimuli and implement emotion-driven behaviors. However, a major challenge in the design of CMEs is how cognitive information is projected from the architecture of AAs. This article presents a cognitive model for CMEs based on appraisal theory aimed at modeling AAs' interactions between cognitive and affective processes. The proposed scheme explains the influence of AAs' cognition on emotions by fuzzy membership functions associated to appraisal dimensions. The computational simulation is designed in the context of an integrative framework to facilitate the development of CMEs, which are capable of interacting with cognitive components of AAs. This article presents a case study and experiment that demonstrate the functionality of the proposed models.
Article Preview

Introduction

Autonomous Agents (AAs) are software and robot entities that act on behalf of users or other programs with certain degree of independence and autonomy. In doing so, AAs make use of knowledge about the environment and representations of desires and goals (Franklin & Graesser, 1997; Wang, 2010; Wang, Zatarain, & Valipour, 2017). This type of intelligent system has been crucial for the advance of fields such as software engineering (SE), human-computer interaction (HCI), and artificial intelligence (AI). In these fields, AAs have been designed to carry out tasks that require the imitation of human cognitive functions, including decision making, planning, and reasoning (Ligeza, 1995; Maes, 1995; Sun, 2009). Giving AAs such cognitive functions allow them to carry out more complex tasks by minimizing human intervention. That is why research in these fields (e.g., AI, HCI, and SE) focuses on improving problem solving, reasoning, and communication skills of AAs. Particularly, the research community in the AI field has devoted efforts to create human-like systems for communication and reasoning as well as to reproduce in computer environments the associated brain processes (Ligeza, 1995). In the HCI field some interfaces and mechanisms that improve the interaction of these systems with other agents (computational or human agents) have also been developed (Martínez-Miranda & Aldea, 2005; Perlovsky & Kuvich, 2013).

Evidence shows that emotions influence cognitive functions (Ayesh, Arevalillo-Herráez, & Ferri, 2016; Hurtubise, 1995; Phelps, 2006). The emotional significance of perceived stimuli influences the normal operation of brain processes such as attention, perception, and decision making. According to fields such as psychology and neuroscience, emotions result from the interaction of several cognitive and affective processes, including memory, perception, motivations, and attention (Frijda, 2005; Goldie, 2002; LeDoux, 2000; Smith & Lane, 2016). Emotions are psychophysiological reactions that represent ways of adapting to perceived stimuli from an important object, person, place, event, or memory. Psychologically, emotions alter attention, trigger certain behaviors, and activate relevant associative networks in memory (Wang, 2012). According to Breazeal (1998) and Wang (2010), emotions are necessary to establish long-term memories. In addition, emotions play a key role in learning, from simple reinforcement learning to complex and conscious planning.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 13: 4 Issues (2019): Forthcoming, Available for Pre-Order
Volume 12: 4 Issues (2018)
Volume 11: 4 Issues (2017)
Volume 10: 4 Issues (2016)
Volume 9: 4 Issues (2015)
Volume 8: 4 Issues (2014)
Volume 7: 4 Issues (2013)
Volume 6: 4 Issues (2012)
Volume 5: 4 Issues (2011)
Volume 4: 4 Issues (2010)
Volume 3: 4 Issues (2009)
Volume 2: 4 Issues (2008)
Volume 1: 4 Issues (2007)
View Complete Journal Contents Listing