CAAF: A Cognitive Affective Agent Programming Framework

Artificial Intelligence Review, 2012

A great number of computational models of emotions (CMEs) have been developed to be included in, or as part of, cognitive agent architectures. These computational models have been designed to provide autonomous agents with appropriate mechanisms for the processing of emotional stimuli, the elicitation of synthetic emotions, and the generation of emotional responses. The research on CMEs has allowed for improvements in several application domains and contributed to progress in areas such as human-computer interaction and artificial intelligence. Nevertheless, despite the wide variety of CMEs proposed in the literature and their success in multiple areas, the complexity and quality of current and future human-centered applications require the development of more flexible and robust CMEs. In this sense, CMEs have yet to face a series of challenges in order to meet such types of requirements. In this paper, we explore key aspects of the development and applications of CMEs for autonomous agents, discuss four major challenges facing their development process, and present a novel approach to deal with these challenges. Keywords Emotions • Computational modeling • Agent architectures • Affective agents 1 Introduction The multidisciplinary study of human emotions has led to the formulation of a great volume of theories and models that explain the various facets of this human function. These theories suggest that emotions play an important role in the development of rational behavior in humans and that experiencing and expressing them are essential for survival purposes. According to Damasio (1994) and Loewenstein and Lerner (2003), emotions influence human cognitive

PRICAI 2002: Trends in Artificial Intelligence, 2002

Information and Software Technology, 2007

This paper proposes modeling of artificial emotions through agents based on symbolic approach. The symbolic approach utilizes symbolic emotional rule-based systems (rule base that generated emotions) with continuous interactions with environment and an internal ''thinking'' machinery that comes as a result of series of inferences, evaluation, evolution processes, adaptation, learning, and emotions. We build two models for agent based systems; one is supported with artificial emotions and the other one without emotions. We use both in solving a bench mark problem; ''The Orphanage Care Problem''. The two systems are simulated and results are compared. Our study shows that systems with proper model of emotions can perform in many cases better than systems without emotions. We try to shed the light here on how artificial emotions can be modeled in a simple rule-based agent systems and if emotions as they exist in ''real intelligence'' can be helpful for ''artificial intelligence''. Agent architectures are presented as a generic blueprint on which the design of agents can be based. Our focus is on the functional design, including flow of information and control. With this information provided, the generic blueprints of architectures should not be difficult to implement agents, thus putting these theoretical models into practice. We build the agents using this architecture, and many experiments and analysis are shown.

Proceedings of the 11th International Conference on Agents and Artificial Intelligence, 2019

Interest in affective computing is increasing in recent years. Different emotional approaches have been developed to incorporate emotions in multi-agent systems. However, most of these models do not offer an adequate representation of emotions. An internal representation of emotions allows to define emotions according to different affective variables. In addition, many of these approaches do not take into account factors such as culture and language when defining emotions. In this work we show the results obtained in an experiment carried out to design an affective model for a multi-agent system taking into account factors such as language and culture.

Cognitive Computation, 2014

It has been recognized that human behavior is an observable consequence of the interactions between cognitive and affective functions. This perception has motivated the study of human emotions in disciplines such as psychology and neuroscience and led to the formulation of a number of theories and models that attempt to explain the mechanisms underlying this human process. In the field of artificial intelligence, these theoretical findings have posed a series of challenges in the development of autonomous agents (AAs) capable of exhibiting very believable and human-like behaviors. One of these challenges is the design and implementation of computational models of emotions (CMEs), which are software systems designed to provide AAs with proper mechanisms for the processing of emotional information, elicitation of synthetic emotions, and generation of emotional behaviors. In this paper, we review this type of computational model from the perspective of their development. Particularly, we investigate five design aspects that influence their development process: theoretical foundations, operating cycle, interaction between cognition and emotion, architectural design, and role in cognitive agent architectures. We provide discussions about key issues and challenges in the development of CMEs and suggest research that may lead to more robust and flexible designs for this type of computational model.

ACM Transactions on Internet Technology

Affective characteristics are crucial factors that influence human behavior, and often, the prevalence of either emotions or reason varies on each individual. We aim to facilitate the development of agents’ reasoning considering their affective characteristics. We first identify core processes in an affective BDI agent, and we integrate them into an affective agent architecture ( GenIA 3 ). These tasks include the extension of the BDI agent reasoning cycle to be compliant with the architecture, the extension of the agent language (Jason) to support affect-based reasoning, and the adjustment of the equilibrium between the agent’s affective and rational sides.

Eludamos: Journal for Computer Game Culture, 2008

This paper attempts to highlight some of the research that has been conducted worldwide in the area of computational models of emotions, with a particular emphasis on agent emotions suitable for simulations and games. The intended outcome is to both review some of the more prominent research in the field, and to also ascertain the level of formal psychology that may underpin such work with a view to proposing that there is scope for an architecture built from the ground up, that arises from non-conflicting theories of emotion.

Lecture Notes in Computer Science, 2011

Emotional agents are an active research domain, with direct application in several industrial fields such as video games, interactive environments or enhanced human computer interactions. Emotional behavior should consider both the representation of the emotions and the mood states. There are two mostly accepted, and used, cognitive psychological models for this: OCC model and PAD model.Based on these models, this paper includes two main contributions, on one hand, we discuss the use of common representation for both mood states and emotions and, on the other hand, this paper introduces the concept of the Mood Vector Space and analyzes the properties and foundations of such a space to support emotional agent representation and operation.