Emotions in the models of Artificial Intelligence (original) (raw)

Emotions in human and artificial intelligence

Computers in Human Behavior, 2005

Intelligence and emotions differentiate humans from animals. Emotion is part of a persons behaviour and certain feelings can affect his/her performance, emotions can even prevent a person from producing an intelligent outcome. Therefore, when a computer aims to emulate human behaviour, not only should this computer think and reason, but it should also be able to show emotions. This paper presents a review of recent research that shows the importance of the emotions in human intelligence. This paper also presents the research that has been carried out into the incorporation of emotions to intelligent systems, how a computer can show affections and how to create intelligent agents that show emotions to other agents that communicate with them in the same environment.

Artificial Intelligence and Emotions

Philosophical Problems of IT & Cyberspace (PhilIT&C), 2023

The development of the mind follows the path of biological evolution towards the accumulation and transmission of information with increasing efficiency. In addition to the “cognitive constants” of speech (Solntsev, 1974), which greatly improved the communication of information, people have created computing devices, from the abacus to the quantum computer. The capabilities of computers categorized as artificial intelligence are developing at a rapid pace. However, at the present stage, artificial intelligence (AI) lacks a module of emotions, and this makes AI fundamentally different from human intelligence, since the life of the mind in humans cannot be separated from their feelings (Damasio, 2010; Panksepp, 1997). Consciousness itself is formed through the sensory and motor systems, that is, consciousness is embodied (Foglia & Wilson, 2013), which means that our mental life is inseparable from our sensory-motor experience (Wellsby & Pexman, 2014). Moreover, studies show interaction between neural mechanisms in the motor and limbic systems (Pierce & Péron, 2020; Lima Portugal, et al., 2020). Evolutionarily, our minds rely on ancient survival mechanisms that influence our decisions and choices. Hence is the question whether the choice of Artificial Intelligence will always be favorable for humanity.

The Relationship Between Emotion Models and Artificial Intelligence

Emotions play a central role in most forms of natural human interaction so we may expect that computational methods for the processing and expression of emotions will play a growing role in human-computer interaction. The OCC model has established itself as the standard model for emotion synthesis. A large number of studies employed the OCC model to generate emotions for their embodied characters. Many developers of such characters believe that the OCC model will be all they ever need to equip their character with emotions. This study reflects on the limitations of the OCC model specifically, and on the emotion models in general due to their dependency on artificial intelligence.

Artificial intelligence and human emotions

not published

Emotion In Artificial Intelligence and Artificial Life Research: Facing Problems

Lecture Notes in Computer Science, 2005

In last decades, neuroscience and psychology research findings about emotion have been increasingly attracting the attention of many researchers in Computer Science and Artificial Intelligence (AI) areas. AI, interested in cognitive processes modeling and simulation, clearly see that emotion is a crucial element to model perception, learning, decision processes, memory, behavior and others functions. Currently, two Computer Science areas use emotion concepts on their research: Human-Computer Interaction and systems whose internal architecture is emotion-based.Even considering current state-of-art projects, theoretical aspects of emotion to be employed in computational systems projects are scarcely discussed. Our research intends to discuss these problems and propose tentative directions to solve them.First, (ii) the lack of a well defined scientific framework to approach 'Artificial Emotion', with few advanced attempts been published suggesting one. Besides that, a close look at some projects provides a non-exhausted list of (ii) important questions they might face to achieve trustworthy results. They can be grouped in two types, theoretical-conceptual or computational questions. Examples are: How to integrate emotion with other mechanisms, such as: sensory, learn, selection and communication? Can artificial emotion be an emergent property? What kind of data structure and computational mechanisms should be used to both capture and represent the complexity of emotion processes? What kind of experimental test allows to better explore emotion-based models? Moreover, an essential question to be answered is related to which extent supposed structural complexity involved in emotion phenomenon can be abstracted and modeled, not missing important brain structure interactions and not being too complex to impair computational representation.Last, these facts mainly contribute to a third noticeable problem: (iii) lack of comparative analysis between projects and also within same project, with beneficial comparisons of emotion and non-emotion-based experiments. Positively, overcome these challenges can be an important step to field progress goes beyond engineering applications and towards a more scientific discipline.

THE ROLE OF EMOTIONS IN HUMAN AND ARTIFICIAL INTELLIGENCE

2023

Emotional artificial intelligence (AI) represents a burgeoning frontier in technological advancement, with transformative implications for various aspects of human life and scientific progress. This paper delves into the rapidly evolving field of emotional AI, shedding light on its current state of the art and exploring the myriad ways it stands to revolutionize humantechnology interactions. The integration of emotions into AI systems holds the promise of reshaping conventional notions of intellect and emotional awareness. By bridging the gap between machine and human emotions, emotional AI has the potential to usher in a new era of innovation and understanding. This paper critically examines the implications of emotional AI in diverse disciplines, challenging preconceived ideas about the limits of machine learning and artificial intelligence. A focal point of discussion is the application of emotional AI in patient emotional monitoring and mental health support. The paper investigates the feasibility and potential impact of leveraging emotional AI technologies to enhance the quality of healthcare services. Through an exploration of existing research and emerging technologies, the paper outlines how emotional AI could play a pivotal role in revolutionizing patient care by providing real-time emotional insights and tailored mental health support. In conclusion, this paper contributes to the ongoing discourse surrounding emotional AI by providing a comprehensive overview of its current state and potential applications. By emphasizing its role in patient emotional monitoring and mental health support, the paper advocates for the responsible and ethical development of emotional AI, recognizing its capacity to positively transform the human experience and advance scientific frontiers.

Emotion, Cognition, and Artificial Intelligence

Minds and Machines, 2014

Some have claimed that since machines lack emotional “qualia”, or conscious experiences of emotion, machine intelligence will fall short of human intelligence. I examine this objection, ultimately finding it unpersuasive. I first discuss recent work on emotion that suggests that emotion plays various roles in cognition. I then raise the following question: are phenomenal experiences of emotion an essential or necessary component of the performance of these cognitive abilities? I then sharpen the question by distinguishing between four possible positions one might take. I reject one of these four positions largely on empirical grounds. But the remaining three positions all suggest that even if emotional qualia play an important role in human cognition, emotional qualia are not essential to the performance of these cognitive abilities in principle, so, e.g., a machine that lacks emotional qualia might still be able to perform them.

Toward a comprehensive theory of emotions for biological and artificial agents

Biologically Inspired Cognitive Architectures (2013) 4, 3– 26, 2013

A new model of emotions that is applicable to both biological and artificial agents is proposed. The description includes theoretical foundations, internal representation, and the role of emotions in cognition. This model is based on definitions of emotions in valence and arousal space coupled with an adaptation of Maslow’s hierarchy and other ideas. The resulting architecture provides for a significantly more expressive range and organization of represented emotional experience compared to other models. Requirements for a satisfactory general computational theory of emotions are applied to the new theory and analyzed in terms of (i) neurological and psychological plausibility, (ii) range and complexity of human emotional experience, (iii) applicability to learning, memory, behavior, and decision-making, and (iv) consistency with wellaccepted models and general facts about emotions. The model is implemented and studied through simulations of virtual agent-based systems. Presented results support the model’s applicability to perception, action selection, learning, and memory in virtual agents capable of human-like behavior. Paradigms and predictions all

A new approach to modeling emotions and their use on a decision-making system for artificial agents

2012

In this paper, a new approach to the generation and the role of artificial emotions in the decision-making process of autonomous agents (physical and virtual) is presented. The proposed decision-making system is biologically inspired and it is based on drives, motivations, and emotions. The agent has certain needs or drives that must be within a certain range, and motivations are understood as what moves the agent to satisfy a drive. Considering that the well-being of the agent is a function of its drives, the goal of the agent is to optimize it. Currently, the implemented artificial emotions are happiness, sadness, and fear. The novelties of our approach are, on one hand, that the generation method and the role of each of the artificial emotions are not defined as a whole, as most authors do. Each artificial emotion is treated separately. On the other hand, in the proposed system it is not mandatory to predefine either the situations that must release any artificial emotion or the actions that must be executed in each case. Both the emotional releaser and the actions can be learned by the agent, as happens on some occasions in nature, based on its own experience. In order to test the decision-making process, it has been implemented on virtual agents (software entities) living in a simple virtual environment. The results presented in this paper correspond to the implementation of the decision-making system on an agent whose main goal is to learn from scratch how to behave in order to maximize its well-being by satisfying its drives or needs. The learning process, as shown by the experiments, produces very natural results. The usefulness of the artificial emotions in the decisionmaking system is proven by making the same experiments with and without artificial emotions, and then comparing the performance of the agent.

Computational models of emotions for autonomous agents: major challenges

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

Emotions in the models of Artificial Intelligence (original) (raw)

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