Empirically building and evaluating a probabilistic model of user affect

We present a probabilistic model of user affect designed to allow an intelligent agent to recognise multiple user emotions during the interaction with an educational computer game. Our model is based on a probabilistic framework that deals with the high level of uncertainty involved in recognizing a variety of user emotions by combining in a Dynamic Bayesian Network information on both the causes and effects of emotional reactions. The part of the framework that reasons from causes to emotions (diagnostic model) implements a theoretical model of affect, the OCC model, which accounts for how emotions are caused by one’s appraisal of the current context in terms of one’s goals and preferences. The advantage of using the OCC model is that it provides an affective agent with explicit information not only on which emotions a user feels but also why, thus increasing the agent’s capability to effectively respond to the users’ emotions. The challenge is that building the model requires having mechanisms to assess user goals and how the environment fits them, a form of plan recognition. In this paper, we illustrate how we built the predictive part of the affective model by combining general theories with empirical studies to adapt the theories to our target application domain. We then present results on the model’s accuracy, showing that the model achieves good accuracy on several of the target emotions. We also discuss the model’s limitations, to open the ground for the next stage of the work, i.e., complementing the model with diagnostic information.

Affect expression in ECAs: Application to politeness displays

In this paper we present our embodied conversational agent (ECA) capable of displaying a vast set of facial expressions to communicate its emotional states as well as its social relations. Our agent is able to superpose and mask its emotional states as well as fake or inhibit them. We defined complex facial expressions as expressions arising from these displays. In the following, we describe a model based on fuzzy methods that enables to generate complex facial expressions of emotions. It uses fuzzy similarity to compute the degree of resemblance between facial expressions of the ECA. We also present an algorithm that adapts the facial behaviour of the agent depending on its social relationship with the interactants. This last algorithm is based on the theory of politeness by Brown and Levinson (1987). It outputs complex facial expressions that are socially adequate.     

A Multimodal Measurement Method of Users’ Emotional Experiences Shopping Online

Emotions play an important role in the design of e‐commerce websites. A website should satisfy its users’ emotional needs. Emotion measurement is a prerequisite to understanding users’ emotional needs; because emotions contain complicated components, they are difficulty to measure. To interpret users’ emotional experiences while the users are interacting with e‐commerce websites, we propose a multimodal measurement method conjoint using questionnaires, eye tracking, and physiological measures. The effects of various websites on users’ emotions and the relationship between users’ subjective emotional ratings, eye movements, and physiological responses, along with the effects of their emotions on behavior intentions, were analyzed. The results indicate that differences in users’ emotional experiences while shopping on various e‐commerce websites are primarily embodied in subjective emotional ratings and eye movements. There is a correlation between users’ subjective emotional ratings, eye movements, and physiological responses, and to some extent, users’ emotional experiences can affect their behavior intentions.     

EMOTIONAL AND NON-EMOTIONAL PERSUASION

A relevant issue in the domain of natural argumentation and persuasion is the interaction (synergic or conflicting) between “rational” or “cognitive” modes of persuasion and “irrational” or “emotional” ones. This work provides a model of general persuasion and emotional persuasion. We examine two basic modes for appealing to emotions, arguing that emotional persuasion does not necessarily coincide with irrational persuasion, and showing how the appeal to emotions is grounded on the strict and manifold relationship between emotions and goals, which is, so to say, “exploited” by a persuader. We describe various persuasion strategies, propose a method to formalize and represent them as oriented graphs, and show how emotional and non-emotional strategies (and also emotional and non-emotional components in the same strategy) may interact with and strengthen each other. Finally, we address the role of uncertainty in persuasion strategies and show how it can be represented in persuasion graphs.     

Reasoning about emotional agents

In this article we discuss the role of emotions in artificial agent design, and the use of logic in reasoning about the emotional or affective states an agent can reside in. We do so by extending the KARO framework for reasoning about rational agents appropriately. In particular, we formalize in this framework how emotions are related to the action monitoring capabilities of an agent. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 601–619, 2006.     

An emotion understanding framework for intelligent agents based on episodic and semantic memories

Emotional intelligence is the ability to process information about one’s own emotions and the emotions of others. It involves perceiving emotions, understanding emotions, managing emotions and using emotions in thought processes and in other activities. Emotion understanding is the cognitive activity of using emotions to infer why an agent is in an emotional state and which actions are associated with the emotional state. For humans, knowledge about emotions includes, in part, emotional experiences (episodic memory) and abstract knowledge about emotions (semantic memory). In accordance with the need for more sophisticated agents, the current research aims to increase the emotional intelligence of software agents by introducing and evaluating an emotion understanding framework for intelligent agents. The framework organizes the knowledge about emotions using episodic memory and semantic memory. Its episodic memory learns by storing specific details of emotional events experienced firsthand or observed. Its semantic memory is a lookup table of emotion-related facts combined with semantic graphs that learn through abstraction of additional relationships among emotions and actions from episodic memory. The framework is simulated in a multi-agent system in which agents attempt to elicit target emotions in other agents. They learn what events elicit emotions in other agents through interaction and observation. To evaluate the importance of different memory components, we run simulations with components “lesioned”. We show that our framework outperformed Q-learning, a standard method for machine learning.     

Modeling Emotions and Reason in Agent-Based Systems

We analyze how to develop an agent-based system in which agents evolve co-evolutionary endogenous rules of behavior by using best response and emotions. We show that best response is not sufficient to define complete and consistent rules of behavior and we prove that the use of emotions, which complement reason, is necessary to learn rules of behavior. We model four different emotions (apathy, patience, anger and confidence) which enable the agent to deal with the rewards and with others. We propose an algorithm to model automata-based systems incorporating rationality and emotions.     

A computational model of emotion through the perspective of benevolent agents for a cooperative task

This paper deals with a computational model of emotions and its application for cooperative benevolent agents. A stochastic emotion model based on Markov theory is adapted to perform their well organized tasks to achieve goal. The emotional model consists of four basic emotions: joy, anger, fear and sad. Different emotional behavior is obtained by updating the state transition matrix of stochastic model. Perception of stimuli has an impact on emotion inducing factors and thus, affects on emotion dynamics. With the developed model, a Matlab based simulation is performed to analyze the behavior of the agents with the emotional capability. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

人机交互中的智能体情感计算研究

机器的情感是通过融入具有情感能力的智能体实现的,虽然目前在人机交互领域已经有大量研究成果,但有关智能体情感计算方面的研究尚处起步阶段,深入开展这项研究对推动人机交互领域的发展具有重要的科学和应用价值。本文通过检索Scopus数据库选择有代表性的文献,重点关注情感在智能体和用户之间的双向流动,分别从智能体对用户的情绪感知和对用户情绪调节的角度开展分析总结。首先梳理了用户情绪的识别方法,即通过用户的表情、语音、姿态、生理信号和文本信息等多通道信息分析用户的情绪状态,归纳了情绪识别中的一些机器学习方法。其次从用户体验角度分析具有情绪表现力的智能体对用户的影响,总结了智能体的情绪生成和表现技术,指出智能体除了通过表情之外,还可以通过注视、姿态、头部运动和手势等非言语动作来表现情绪。并且梳理了典型的智能体情绪架构,举例说明了强化学习在智能体情绪设计中的作用。同时为了验证模型的准确性,比较了已有的情感评估手段和评价指标。最后指出智能体情感计算急需解决的问题。通过对现有研究的总结,智能体情感计算研究是一个很有前景的研究方向,希望本文能够为深入开展相关研究提供借鉴。     

Toward incorporating emotions with rationality into a communicative virtual agent

This paper addresses the problem of human–computer interactions when the computer can interpret and express a kind of human-like behavior, offering natural communication. A conceptual framework for incorporating emotions with rationality is proposed. A model of affective social interactions is described. The model utilizes the SAIBA framework, which distinguishes among several stages of processing of information. The SAIBA framework is extended, and a model is realized in human behavior detection, human behavior interpretation, intention planning, attention tracking behavior planning, and behavior realization components. Two models of incorporating emotions with rationality into a virtual artifact are presented. The first one uses an implicit implementation of emotions. The second one has an explicit realization of a three-layered model of emotions, which is highly interconnected with other components of the system. Details of the model with implicit implementation of emotional behavior are shown as well as evaluation methodology and results. Discussions about the extended model of an agent are given in the final part of the paper.     

Modeling human behavior during emergency evacuation using intelligent agents: A multi-agent simulation approach

It is costly and takes a lot of time for disaster employees to execute several evacuation drills for a building. One cannot glean information to advance the plan and blueprint of forthcoming buildings without executing many drills. We have developed a multi-agent system simulation application to aid in running several evacuation drills and theoretical situations. This paper combines the genetic algorithm (GA) with neural networks (NNs) and fuzzy logic (FL) to explore how intelligent agents can learn and adapt their behavior during an evacuation. The adaptive behavior focuses on the specific agents changing their behavior in the environment. The shared behavior of the agent places an emphasis on the crowd-modeling and emergency behavior in the multi-agent system. This paper provides a fuzzy individual model being developed for realistic modeling of human emotional behavior under normal and emergency conditions. It explores the impact of perception and emotions on the human behavior. We have established a novel intelligent agent with characteristics such as independence, collective ability, cooperativeness, and learning, which describes its final behavior. The contributions of this paper lie in our approach of utilizing a GA, NNs, and FL to model learning and adaptive behavior of agents in a multi-agent system. The planned application will help in executing numerous evacuation drills for what-if scenarios for social and cultural issues such as evacuation by integrating agent characteristics. This paper also compares our proposed multi-agent system with existing commercial evacuation tools as well as real-time evacuation drills for accuracy, building traffic characteristics, and the cumulative number of people exiting during evacuation. Our results show that the inclusion of GA, NNs, and fuzzy attributes made the evacuation time of the agents closer to the real-time evacuation drills.     

情绪主导型群体事件的情绪演化模型与仿真

大规模群体负面情绪的形成与蔓延是引发情绪主导型群体事件的根本原因。考虑在社会关系网络中的个体是有限理性的情形下,对群体情绪形成原因进行分析,归纳了群体情绪感染规则与蔓延机制,并依此构建了群体情绪感染模型,利用多主体仿真平台Netlogo对群体负面情绪的演化过程进行仿真实验,考察不同情景下群体负面情绪的演变情况,结果表明普通民众的理性程度,个体间的情感关系,意见领袖的干预、占比、干预时间、情绪感染阈值等都对群体负面情绪有影响。最后,对情绪主导型群体突发事件的预防与对策给出了合理建议。     

Emotional agents: A modeling and an application

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.     

Improvement of group performance of job distributed mobile robots by an emotionally biased control system

This paper deals with the implementation of emotions in mobile robots performing a specified task in a group in order to develop intelligent behavior and easier forms of communication. The overall group performance depends on the individual performance, group communication, and the synchronization of cooperation. With their emotional capability, each robot can distinguish the changed environment, can understand a colleague robot’s state, and can adapt and react with a changed world. The adaptive behavior of a robot is derived from the dominating emotion in an intelligent manner. In our control architecture, emotion plays a role to select the control precedence among alternatives such as behavior modes, cooperation plans, and goals. Emotional interaction happens among the robots, and a robot is biased by the emotional state of a colleague robot in performing a task. Here, emotional control is used for a better understanding of the colleague’s internal state, for faster communication, and for better performance eliminating dead time. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

FLAME—Fuzzy Logic Adaptive Model of Emotions

Emotions are an important aspect of human intelligence and have been shown to play a significant role in the human decision-making process. Researchers in areas such as cognitive science, philosophy, and artificial intelligence have proposed a variety of models of emotions. Most of the previous models focus on an agent's reactive behavior, for which they often generate emotions according to static rules or pre-determined domain knowledge. However, throughout the history of research on emotions, memory and experience have been emphasized to have a major influence on the emotional process. In this paper, we propose a new computational model of emotions that can be incorporated into intelligent agents and other complex, interactive programs. The model uses a fuzzy-logic representation to map events and observations to emotional states. The model also includes several inductive learning algorithms for learning patterns of events, associations among objects, and expectations. We demonstrate empirically through a computer simulation of a pet that the adaptive components of the model are crucial to users' assessments of the believability of the agent's interactions.     

Emotional disclosure on social networking sites: The role of network structure and psychological needs

We conducted three studies to understand how online emotional disclosure is influenced by social network structure on Facebook. Results showed that emotional disclosure was associated with both the density and size of users’ personal networks. Facebook users with denser networks disclosed more positive and negative emotions, and the relation between network density and emotional disclosure was mediated by stronger need for emotional expression. Facebook users with larger networks on Facebook disclosed more positive emotions, and the relation between network size and emotional disclosure was mediated by a stronger need for impression management. Our study extends past research by revealing the psychological mechanisms through which personal social network structure influences emotional disclosure. It suggests that social network size and density are associated with different psychological needs, which in turn lead to different patterns of emotional disclosure.     

Spontaneous behavior for cooperation through interaction: An emotionally intelligent robot system

This paper deals withspontaneous behavior for cooperation through interaction in a distributed autonomous robot system. Though a human gives the robots evaluation functions for the relation of cooperation among robots, each robot decides its behavior depending on its environment, its experience, and the behavior of other robots. The robot acquires a model of the behavior of the other robots through learning. Inspired by biological systems, the robot's behaviors are interpreted as emotional by an observer of the system. In psychology, the emotions have been considered to play important roles for generation of motivation and behavior selection. In this paper, the robot's behaviors are interpreted as follows: each robot feels frustration when its behavior decision does not fit its environment. Then, it changes its behavior to change its situation actively and spontaneously. The results show potential of intelligent behavior by emotions. This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996     

How emotion is made and measured

How we design and evaluate for emotions depends crucially on what we take emotions to be. In affective computing, affect is often taken to be another kind of information—discrete units or states internal to an individual that can be transmitted in a loss-free manner from people to computational systems and back. While affective computing explicitly challenges the primacy of rationality in cognitivist accounts of human activity, at a deeper level it often relies on and reproduces the same information-processing model of cognition. Drawing on cultural, social, and interactional critiques of cognition which have arisen in human–computer interaction (HCI), as well as anthropological and historical accounts of emotion, we explore an alternative perspective on emotion as interaction: dynamic, culturally mediated, and socially constructed and experienced. We demonstrate how this model leads to new goals for affective systems—instead of sensing and transmitting emotion, systems should support human users in understanding, interpreting, and experiencing emotion in its full complexity and ambiguity. In developing from emotion as objective, externally measurable unit to emotion as experience, evaluation, too, alters focus from externally tracking the circulation of emotional information to co-interpreting emotions as they are made in interaction.     

Employing Emotion Regulation Strategies in Tracking Personal Fitness Progress

This study examined the effect of adding an emotion regulation feature into fitness trackers. Applying the theoretical framework of emotion regulation, we argue that such feature can mitigate tracker users’ downward emotions due to failure to meet their fitness goals, and as such, the users would be continuously motivated to meet their fitness goals. To answer our hypotheses and research questions, we conducted a 2 (emotional intensity: low vs. high) × 3 (emotion regulation strategy: no regulation vs. cognitive change vs. attention deployment) online between-subjects experiment (N = 228). Our results indicate that emotion regulation function successfully regulated users’ downward emotions, which enhanced their state psychological well-being, perceived self-efficacy for exercise, and then facilitated more favorable fitness outcomes. We discuss design implications based on our results.