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A distributed architecture for a system simulating the emotional state of an agent acting in a virtual environment is presented. The system is an implementation of an event appraisal model of emotional behaviour and uses neural networks to learn how the emotional state should be influenced by the occurrence of environmental and internal
stimuli. A...
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... the domain we were inspired by [Inoue, Kawabata & Kobayashi 1996]. In Figure 2 a picture of the domain can be seen. The domain is a gridworld containing grass, water pools that can be dry or contain water, apple trees that can have apples growing and rocks, with possibly herbs growing on it. ...
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Citations
... Picard [17] proposed the hidden Ma emotion model, which can capture emotion changes exhibited by external states su changes in facial expressions, heart rate changes, voice fluctuations, and so on. Keste al. [18] established a distributed emotion state model based on neural network algori for event processing, which could convert the external emotion events into correspon emotion states by using neural network algorithms. Samani et al. [19] proposed a co site artificial emotion system, which consisted of three modules: probability-based tion matching (PLA), emotion-based artificial endocrine system (AES), and emotion transition (AST). ...
... Picard [17] proposed the hidden Markov emotion model, which can capture emotion changes exhibited by external states such as changes in facial expressions, heart rate changes, voice fluctuations, and so on. Kestern et al. [18] established a distributed emotion state model based on neural network algorithms for event processing, which could convert the external emotion events into corresponding emotion states by using neural network algorithms. Samani et al. [19] proposed a composite artificial emotion system, which consisted of three modules: probability-based emotion matching (PLA), emotion-based artificial endocrine system (AES), and emotion state transition (AST). ...
... pm pm e e e E 0 n s l n r t B = P P P (18) where ˆi pm e , ˆj pm e , and ˆk pm e represent different emotions, respectively. ...
How to express emotions through motion behaviors of robots (mainly for robotic arms) to achieve human–robot emotion interactions is the focus of this paper. An artificial emotion expression method that accords with human emotion that can deal with external stimuli and has the capability of emotion decision-making was proposed based on the motion behaviors of robot. Firstly, a three-dimensional emotion space was established based on the motion indexes (deviation coefficient, acceleration, and interval time). Then, an artificial emotion model, which was divided into three parts (the detection and processing of external events, the generation and modification of emotion response vectors, and the discretization of emotions) was established in the three-dimensional emotion space. Then emotion patterns (love, excited, happy, anxiety, hate) and emotion intensity were calculated based on the artificial emotion model in human–robot interaction experiments. Finally, the influence of motion behaviors of humanoid robot NAO on the emotion expression of experimenters was studied through human–robot emotion interaction experiments based on the emotion patterns and emotion intensity. The positive emotion patterns (love, excited, happy) and negative emotion patterns (anxiety, hate) of the experimenters were evaluated. The experimental results showed that the personalized emotion responses could be generated autonomously for external stimuli, and the change process of human emotions could be simulated effectively according to the established artificial emotion model. Furthermore, the experimenters could recognize the emotion patterns expressed by the robot according to the motion behaviors of the robot, and whether experimenters were familiar with robots did not influence the recognition of different emotion patterns.
... The MIT lab is also producing an emotional multi-agent project.The model only describes basic emotions and innate reactions. A distributed architecture for a system simulating the emotional state of an agent acting in a virtual environment was presented by Aard-Jan van Kesteren, Rieks op den Akker, Mannes Poel, Anton Nijholt [3]. The system is an implementation of an event appraisal model of emotional behavior and uses neural networks to learn how the emotional state should be influenced by the occurrence of environmental and internal stimuli. ...
Text Analytics is an interdisciplinary field which draws on data mining, machine learning, information extraction, statistics and computational linguistics.This paper deals with the development of an IntelligentAgent that exhibits the concept of emotional intelligence and which can be used for Text Analytics. Text Analytics is the most recent name given to Natural Language Understanding, Data and Text Mining. The architecture used is a Neuro-Fuzzy system which is a fuzzy system that uses a learning algorithm derived from or inspired by neural network theory to determine its parameters (fuzzy sets and fuzzy rules) by processing data samples. The input to the system is a real life event which is tokenized and the tokens are compared to a pre-defined database of emotional keywords. Based on the matching, the tokens are processed by the neuro-fuzzy controller and the corresponding emotion underlying the event is generated.The system can be used to analyze the text content in emails, blogs, tweets, forums and other forms of textual communication.
... This model is based on the study of Ard-jan van Kesteren et al. [15], and sets up a distributed model aim at behavior decision-making (Fig. 3), The whole distributed system converts specific behavior decision-making into the corresponding active field, process is divided into two stages: The first phase evaluates the emotional significance of behavior by Behavior Appraiser, for each type of behavior defined a corresponding Behavior Appraiser. When agent encounters with external behavior stimulus, first determines the behavior type and information, and then selects the relevant Behavior Appraiser to evaluate emotion and classify behavior stimulus. ...
Emotional intelligence is an important branch of artificial intelligence, it is an important way to achieve the truly intelligent robot. This paper regards an Agent living in a virtual environment as the research object and proposes a fuzzy emotion model based on particle system, and combines the feedback signal of emotion with associative learning mechanism based on CMAC neural network. Finally, combined the environment, emotion model and CMAC network we propose a new Agent's behavior decision-making model based on artificial emotion, the results show that this model can improve the performance of the agent.
... A distributed architecture for a system simulating the emotional state of an agent acting in a virtual environment was presented by Aard-Jan Van Kesteren, Rieks Op Den Akker, Mannes Poel, Anton Nijholt [1]. Devillers et al. [5] found the most appropriate emotional state by calculating the conditional probability between the emotional keywords and emotional states. ...
Emotion recognition is an important aspect of Affective Computing. This paper deals with the development of an intelligent agent for automatic recognition of emotion from text based events. The approach chosen is Soft Computing and the architecture used is a Neuro-Fuzzy system. The input (event) string is divided into tokens which are then compared to a standard corpus (i.e.,Wordnet-Affect) of emotional keywords. The computed values of emotional weight and polarity are then processed by a Neuro-Fuzzy Controller, which generates the emotion underlying the event. The system considers Ekman’s six basic emotions {Happiness, Despair, Disgust, Fear, Anger, Surprise}. The controller is trained to generate correct output through the backpropagation algorithm. The system is implemented using Java, and, Matlab is used for mathematical analysis. The performance of the system is graded based on the measures of Precision and Accuracy.
... A distributed architecture for a system simulating the emotional state of an agent acting in a virtual environment was presented by Aard-Jan van Kesteren, Rieks op den Akker, Mannes Poel, Anton Nijholt [3]. The system is an implementation of an event appraisal model of emotional behavior and uses neural networks to learn how the emotional state should be influenced by the occurrence of environmental and internal stimuli. ...
... The links in this layer are used to perform antecedent matching fuzzy logic rules and also the fuzzy AND operation. The link weight, w 3 i , at the third layer is also equal to unity. Layer 4: the links to this layer define consequences of the rule nodes. ...
In a very real sense we have two minds, one that thinks (rational mind) and one that feels (emotional mind). These two fundamentally different ways of knowing interact to construct our mental life. This results in two different kinds of intelligence i.e, rational and emotional. Intellect cannot work at its best without emotional intelligence. If our aim in AI is to build systems that behave like human beings then it is necessary that we incorporate elements of both rational and emotional intelligence into the system. The main concept in this paper is to develop an Intelligent Agent that exhibits the notion of emotional intelligence. The architecture that will be considered for implementation is a Neuro-Fuzzy system with concepts of artificial intelligence incorporated into the functionality of the system. The approach chosen for the implementation is Soft Computing which is basically a synergistic integration of three computing paradigms: neural networks, fuzzy logic and probabilistic reasoning, to provide a flexible framework to construct computationally intelligent systems.
... Furthermore, the mind of believable agents should not be restricted to model reasoning, intelligence and knowledge but also emotions and personality. We have therefore started exploring computational models of emotional behaviour, [10]. The representations used in this work form the basis for the results reported here on the facial expressions of emotions. ...
We propose a fuzzy rule-based system to map representations of the emotional state of an animated agent onto muscle contraction values for the appro- priate facial expressions. Our implementation pays special attention to the way in which continuous changes in the intensity of emotions can be displayed smoothly on the graphical face. The rule system we have defined implements the patterns described by psychologists and researchers dealing with facial expressions of hu- mans, including rules for displaying blends of expressions.
... studying the expressions of emotions by our talking faces and otherwise embodied agents, we are also studying computational models of emotions. An architecture for a system simulating the emotional state of an agent that acts in a virtual environment was constructed ([6]). It is an implementation of an event-appraisal model of emotional behaviour by Ortony, Clore and Collins ([11]). ...
In this paper, we provide an overview of part of our experience in designing and implementing some of the embodied agents and talking faces that we have used for our research into human computer interaction. We focus on the techniques that were used and evaluate this with respect to the purpose that the agents and faces were to serve and the costs involved in producing and maintaining the software. We discuss the function of this research and development in relation to the educational programme of our graduate students.
Aiming at the problems of lack of background knowledge and the inconsistent response of robots in the current human-computer interaction system, we proposed a human-computer interaction model based on a knowledge graph ripple network. The model simulated the natural human communication process to realize a more natural and intelligent human-computer interaction system. This study had three contributions: first, the affective friendliness of human-computer interaction was obtained by calculating the affective evaluation value and the emotional measurement of human-computer interaction. Then, the external knowledge graph was introduced as the background knowledge of the robot, and the conversation entity was embedded into the ripple network of the knowledge graph to obtain the potential entity content of interest of the participant. Finally, the robot replies based on emotional friendliness and content friendliness. The experimental results showed that, compared with the comparison models, the emotional friendliness and coherence of robots with background knowledge and emotional measurement effectively improve the response accuracy by 5.5% at least during human-computer interaction.
Improving the interaction between artificial systems and their users is an important issue in artificial intelligence. In current trends in social robotics, this is accomplished by making such systems not just intelligent but also emotionally sensitive. Therefore, artificial emotional intelligence (AEI) is focused on simulating and extending natural emotion (especially human emotion) to provide robots with the capability to recognise and express emotions in human–robot interaction (HRI). In this treatise, we present an overview of the advances made in AEI by highlighting the progress recorded in the areas of emotion classification, emotional robots, and emotion space modelling in HRI. This review is aimed at providing readers with a succinct, yet adequate compendium of the progresses made in the emerging AEI sub-discipline. It is hoped this effort will stimulate further interest aimed at the pursuit of more advanced algorithms and models for available technologies and possible applications to other domains.
Traditional interactive evolutionary computing approaches are usually susceptible to limited searching ability and human’s strong subjectivity. In response, by extending a traditional Belief-Desire-Intention (BDI) structure, a kind of affective learning agent which can perform affective computing and learning activities in human-computer interaction environment is explicitly introduced. In solving human-computer interactive multi-objective decision-making problems whose objectives are usually far from well structured and quantified, this kind of agent may help reduce human’s subjective fatigue as well as make decisions more objective and scientific. Specifically, a conceptual model of the agent, affective learning-BDI (AL-BDI) agent, is proposed initially, along with corresponding functional modules to learn human’s affective preference. After that, a kind of high level Petri nets, colored Petri nets are employed to realize the components and scheduler of the AL-BDI agents. To exemplify applications of the approaches, test functions are suggested to case studies, giving rise to satisfied results and showing validity of the contribution.
