The emergence of what is now called `emotional intelligence' has revealed yet another aspect of human intelligence. Emotions have been shown to have a major impact on many of our everyday functions, including decision-making, planning, communication, and behavior. AI researchers have recently acknowledged this major role that emotions play, and thus have began to incorporate models for simulating emotions into agents. However, the emotional process is not a simple process; it is often linked with many other processes, one of which is learning. It has long been emphasized in psychology that memory and experience help shape the dynamic nature of the emotional process. In this paper, we introduce PETEEI (a PET with Evolving Emotional Intelligence). PETEEI is based on a fuzzy logic model for simulating emotions in agents, with a particular emphasis on incorporating various learning mechanisms so that an agent can adapt its emotions according to its own experience. Additionally, PETEEI is d...

Music today is more ubiquitous, accessible, and democratized than ever. Thanks to technologies such as high-end home studios, audio compression, and digital distribution, music now surrounds us in everyday life, almost every piece of music is a few minutes of download away, and almost any western musician, novice or expert, can compose, perform and distribute their music directly to their listeners from their home studios. But at the same time these technologies lead to some concerning social effects on the culture of consuming and creating music. Although music is available for more people, in more locations, and for longer periods of time, most listeners experience it in an incidental, unengaged, or utilitarian manner. On the creation side, home studios promote private and isolated practice of music making where hardly any musical instruments or even musicians are needed, and where the value of live

It is now easy to find examples of interactive software agents and animated creatures that have the ability to express emotion; this paper describes research for giving them the ability to recognize emotion. The ability to recognize a person 's emotions is a key aspect of human "emotional intelligence," which has been described by a number of scientists as being more important to success in life than are the traditional forms of mathematical and verbal intelligence. This paper describes research underway in emotion recognition at the MIT Media Lab, especially research involving new wearable interfaces. 1 Introduction People often laugh or express delight at something presented by a computer---a funny animation, a virtual pet, a piece of humor mail---even though computers, to date, have been unaware of these human reactions. It is perhaps even more frequent to see a person expressing frustration or irritation at a computer, especially when they feel that the system is hind...

The nature of the general factor of intelligence, or g, is examined. This article begins by observing that the finding of a general factor of intelligence appears to be inconsistent with current findings in neuroscience and cognitive science, where specific connections are argued to be critical for different intellectual abilities and the brain is argued to develop these connections in response to environmental stimuli. However, it is then observed that if people differed in neural plasticity, or the ability to adapt their connections to the environment, then those highly developed in one intellectual ability would be highly developed in other intellectual abilities as well. Simulations are then used to confirm that such a pattern would be obtained. Such a model is also shown to account for many other findings in the field of intelligence that are currently unexplained. A critical period for intellectual development is then emphasized. Perhaps the most well-known concept that scientific psychology has provided to the wider community is that of IQ. Although few people in the general public are familiar with concepts such as latent inhibition and event schemas, IQ is recognized as an attempt to identify and measure differences in that mysterious characteristic

The interactivist approach to development generates a framework of types of constraints on what can be constructed. The four constraint types are based on: (1) what the constructed systems are about; (2) the representational relationship itself; (3) the nature of the systems being constructed; and (4) the process of construction itself. We give illustrations of each constraint type. Any developmental theory needs to acknowledge all four types of constraint; however, some current theories conflate different types of constraint, or rely on a single constraint type to explicate development. Such theories will be inherently unable to explain important aspects of development.

Virtual Environment interfaces are designed by implementing an interaction metaphor and comparing it to existing implementations. This technique has proven effective in desktop interfaces but the difficulty of working inside a VE remains because VE interfaces do not understand what the user is trying to do, only what the metaphor wants to do. To handle this problem, we investigated a lower-level approach in interface design of letting the user work as they wish and the interface adapting to the user’s method of interaction. Two exploratory experiments were performed on the task of selection to learn how users want to work, with the results being that users do not know until guided by affordances and feedback. Discussed is the intelligent capturing and dealing with VE interface data in terms of Nuances that can represent the details of the interface.

The fast growing technology of agents and the rise of emotional intelligence opened the doors to many new and interesting ideas. Among these ideas, one that has captured the interest of many researchers within the AI community is the development of emotional agents. While research on emotional agents is in a very early stage, many models have been developed to simulate emotions in agents. Some of the most recent computational models of emotions attempt to account for the inevitable links between emotions and many other processes residing in the mind, such as planning, cognition, personality, and motivation. Most of these models, however, simulate an incomplete picture of the links involved. Furthermore, most of these models tend to simulate emotional triggered expressions or behaviors according to one emotion, which is an invalid assumption, since emotions, realistically, occur in a mixture. We have taken a relatively new approach in emotional agents' development. Rather than defining ...

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Abstract Educational systems tend to emphasise the verbal, symbolic and numerical modes of learning though recently, there has been a wave of change in education, with an increasing emphasis on visual literacy. In everyday life and in learning, visual information is used to interpret experience and build understanding. This can be illustrated in three ways. First, visual thinking is part of the way we reason, such as when we extract of information from a map, chart or table and represent and express it in language. Second, visual thinking can be integral to problem solving, as when we need to use a diagram to explain, document, calculate or show the steps involved in reaching a solution. Third, visual representation can play a role in communication, for instance using diagrammatic and visual forms to communicate information, represent data and show relationships. This paper aims to give an overview of current theories and research on visual thinking and how it relates to learning. Examples of how technologies can enhance the visual dimension of communication and learning are discussed.

In this paper we present both theoretical structure and empirical validation of an on-line questionnaire to measure learners self-rated motivation, learning strategies, learning styles and social abilities. Profiling information of the measurement instrument has been utilised in various learning management systems in Finnish distance learning courses. The Abilities for Computer Assisted Learning Questionnaire I (ACALQ) contains 48 items measuring following dimensions: (1) motivation, 12 items; (2) learning strategies, 10 items; (3) learning styles, 6 items; (4) social abilities, 12 items; (5) serialistic-holistic approach (8 items); and (6) signaling (8 items). The theoretical structure of the first four parts of the instrument was analysed with the following three empirical samples (n=328): Finnish elementary school 5th and 6th grade children (age median 11 years, n=166), Finnish university students (age median 21 years, n=112) and Finnish post graduate adult learners preparing their dissertations (age median 27 years, n=50). The results of Bayesian network modeling show that the structure of the ACALQ is valid for all the three groups except for the third dimension (“Learning styles”) that was not found in the adolescent and adult samples. Results of CFA show that all the optimised solutions (except the 26-item solution for the sample 1) surpass the baseline model by both comparative fit index and Tucker-Lewis coefficient.