Physics and Cognitive-Emotional-Metacognitive Variables

In our study we collected data with respect to cognitive variables (learning outcome), metacognitive indicators (knowledge about cognition and regulation of cognition) psychological variables (self-esteem) and emotional variables (motives, anxiety). The teaching sequence was implemented using the CTAT authoring tool and the basic teaching unit was referred to fundamental concepts in Mechanics for 20 4th year undergraduate students enrolled in the course «Ápplied Didactics in Natural Sciences» of the University of the Aegean-Department of Education. Analysis of the results shows that anxiety (a negative emotion) can be reduced using CTAT , there is a transfer from extrinsic to intrinsic motivation while metacognitive indicators as well as learning performance can be improved using CTAT . The interactivity of the learning environment influences also self esteem and the results are presented.

A Theory of Emotions Based on Natural Language Semantics

A new theory of emotions is derived from the semantics of the language of emotions. The sound structures of 36 Old Arabic word roots that express specific emotions are converted into abstract models. By substitution from two tables, abstract models are converted into concrete theories about the nature of the specific emotions that are likely to be validated. Theories confirmed by the author’s own emotional experience (self reports), and by previously corroborated theories, are considered corroborated. These theories about specific emotions are woven together into an integrated theory of all emotions. The theory models emotions and emotional mechanisms, dimensions and polarities in ways amenable to affective computing. The findings are supported by clinical psychology. Old Arabic is chosen because its words, sounds and meanings are consistent and have not changed for at least 1,400 years. The theory can be expanded by incorporating additional emotional word roots from Arabic and other alphabetical languages.

Emotion Generation Based on a Mismatch Theory of Emotions for Situated Agents

Emotion plays several important roles in the cognition of human beings and other life forms, and is therefore a legitimate inspiration for providing situated agents with adaptability and autonomy. However, there is no unified theory of emotion and many discoveries are yet to be made in its applicability to situated agents. One function of emotion commonly identified by psychologists is to signal to other cognitive processes that the current situation requires an adaptation. The main purposes of this chapter are to highlight the usefulness of this signaling function of emotion for situated agents and to present an artificial model of anger and fear based on mismatch theories of emotion, which aims at replicating this function. Collective foraging simulations are used to demonstrate the feasibility of the model and to characterize its influence on a decision-making architecture.

Affective Goal and Task Selection for Social Robots

Effective decision-making under real-world conditions can be very difficult as purely rational methods of decision-making are often not feasible or applicable. Psychologists have long hypothesized that humans are able to cope with time and resource limitations by employing affective evaluations rather than rational ones. In this chapter, we present the distributed integrated affect cognition and reflection architecture DIARC for social robots intended for natural human-robot interaction and demonstrate the utility of its human-inspired affect mechanisms for the selection of tasks and goals. Specifically, we show that DIARC incorporates affect mechanisms throughout the architecture, which are based on “evaluation signals” generated in each architectural component to obtain quick and efficient estimates of the state of the component, and illustrate the operation and utility of these mechanisms with examples from human-robot interaction experiments.

Unfolding Commitments Management

The Commitment Theory of Emotions is issued from a careful scrutiny of emotional behavior in humans and animals, as reported in the literature on Evolutionary Biology, Neurosciences and the Psychology of emotions. Emotions are viewed as a special layer of processes, wired upon needs and instincts, and dealing mainly with nurturance, social bonding and cooperative behavior, especially between kin and relatives. As every other motivational system, they operate so as to manage resources critical for survival and reproduction. The peculiar resources emotions do manage are commitments, understood as the predispositions of a given individual to help others and collaborate with them in a reciprocal manner. This view clarifies considerably the interactions emotions entertain with a variety of modules involved in their operation, from the detection of antecedents in perceptual or memory systems, to the elicitation of the appropriate emotion, to the execution of the corresponding script, to the expression of emotions as the typical outcome of emotional episodes. The flow of processing is continuously modulated by affective states of the organisms and by other motivational systems. The chapter expounds the operation of each module as well as their interactions with each other. It concludes that successful implementation of emotions in artificial systems will have to rest upon the specifications of complex and realistic models of the kind presented.

Multirobot Team Work with Benevolent Characters

Multi-robot team work is necessary for complex tasks which cannot be performed by a single robot. To get the required performance and reliability, it is necessary to develop a proper cooperative task. The robots need to be intelligent enough to adjust with dynamic workload and environment. Benefits can be amplified from a team if benevolence combines with cooperation. The benevolence behaviors among the team members are extra benefits to the society. There is a flexible relation among intelligence, benevolence and emotions. We describe an emotion model to be used for each of the members of a multi-robot team. In respect of some drawbacks with the existing approaches, we present an emotion based multi-robot cooperation with some benevolent characters.

Facial Expression Analysis, Modeling and Synthesis

The human face plays a central role in most forms of natural human interaction so we may expect that computational methods for analysis of facial information, modeling of internal emotional states, and methods for graphical synthesis of faces and facial expressions will play a growing role in human-computer and human-robot interaction. However, certain areas of face-based HCI, such as facial expression recognition and robotic facial display have lagged others, such as eye-gaze tracking, facial recognition, and conversational characters. Our goal in this paper is to review the situation in HCI with regards to the human face, and to discuss strategies, which could bring more slowly developing areas up to speed. In particular, we are proposing the “The Art of the Soluble” as a strategy forward and provide examples that successfully applied this strategy.

Emotional Modeling in an Interactive Robotic Head

Social intelligence seems to obviously require emotions. People have emotions, recognize them in others and also express them. A wealth of information is conveyed through facial expressions, voice tone, etc. If robots can recognize and express emotions, the interaction with the user will be improved because the robot will be able to analyze his/her affective state and choose a different action course depending on it. Thus, it seems clear that any attempt to imitate human social abilities should consider modeling emotions or affective states. This chapter describes the emotional model and implementation of CASIMIRO, a prototype social robot built by the authors. CASIMIRO is a complex robot with multimodal capabilities defined by a number of software modules. Examples of user interactions will be also shown that suggest that the model is appropriate for regulating the behavior of the robot.

Modelling Hardwired Synthetic Emotions

During the previous stage of our research we developed a computer simulation (called ‘The Panic Room’ or, more simply, ‘TPR’) dealing with synthetic emotions. TPR was developed with Python code and led us to interesting results. With TPR, we were merely trying to design an artificial device able to learn from, and interact with, the world by using two basic information types: positive and negative. We were developing the first steps towards an evolutionary machine, defining the key elements involved in the development of complex actions (that is, creating a physical intuitive ontology, from a bottomup approach). After the successful initial results of TPR, we considered that it would be necessary to develop a new simulation (which we will call “TPR 2.0.”), more complex and with better visualisation characteristics. We have now developed a second version, TPR 2.0., using the programming language Processing, with new improvements such as: a better visual interface, a database which can record and also recall easily the information on all the paths inside the simulation (human and automatically generated ones) and, finally, a small memory capacity which is a next step in the evolution from simple hard-wired activities to self-learning by simple experience.

Computer-Based Learning Environments with Emotional Agents

The purpose of this contribution is to discuss conceptual issues and challenges related to the integration of emotional agents in the design of computer-based learning environments and to propose a framework for the discussion of future research. We review some emotion theories and computational models that have been developed in cognitive science and Artificial Intelligence (AI). We then will discuss some basic principles pertaining to motivation and emotion in instructional design. Grounded on these principles, we then shall present the state of the art of integrating emotions into the design of educational systems, and notably examine how to create intelligent emotional agents that enhance interaction with users. We will introduce the concept of “socio-emotional climate” as an evaluative indicator of the diversity of desirable interactions within a computer-based learning environment. We formulate the conjecture that a socio-emotional climate capable of enhancing learner motivation, self-assessment and self-motivation could be developed through the use of various socio-emotional agents.