Methods to Improve the Field of Intelligent Tutoring Systems using Emotion-based Agents

The aim of this paper is to review select current methods used in the field of Intelligent Tutoring Systems (ITS) with respect to the use of emotion-based agents and how those systems interact with the learner to capture criti-cal data, store the data, and effectively process the data to produce valuable feedback. From this data collected, proposed methods are presented on how to improve existing ITS systems and how to make new ITS’s more effective

Context-aware emotion-based model for group decision making

Involving groups in important management processes such as decision making has several advantages. By discussing and combining ideas, counter ideas, critical opinions, identified constraints, and alternatives, a group of individuals can test potentially better solutions, sometimes in the form of new products, services, and plans. In the past few decades, operations research, AI, and computer science have had tremendous success creating software systems that can achieve optimal solutions, even for complex problems. The only drawback is that people don’t always agree with these solutions. Sometimes this dissatisfaction is due to an incorrect parameterization of the problem. Nevertheless, the reasons people don’t like a solution might not be quantifiable, because those reasons are often based on aspects such as emotion, mood, and personality. At the same time, monolithic individual decisionsupport systems centered on optimizing solutions are being replaced by collaborative systems and group decision-support systems (GDSSs) that focus more on establishing connections between people in organizations. These systems follow a kind of social paradigm. Combining both optimization- and socialcentered approaches is a topic of current research. However, even if such a hybrid approach can be developed, it will still miss an essential point: the emotional nature of group participants in decision-making tasks. We’ve developed a context-aware emotion based model to design intelligent agents for group decision-making processes. To evaluate this model, we’ve incorporated it in an agent-based simulator called ABS4GD (Agent-Based Simulation for Group Decision), which we developed. This multiagent simulator considers emotion- and argument based factors while supporting group decision-making processes. Experiments show that agents endowed with emotional awareness achieve agreements more quickly than those without such awareness. Hence, participant agents that integrate emotional factors in their judgments can be more successful because, in exchanging arguments with other agents, they consider the emotional nature of group decision making

Coordinating a team of agents in the forest firefighting domain

Documento confidencial. Não pode ser disponibilizado para consultaTese de mestrado. Inteligência Artificial e Sistemas Inteligentes. 2006. Faculdade de Engenharia. Universidade do Port

Issues of Emotion-Based Multi-Agent System

Emotion plays a significant contribution in perceptual processes of psychology and neuroscience research. Gently, area of Artificial Intelligent and Artificial Life in simulation and cognitive processes modeling uses this knowledge of emotions. Psychology and neuroscience researches are increasingly show how emotion plays an important role in cognitive processes. Gradually, this knowledge is being used in Artificial Intelligent and Artificial Life areas in simulation and cognitive processes modeling. Researchers are still not very clear about working of mind to generate emotion. Different people have different emotion at the same time and for same situation. Thus, to generate artificial emotion for agents is very complex task. Each agent and its emotion are autonomous but when we work on multi-agent system. Agents have to cooperate and coordinate with each other. In this paper we are discussing the role of emotions in multi-agent system while decision making, coordinate and cooperate with other agents. Also, we are about to discuss some major issues related to Artificial Emotion (AE) that should be considered when any research is proposed for it. In this paper we are discussing the role of emotions in multi-agent system while decision making, coordinate and cooperate with other agents. Also, we are about to discuss some major issues related to it that should be considered when any research is proposed for Artificial Emotion (AE)

Using emotions to enhance decision-making.

Abstract We present a novel methodology for decisionmaking by computer agents that leverages a computational concept of emotions. It is believed that emotions help living organisms perform well in complex environments. Can we use them to improve the decision-making performance of computer agents? We explore this possibility by formulating emotions as mathematical operators that serve to update the relative priorities of the agent's goals. The agent uses rudimentary domain knowledge to monitor the expectation that its goals are going to be accomplished in the future, and reacts to changes in this expectation by "experiencing emotions." The end result is a projection of the agent's long-run utility function, which might be too complex to optimize or even represent, to a time-varying valuation function that is being myopically maximized by selecting appropriate actions. Our methodology provides a systematic way to incorporate emotion into a decision-theoretic framework, and also provides a principled, domainindependent methodology for generating heuristics in novel situations. We test our agents in simulation in two domains: restless bandits and a simple foraging environment. Our results indicate that emotion-based agents outperform other reasonable heuristics for such difficult domains, and closely approach computationally expensive near-optimal solutions, whenever these are computable, yet requiring only a fraction of the cost

Modelling cultural dimensions and social relationships to create cultural synthetic characters

The work presented in this thesis investigates studies and theories of culture, social power and the relationship between culture and emotion studied by psychologists and anthropology. We operationalised a Cultural Dimension model, proposed by Hofstede, and Social Power and integrated them into an already existing architecture for autonomous agents called “FAtiMA”. The purpose of the adapted system is to generate culturally-specific behaviour in character interaction which is recognisably different to users. Two different experiments, with human participants, were conducted to investigate the perceived differences between two different groups of characters: with and without cultural parameters. The main result shows that users do recognise the differences in character behaviour between the two experimental cases, which demonstrates that our model is able to create culturally-specific synthetic characters

A Model of Trust, Moods, and Emotions in Multiagent Systems and its Empirical Evaluation

Abstract We study the interplay of moods, emotions, and trust in decisionmaking contexts characterized by commitments among agents. We develop a general approach representing the relationships among these concepts via a Bayesian network model. Our approach incorporates insights from the literature and provides a computational methodology for identifying improved Bayesian models. Based on observations from an empirical study, we motivate a refined Bayesian model involving the above-mentioned concepts that goes beyond the relationships known in the literature. Our findings include (1) the violation of a commitment affects trust more than its satisfaction; (2) goal satisfaction affects mood and emotion more than commitment satisfaction, but the outcome of a commitment affects trust more than the outcome of a goal; and (3) an agent's prior mood and trust affect whether it satisfies its commitments

Deploying Affect-Inspired Mechanisms to Enhance Agent Decision-Making and Communication

Computer agents are required to make appropriate decisions quickly and efficiently. As the environments in which they act become increasingly complex, efficient decision-making becomes significantly more challenging. This thesis examines the positive ways in which human emotions influence people’s ability to make good decisions in complex, uncertain contexts, and develops computational analogues of these beneficial functions, demonstrating their usefulness in agent decision-making and communication. For decision-making by a single agent in large-scale environments with stochasticity and high uncertainty, the thesis presents GRUE (Goal Re-prioritization Using Emotion), a decision-making technique that deploys emotion-inspired computational operators to dynamically re-prioritize the agent’s goals. In two complex domains, GRUE is shown to result in improved agent performance over many existing techniques. Agents working in groups benefit from communicating and sharing information that would otherwise be unobservable. The thesis defines an affective signaling mechanism, inspired by the beneficial communicative functions of human emotion, that increases coordination. In two studies, agents using the mechanism are shown to make faster and more accurate inferences than agents that do not signal, resulting in improved performance. Moreover, affective signals confer performance increases equivalent to those achieved by broadcasting agents’ entire private state information. Emotions are also useful signals in agents’ interactions with people, influencing people’s perceptions of them. A computer-human negotiation study is presented, in which virtual agents expressed emotion. Agents whose emotion expressions matched their negotiation strategy were perceived as more trustworthy, and they were more likely to be selected for future interactions. In addition, to address similar limitations in strategic environments, this thesis uses the theory of reasoning patters in complex game-theoretic settings. An algorithm is presented that speeds up equilibrium computation in certain classes of games. For Bayesian games, with and without a common prior, the thesis also discusses a novel graphical formalism that allows agents’ possibly inconsistent beliefs to be succinctly represented, and for reasoning patterns to be defined in such games. Finally, the thesis presents a technique for generating advice from a game’s reasoning patterns for human decision-makers, and demonstrates empirically that such advice helps people make better decisions in a complex game.Engineering and Applied Science

Uma Abordagem Baseada em Negociacao de Agentes para a Resolucao do Problema de Alocacao Dinamica de Navios em Ber os...

Esta tese propõe uma nova abordagem para solução de problemas logísticos dinâmicos e complexos. Esta nova abordagem é baseada na constatação do fato de que na sociedade humana muitos dos problemas são resolvidos por meio da interação e negociação entre diversos seres humanos, os quais sozinhos não poderiam resolver o problema e, no entanto, no momento que se unem e trabalham em conjunto, cada um com seus recursos e capacidades, eles conseguem resolvê-los. É proposta e definida uma abordagem inspirada na sociedade humana com diversos padrões de negociação. Fez-se, também, a opção por propor um padrão de negociação inspirado na emoção humana gratidão que tem como sua principal função ser um elemento para resolver eventuais casos de impasse/conflito. Com base nesta abordagem proposta, foi criado um sistema computacional que cria uma sociedade virtual de indivíduos que possuem recursos não compartilhados e, por meio de mensagens trocadas entre si, negociam até convergir para agendar as solicitações de operação recebidas (navio para carregar e descarregar contêiner). Este sistema foi desenvolvido com base na tecnologia de agentes e sistema multiagentes. Como exemplo prático, a abordagem proposta e o sistema elaborado foram usados para a solução do problema de gerenciamento da fila de navios de um Terminal Portuário de Contêiner. Vários testes foram realizados e os resultados foram apresentados e analisados para cada um dos padrões de negociação