A multi-expert model for dialogue and behavior control of conversational robots and agents

This paper presents an intelligence model for conversational service robots. It employs modules called experts, each of which is specialized to execute certain kinds of tasks such as performing physical behaviors and engaging in dialogues. Some of the experts take charge in understanding human utterances and deciding robot utterances or actions. The model enables switching and canceling tasks based on recognized human intentions, as well as parallel execution of several tasks. This model specifies the interface that an expert must have, and any kind of expert can be employed if it conforms to the interface. This feature makes the model extensible.     

A model of agents (robots) command behavior: The cognitive approach

The paper suggests a mathematical model of agents’ cooperation in dynamics, which employs agents’ utility functions and cognitive dissonance of their relations. The model is based on the theories of social psychologists investigating the behavior of people in small social groups and explaining the principles of their functioning and stability. We illustrate the suggested model via simulation of virtual soccer game of agents (robots). The performed simulation allows to model diverse aspects of agents’ cooperation and selfish behavior.     

Analyzing the emotional outcomes of the online search behavior with search engines

The affective component has been acknowledged as critical to understand information search behavior and user-computer interactions. There is a lack of studies that analyze the emotions that the user feels when searching for information about products with search engines. The present study analyzes the emotional outcomes of the online search process, taking into account the user’s (a) perceptions of success and effort exerted on the search process, (b) initial affective state, and (c) emotions felt during the search process. In addition, we identify profiles of online searchers based on the emotional outcomes of the search process, which allow us to differentiate the emotional processes and behavioral patterns that lead to such emotions. The results of the study stress the importance of the affective component of the online search behavior, given that these emotional outcomes are likely to influence all the subsequent actions that users perform on the Web.     

Distributing emotional services in Ambient Intelligence through cognitive agents

Ambient Intelligence (AmI) is a pervasive computing paradigm whose main aim is to design smart environments composed of invisible, connected, intelligent and interactive systems, which are naturally sensitive and responsive to the presence of people, providing advanced services for improving the quality of life. Nevertheless, AmI systems are more than a simple integration among computer technologies; indeed, their design can strongly depend upon psychology and social sciences aspects describing, analysing and forecasting the human being status during the system’s decision making. This paper introduces a novel methodology for AmI systems designing that exploits a service-oriented architecture whose functionalities are performed by a collection of so-called cognitive agents. These agents exploit a novel extension of Fuzzy Cognitive Maps benefiting on the theory of Timed Automata and a formal method for representing human moods in order to distribute emotional services able to enhance users’ comfort and simplify the human/systems interactions. As will be shown in experimental results, where a usability study and a confirmation of expectations test have been performed, the proposed approach maximizes the system’s usability in terms of efficiency, accuracy and emotional response.     

Specification by-example of virtual agents behavior

The development of virtual agents running within graphic environments which emulate real-life contexts may largely benefit from the use of visual specification by-example. To support this specification, the development system must be able to interpret the examples and cast their underlying rules into an internal representation language. This language must find a suitable trade-off among a number of contrasting requirements regarding expressiveness, automatic executability, and suitability to the automatic representation of rules deriving from the analysis of examples. A language is presented which attains this trade-off by combining together an operational and a declarative fragment to separately represent the autonomous execution of each individual agent and its interaction with the environment, respectively. While the declarative part permits to capture interaction rules emerging from specification examples, the operational part supports the automatic execution in the operation of the virtual environment. A system is presented which embeds this language within a visual shell to support a behavioral training in which the animation rules of virtual agents are defined through visual examples     

The simulation of an emotional robot implemented with fuzzy logic

Recently, researchers have tried to better understand human behaviors so as to let robots act in more human ways, which means a robot may have its own emotions defined by its designers. To achieve this goal, in this study, we designed and simulated a robot, named Shiau_Lu, which is empowered with six universal human emotions, including happiness, anger, fear, sadness, disgust and surprise. When we input a sentence to Shiau_Lu through voice, it recognizes the sentence by invoking the Google speech recognition method running on an Android system, and outputs a sentence to reveal its current emotional states. Each input sentence affects the strength of the six emotional variables used to represent the six emotions, one corresponding to one. After that, the emotional variables will change into new states. The consequent fuzzy inference process infers and determines the most significant emotion as the primary emotion, with which an appropriate output sentence as a response of the input is chosen from its Output-sentence database. With the new states of the six emotional variables, when the robot encounters another sentence, the above process repeats and another output sentence is then selected and replied. Artificial intelligence and psychological theories of human behaviors have been applied to the robot to simulate how emotions are influenced by the outside world through languages. In fact, the robot may help autistic children to interact more with the world around them and relate themselves well to the outside world.     

Group affinity based social trust model for an intelligent movie recommender system

As many researchers have taken an interest in social networks with the development of the user-generated web, trust management and its application have come into the spotlight. User information that is extracted by behavior patterns and user profiles provides the essential relationship between individuals. In this paper, we propose an intelligent movie recommender system with a social trust model. The proposed system is based on a social network for analyzing social relationships between users and generated group affinity values with user profiles. In experiments, the performance of this system is evaluated with precision-recall and F-measures.     

Multimodal behavior realization for embodied conversational agents

Applications with intelligent conversational virtual humans, called Embodied Conversational Agents (ECAs), seek to bring human-like abilities into machines and establish natural human-computer interaction. In this paper we discuss realization of ECA multimodal behaviors which include speech and nonverbal behaviors. We devise RealActor, an open-source, multi-platform animation system for real-time multimodal behavior realization for ECAs. The system employs a novel solution for synchronizing gestures and speech using neural networks. It also employs an adaptive face animation model based on Facial Action Coding System (FACS) to synthesize face expressions. Our aim is to provide a generic animation system which can help researchers create believable and expressive ECAs.     

A behavior language for story-based believable agents

A behavior language is a reactive planning language, based on the Oz Project language Hap, designed specifically for authoring believable agents-characters that express rich personality, and that, in this case, play roles in an interactive story called Facade.     

Cooperative behavior of various agents in dynamic environment

The multi-agent model is a model in which agents with limited ability cooperate each other to accomplish a goal. In this paper, we introduce a multi-agent model in which agents are created to imitate real ants. There are two different type of agents, each type of which has a particular task. We designed agents to communicate each other by using pheromone considering noise. On this model, we observed cooperative behavior of agents and evaluated conditions to make an order of behavior in the model.     

Evolution of visually guided behavior in artificial agents

Recent work on brightness, color, and form has suggested that human visual percepts represent the probable sources of retinal images rather than stimulus features as such. Here we investigate the plausibility of this empirical concept of vision by allowing autonomous agents to evolve in virtual environments based solely on the relative success of their behavior. The responses of evolved agents to visual stimuli indicate that fitness improves as the neural network control systems gradually incorporate the statistical relationship between projected images and behavior appropriate to the sources of the inherently ambiguous images. These results: (1) demonstrate the merits of a wholly empirical strategy of animal vision as a means of contending with the inverse optics problem; (2) argue that the information incorporated into biological visual processing circuitry is the relationship between images and their probable sources; and (3) suggest why human percepts do not map neatly onto physical reality.     

A simulation study with a combined network and production systems model of pilot behavior on an ILS-approach

A simulation study was conducted for determining information flow requirements of a pilot-cockpit interface during an ILS-approach. Study steps for modeling, simulating, and analyzing system processes are discussed. Starting with the problem definition which includes relevant flight processes, the network of pilot tasks, and performance measures, the conceptual model of processes is mathematically described by means of algebraic and difference equations as well as production systems. To transform the model into a simulation program, the simulation language SLAM is used. SLAM elements utilized for modeling flight processes and pilot tasks as well as the procedure of model validation are discussed. The analysis of simulation output data, i.e. state trajectories and task timelines, results in the determination of important information requirements useful in early stages of a design project.     

Asymptotic behavior in a heap model with two pieces

In a heap model, solid blocks, or pieces, pile up according to the Tetris game mechanism. An optimal schedule is an infinite sequence of pieces minimizing the asymptotic growth rate of the heap. In a heap model with two pieces, we prove that there always exists an optimal schedule which is balanced, either periodic or Sturmian. We also consider the model where the successive pieces are chosen at random, independently and with some given probabilities. We study the expected growth rate of the heap. For a model with two pieces, the rate is either computed explicitly or given as an infinite series. We show an application for a system of two processes sharing a resource, and we prove that a greedy schedule is not always optimal.     

Dynamical behavior of a vector-host epidemic model with demographic structure

In the paper, we propose a model that tracks the dynamics of many diseases spread by vectors, such as malaria, dengue, or West Nile virus (all spread by mosquitoes). Our model incorporates demographic structure with variable population size which is described by nonlinear birth rate and linear death rate. The stability of the system is analyzed for the existence of the disease-free and endemic equilibria points. We find the basic reproduction number R₀ in terms of measurable epidemiological and demographic parameters is the threshold condition that determines the dynamics of disease infection: if R₀<1 the disease fades out, and for R₀>1 the disease remains endemic. The threshold condition provides important guidelines for accessing control of the vector diseases, and implies that it is an efficient way to halt the spread of vector epidemic by reducing the carrying capacity of the environment for the vector and the host. Moreover, sufficient conditions are also obtained for the global stability of the unique endemic equilibrium E^*.     

The emotional hearing aid: an assistive tool for children with Asperger syndrome

People diagnosed along the autistic spectrum often have difficulties interacting with others in natural social environments. The emotional hearing aid is a portable assistive computer-based technology designed to help children with Asperger syndrome read and respond to the facial expressions of people they interact with. The tool implements the two principal elements that constitute ones ability to empathize with others: the ability to identify a persons mental state, a process known as mind-reading or theory of mind, and the ability to react appropriately to it (known as sympathizing). An automated mind-reading system attributes a mental state to a person by observing the behaviour of that person in real-time. Then the reaction advisor suggests to the user of the emotional hearing an appropriate reaction to the recognized mental state. This paper describes progress in the development and validation of the emotional hearing aid on two fronts. First, the implementation of the reaction advisor is described, showing how it takes into account the persistence, intensity and degree of confidence of a mental state inference. Second, the paper presents an experimental evaluation of the automated mind-reading system on six classes of complex mental states. In light of this progress, the paper concludes with a discussion of the challenges that still need to be addressed in developing and validating the emotional hearing aid.     

Application of an emotional neural network to facial recognition

In our attempts to model human intelligence by mimicking the brain structure and function, we overlook an important aspect in human cognition, which is the emotional factor. It may currently sound unthinkable to have emotional machines; however, it is possible to simulate certain artificial emotions with the aim of improving machine learning. This paper investigates the efficiency of an emotional neural network, which uses a modified back propagation learning algorithm. The modifed algorithm, namely the emotional BP learning algorithm, has two emotional parameters, anxiety and confidence, that are modeled during machine learning and decision making. The emotional neural network will be implemented to a facial recognition problem using images of faces with different orientations and contrast levels, and its performance will be compared to that of a conventional neural network. Experimental results suggest that artificial emotions can be successfully modeled and efficiciently implemented to improve neural networks learning and generaliztion.     

Realization of emergent behavior in collective autonomous mobile agents using an artificial neural network and a genetic algorithm

This paper proposes a pursuit system that utilizes the artificial life concept where autonomous mobile agents emulate the social behavior of animals and insects and realize their group behavior. Each agent contains sensors to perceive other agents in several directions, and decides its behavior based on the information obtained by these sensors. In this paper, a neural network is used for behavior decision controlling. The input of the neural network is decided by the existence of other agents, and the distance to the other agents. The output determines the directions in which the agent moves. The connection weight values of this neural network are encoded as genes, and the fitness individuals are determined using a genetic algorithm. Here, the fitness values imply how much group behavior adequately fit the goal and can express group behavior. The validity of the system is verified through simulation. Also in this paper, we have observed the agents emergent behavior during simulation.This paper was supported by WonKwang University in 2004.     

扰动自适应模型预测控制的频域特性分析

扰动模型的准确性对模型预测控制算法的扰动抑制能力有重要影响,当前模型预测控制广泛采用的阶跃扰动模型不能准确描述进入系统的不可测扰动,扰动抑制能力有限.自适应扰动模型可以较好的描述不可测扰动,提高对扰动的预估和抑制能力.本文对采用自适应时间序列扰动模型的预测控制进行分析,研究了扰动自适应预测控制(DMCA)的闭环结构以及带宽、灵敏度函数等频域指标与控制器抗扰性能的关系.带宽大的系统抑制扰动的速度快,灵敏度函数幅值越小则对扰动的抑制能力越强.理论分析和仿真结果表明与动态矩阵控制(DMC)相比,采用自适应扰动模型的DMCA算法能够更好的预测和抑制扰动,被控变量偏离设定值的最大幅度降低60％,带宽是DMC的1.5倍、调节速度更快,在低频段有较小的灵敏度函数值.自适应扰动模型提升了DMCA控制器的扰动抑制性能,对保障系统安全平稳运行和增加效益有重要意义.