Autonomous Agents (autonomous + agent)

Distribution by Scientific Domains


Selected Abstracts


Action control of autonomous agents in continuous valued space using RFCN

ELECTRONICS & COMMUNICATIONS IN JAPAN, Issue 2 2008
Shinichi Shirakawa
Abstract Researchers on action control of autonomous agents and multiple agents have attracted increasing attention in recent years. The general methods using action control of agents are neural network, genetic programming, and reinforcement learning. In this study, we use neural network for action control of autonomous agents. Our method determines the structure and parameter of neural network in evolution. We proposed Flexibly Connected Neural Network (FCN) previously as a method of constructing arbitrary neural networks with optimized structures and parameters to solve unknown problems. FCN was applied to action control of an autonomous agent and showed experimentally that it is effective for perceptual aliasing problems. All of the experiments of FCN, however, are only in grid space. In this paper, we propose a new method based on FCN which can decide correction action in real and continuous valued space. The proposed method, called Real-valued FCN (RFCN), optimizes input,output functions of each unit, parameters of the input,output functions and speed of each unit. In order to examine its effectiveness, we applied the proposed method to action control of an autonomous agent to solve continuous-valued maze problems. © 2008 Wiley Periodicals, Inc. Electron Comm Jpn, 91(2): 31,39, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10032 [source]


Purpose-Based Expert Finding in a Portfolio Management System

COMPUTATIONAL INTELLIGENCE, Issue 4 2004
Xiaolin Niu
Most of the research in the area of expert finding focuses on creating and maintaining centralized directories of experts' profiles, which users can search on demand. However, in a distributed multiagent-based software environment, the autonomous agents are free to develop expert models or model fragments for their own purposes and from their viewpoints. Therefore, the focus of expert finding is shifting from the collection at one place as much data about a expert as possible to accessing on demand from various agents whatever user information is available at the moment and interpreting it for a particular purpose. This paper outlines purpose-based expert modeling as an approach for finding an expert in a multiagent portfolio management system in which autonomous agents develop expert agent models independently and do not adhere to a common representation scheme. This approach aims to develop taxonomy of purposes that define a variety of context-dependent user modeling processes, which are used by the users' personal agents to find appropriate expert agents to advise users on investing strategies. [source]


Preference-Based Constrained Optimization with CP-Nets

COMPUTATIONAL INTELLIGENCE, Issue 2 2004
Craig Boutilier
Many artificial intelligence (AI) tasks, such as product configuration, decision support, and the construction of autonomous agents, involve a process of constrained optimization, that is, optimization of behavior or choices subject to given constraints. In this paper we present an approach for constrained optimization based on a set of hard constraints and a preference ordering represented using a CP-network,a graphical model for representing qualitative preference information. This approach offers both pragmatic and computational advantages. First, it provides a convenient and intuitive tool for specifying the problem, and in particular, the decision maker's preferences. Second, it admits an algorithm for finding the most preferred feasible (Pareto-optimal) outcomes that has the following anytime property: the set of preferred feasible outcomes are enumerated without backtracking. In particular, the first feasible solution generated by this algorithm is Pareto optimal. [source]


Negotiating the Semantics of Agent Communication Languages

COMPUTATIONAL INTELLIGENCE, Issue 2 2002
Chris Reed
This article presents a formal framework and outlines a method that autonomous agents can use to negotiate the semantics of their communication language at run,time. Such an ability is needed in open multi,agent systems so that agents can ensure they understand the implications of the utterances that are being made and so that they can tailor the meaning of the primitives to best fit their prevailing circumstances. To this end, the semantic space framework provides a systematic means of classifying the primitives along multiple relevant dimensions. This classification can then be used by the agents to structure their negotiation (or semantic fixing) process so that they converge to the mutually agreeable semantics that are necessary for coherent social interactions. [source]


Augmented reality agents for user interface adaptation

COMPUTER ANIMATION AND VIRTUAL WORLDS (PREV: JNL OF VISUALISATION & COMPUTER ANIMATION), Issue 1 2008
István Barakonyi
Abstract Most augmented reality (AR) applications are primarily concerned with letting a user browse a 3D virtual world registered with the real world. More advanced AR interfaces let the user interact with the mixed environment, but the virtual part is typically rather finite and deterministic. In contrast, autonomous behavior is often desirable in ubiquitous computing (Ubicomp), which requires the computers embedded into the environment to adapt to context and situation without explicit user intervention. We present an AR framework that is enhanced by typical Ubicomp features by dynamically and proactively exploiting previously unknown applications and hardware devices, and adapting the appearance of the user interface to persistently stored and accumulated user preferences. Our framework explores proactive computing, multi-user interface adaptation, and user interface migration. We employ mobile and autonomous agents embodied by real and virtual objects as an interface and interaction metaphor, where agent bodies are able to opportunistically migrate between multiple AR applications and computing platforms to best match the needs of the current application context. We present two pilot applications to illustrate design concepts. Copyright © 2007 John Wiley & Sons, Ltd. [source]


A programming environment for behavioural animation

COMPUTER ANIMATION AND VIRTUAL WORLDS (PREV: JNL OF VISUALISATION & COMPUTER ANIMATION), Issue 5 2002
Frédéric Devillers
Abstract Behavioural models offer the ability to simulate autonomous agents like organisms and living beings. Psychological studies have shown that human behaviour can be described by a perception,decision,action loop, in which the decisional process should integrate several programming paradigms such as real time, concurrency and hierarchy. Building such systems for interactive simulation requires the design of a reactive system treating flows of data to and from the environment, and involving task control and preemption. Since a complete mental model based on vision and image processing cannot be constructed in real time using purely geometrical information, higher levels of information are needed in a model of the virtual environment. For example, the autonomous actors of a virtual world would exploit the knowledge of the environment topology to navigate through it. Accordingly, in this paper we present our programming environment for real-time behavioural animation which is compounded of a general animation and simulation platform, a behavioural modelling language and a scenario-authoring tool. Those tools has been used for different applications such as pedestrian and car driver interaction in urban environments, or a virtual museum populated by a group of visitors. Copyright © 2002 John Wiley & Sons, Ltd. [source]


Artificial Animals and Humans: From Physics to Intelligence

COMPUTER GRAPHICS FORUM, Issue 3 2002
Demetri Terzopoulos
The confluence of virtual reality and artificial life, an emerging discipline that spans the computational and biological sciences, has yielded synthetic worlds inhabited by realistic, artificial flora and fauna. Artificial animals are complex synthetic organisms that possess functional biomechanical bodies, sensors, and brains with locomotion, perception, behavior, learning, and cognition centers. Artificial humans and other animals are of interest in computer graphics because they are self-animating characters that dramatically advance the state of the art of production animation and interactive game technologies. More broadly, these biomimetic autonomous agents in their realistic virtual worlds also foster deeper, computationally oriented insights into natural living systems. [source]


Grids of agents for computer and telecommunication network management

CONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 5 2004
M. D. Assunção
Abstract The centralized system approach for computer and telecommunication network management has been presenting scalability problems along with the growth in the amount and diversity of managed equipment. Moreover, the increase in complexity of the services being offered through the networks also contributes to adding extra workload to the management station. The amount of data that must be handled and processed by only one administration point could lead to a situation where there is not enough processing and storage power to carry out an efficient job. In this work we present an alternative approach by creating a highly distributed computing environment through the use of Grids of autonomous agents to analyze large amounts of data, which reduce the processing costs by optimizing the load distribution and resource utilization. Copyright © 2004 John Wiley & Sons, Ltd. [source]


Action control of autonomous agents in continuous valued space using RFCN

ELECTRONICS & COMMUNICATIONS IN JAPAN, Issue 2 2008
Shinichi Shirakawa
Abstract Researchers on action control of autonomous agents and multiple agents have attracted increasing attention in recent years. The general methods using action control of agents are neural network, genetic programming, and reinforcement learning. In this study, we use neural network for action control of autonomous agents. Our method determines the structure and parameter of neural network in evolution. We proposed Flexibly Connected Neural Network (FCN) previously as a method of constructing arbitrary neural networks with optimized structures and parameters to solve unknown problems. FCN was applied to action control of an autonomous agent and showed experimentally that it is effective for perceptual aliasing problems. All of the experiments of FCN, however, are only in grid space. In this paper, we propose a new method based on FCN which can decide correction action in real and continuous valued space. The proposed method, called Real-valued FCN (RFCN), optimizes input,output functions of each unit, parameters of the input,output functions and speed of each unit. In order to examine its effectiveness, we applied the proposed method to action control of an autonomous agent to solve continuous-valued maze problems. © 2008 Wiley Periodicals, Inc. Electron Comm Jpn, 91(2): 31,39, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10032 [source]


Constructing deliberative agents with case-based reasoning technology

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 12 2003
J. M. Corchado
This article shows how autonomous agents may be constructed with the help of case-based reasoning (CBR) systems. The advantages and disadvantages of deliberative agents are discussed, and it is shown how to solve some of their inconveniences, especially those related to their implementation and adaptation. The Internet is one of the most popular vehicles for disseminating and sharing information through computer networks and it is influencing the business world. An agent-based solution is presented to show how the proposed technology may facilitate and improve an e-business strategy. © 2003 Wiley Periodicals, Inc. [source]


For the biotechnology industry, the penny drops (at last): genes are not autonomous agents but function within networks!

BIOESSAYS, Issue 12 2007
Adam S. Wilkins
No abstract is available for this article. [source]