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In this paper we introduce the concept of knowledge granularity and study the relationship between different knowledge representation schemes and the scaling problem. By scale to a task, we mean that an agent's planning system and knowledge representation scheme are able to generate the range of behaviors required by the task in a timely fashion. Action selection is critical to an agent performing a task in a dynamic, unpredictable environment. Knowledge representation is central to the agent's action selection process. It is important to study how an agent should adapt its methods of representation such that its performance can scale to different task requirements. Here we study the following issues. One is the knowledge granularity problem: to what detail should an agent represent a certain kind of knowledge if a single granularity of representation is to be used. Another is the representation scheme problem: to scale to a given task, should an agent represent its knowledge using a single granularity or a set of hierarchical granularities.

Dynamic power management is a technique to reduce power consumption of electronic systems by selectively shutting down idle components. In this paper, an intelligent approach is presented based on reinforcement learning to predict the best policy amongst the existing DPM policies. Reinforcement learning is a computational approach to understanding and automating goal-directed learning and decision-making. The effectiveness of this approach is demonstrated by an event-driven simulator, which is designed using JAVA for power-manageable embedded devices. The results of the experiments conducted in this regard establish that the proposed DPM scheme enhances power savings by 10 to 28%.

This research presents an optimization technique for route planning using simulated ant agents for dynamic online route planning and optimization of the route. It addresses the issues involved during route planning in dynamic and unknown environments cluttered with obstacles and objects. A simulated ant agent system (SAAS) is proposed using modified ant colony optimization algorithm for dealing with online route planning. It is compared with evolutionary technique on randomly generated environments, obstacle ratio, grid sizes, and complex environments. The evolutionary technique performs well in simple and less cluttered environments while its performance degrades with large and complex environments. The SAAS generates and optimizes routes in complex and large environments with constraints. The traditional route optimization techniques focus on good solutions only and do not exploit the solution space completely. The SAAS is shown to be an efficient technique for providing safe, short, and feasible routes under dynamic constraints and its efficiency has been tested in a mine field simulation with different environment configurations and is capable of tracking the moving goal and performs equally well as compared to moving target search algorithm.

Firstly, some novel understanding about agents and multi-agent systems, in particular, agentization and coordination, is presented to build a consensus. Afterwards, a brief overview is given of the papers that are included in this special issue.

In this paper, we apply market mechanism and agent to build grid resource management, where grid resource consumers and providers can buy and sell computing resource based on an underlying economic architecture. All market participants in the grid environment including computing resources and services can be represented as agents. Market participant is registered with a Grid Market Manager. A grid market participant can be a service agent that provides the actual grid service to the other market participants. Grid market participants communicate with each other by communication space that is an implementation of tuple space. In this paper, Grid agent model description is given. Then, the structure of Grid Market is described in detail. The design and implementation of agent oriented and market oriented grid resource management are presented in this paper.

Current desktop environments provide weak support for carrying out complex user-oriented tasks. Although individual applications are becoming increasingly sophisticated and feature-rich, users must map their high-level goals to the low-level operational vocabulary of applications, and deal with a myriad of routine tasks (such as keeping up with email, keeping calendars and websites up-to-date, etc.). An alternative vision is that of a personal cognitive assistant. Like a good secretary, such an assistant would help users accomplish their high-level goals, coordinating the use of multiple applications, automatically handling routine tasks, and, most importantly, adapting to the individual needs of a user over time. In this paper we describe the architecture and its implementation for a personal cognitive assistant called RADAR. Key features include.

(a) extensibility through the use of a plug-in agent architecture.

(b) transparent integration with legacy applications and data of today's desktop environments, and.

(c) extensive use of learning so that the environment adapts to the individual user over time.

In this paper we describe a paradigm for content-focused matchmaking, based on a recently proposed model for constraint acquisition and satisfaction. Matchmaking agents are constraint-based solvers that interact with other, possibly human, agents (Customers). The Matchmaker provides potential solutions ("suggestions") based on partial knowledge, while gaining further information about the problem itself from the other agent through the latter's evaluation of these suggestions. The dialogue between Matchmaker and Customer results in iterative improvement of solution quality, as demonstrated in simple simulations. We also show empirically that this paradigm supports "suggestion strategies" for finding acceptable solutions more efficiently or for increasing the amount of information obtained from the Customer. This work also indicates some ways in which the tradeoff between these two metrics for evaluating performance can be handled.

We propose an inference prevention agent as a tool that enables each of the databases in a distributed system to keep track of probabilistic dependencies with other databases and then use that information to help preserve the confidentiality of sensitive data. This is accomplished with minimal sacrifice of the performance and survivability gains that are associated with distributed database systems.

Today's supply chains increasingly involve complex sets of processes, objectives and constraints, and therefore agent-based architectures for supply chain management (SCM) become much more difficult to implement and maintain. The paper presents a multi-agent architecture for specifying, analyzing and developing SCM systems, in which asynchronous teams (A-Team) of problem solving agents exchange results within populations that provide effective management of information flows in supply chains, and cooperate to produce sets of non-dominated solutions that show the tradeoffs between objectives. Our approach distinguishes itself by improving problem-solving efficiency based on a diverse set of algorithms without complicated synthesis efforts, removing the focus from agent communication and coordination details, and improving reusability, flexibility and extensibility by supporting object-oriented and component-based programming style. We examine the effectiveness of the architecture through a real-world case study and experimental results.

This paper develops a hybrid model which provides a unified framework for the following four kinds of reasoning: 1) Zadeh's fuzzy approximate reasoning; 2) truth-qualification uncertain reasoning with respect to fuzzy propositions; 3) fuzzy default reasoning (proposed, in this paper, as an extension of Reiter's default reasoning); and 4) truth-qualification uncertain default reasoning associated with fuzzy statements (developed in this paper to enrich fuzzy default reasoning with uncertain information). Our hybrid model has the following characteristics: 1) basic uncertainty is estimated in terms of words or phrases in natural language and basic propositions are fuzzy; 2) uncertainty, linguistically expressed, can be handled in default reasoning; and 3) the four kinds of reasoning models mentioned above and their combination models will be the special cases of our hybrid model. Moreover, our model allows the reasoning to be performed in the case in which the information is fuzzy, uncertain and partial. More importantly, the problems of sharing the information among heterogeneous fuzzy, uncertain and default reasoning models can be solved efficiently by using our model. Given this, our framework can be used as a basis for information sharing and exchange in knowledge-based multi-agent systems for practical applications such as automated group negotiations. Actually, to build such a foundation is the motivation of this paper.

This paper presents an agent-based approach to identification of prediction models in two-dimensional data spaces. A number of agents are sent to the two-dimensional data space that people want to investigate. At the micro-level, every agent tries to build a local linear model by competing with others, and then at the macro-level all surviving agents build the global model by cooperating with each other. And a genetic algorithm is introduced for improving the global model built by the agents. Two examples that apply this approach are given. The advantages of this approach are it does not need people to give a certain formula in advance; and most of time, it can give more precise prediction models than those given by traditional methods.

In e-commerce applications, the magnitude of products and the diversity of venders cause confusion and difficulty for the common consumer to choose the right product from a trustworthy vender. Although people have recognized the importance of feedback and reputation for the trustworthiness of individual venders and products, they still have difficulty when they have to make a shopping decision from a massive number of options. This paper introduces fuzzy logic into rule definition for users' preferences and designs a novel agent-based decision system using fuzzy rules. This system can help users to find the right product recommendation from a trustworthy vender following users' own preferences.

The currently observed developments in Artificial Intelligence (AI) and its influence on different types of industries mean that human-robot cooperation is of special importance. Various types of robots have been applied to the so-called field of Edutainment, i.e., the field that combines education with entertainment. This paper introduces a novel fuzzy-based system for a human-robot cooperative Edutainment. This co-learning system includes a brain-computer interface (BCI) ontology model and a Fuzzy Markup Language (FML)-based Reinforcement Learning Agent (FRL-Agent). The proposed FRL-Agent is composed of (1) a human learning agent, (2) a robotic teaching agent, (3) a Bayesian estimation agent, (4) a robotic BCI agent, (5) a fuzzy machine learning agent, and (6) a fuzzy BCI ontology. In order to verify the effectiveness of the proposed system, the FRL-Agent is used as a robot teacher in a number of elementary schools, junior high schools, and at a university to allow robot teachers and students to learn together in the classroom. The participated students use handheld devices to indirectly or directly interact with the robot teachers to learn English. Additionally, a number of university students wear a commercial EEG device with eight electrode channels to learn English and listen to music. In the experiments, the robotic BCI agent analyzes the collected signals from the EEG device and transforms them into five physiological indices when the students are learning or listening. The Bayesian estimation agent and fuzzy machine learning agent optimize the parameters of the FRL agent and store them in the fuzzy BCI ontology. The experimental results show that the robot teachers motivate students to learn and stimulate their progress. The fuzzy machine learning agent is able to predict the five physiological indices based on the eight-channel EEG data and the trained model. In addition, we also train the model to predict the other students’ feelings based on the analyzed physiological indices and labeled feelings. The FRL agent is able to provide personalized learning content based on the developed human and robot cooperative edutainment approaches. To our knowledge, the FRL agent has not applied to the teaching fields such as elementary schools before and it opens up a promising new line of research in human and robot co-learning. In the future, we hope the FRL agent will solve such an existing problem in the classroom that the high-performing students feel the learning contents are too simple to motivate their learning or the low-performing students are unable to keep up with the learning progress to choose to give up learning.

In this paper, one of the informally described models of agent cooperation (Jennings, 1995) has been used to develop and formally specify a generic model of a cooperative agent (GCAM). The compositional development method for multi-agent systems DESIRE supported the principled design of this model of cooperation. To illustrate reusability of the generic model, two application domains have been addressed: collaborative engineering design, and Call Center support.

Using an information-theoretic framework, we examine how an intelligent agent, given an accurate model of its environment, synchronizes to the environment — i.e., comes to know in which state the environment is. We show that the total uncertainty experienced by the agent during the process is closely related to the transient information, a new quantity that captures the manner in which the environment's entropy growth curve converges to its asymptotic form. We also discuss how an agent's estimates of its environment's structural properties are related to its estimate of the environment entropy rate. If structural properties are ignored, the missed regularities are converted to apparent randomness. Conversely, using representations that assume too much memory results in false predictability.

Product development capability is more and more important for an enterprise in a knowledge-based economic era. In the philosophy of concurrent engineering, product development should be carried out in a concurrent way. Computer support is necessary for Concurrent Product Development (CPD). As an excellent tool to meet complex needs, CSCW has been used in CPD. But nearly all CSCW systems that have been developed so far concentrate on a more or less narrow sub-field of cooperative work. Thus, the need of integrated CSCW applications are apparent. The agent is a suitable programming paradigm that can be used to meet the complex needs. In this paper, a P-PROCE (Process, Product, Resource, Organization, Control & Evaluation) model is introduced for CPD firstly. By categorizing the agents of the multi-agent system (MAS) into different types of agent according to P-PROCE model and offering a structure of MAS, the CPD is mapped to MAS. The cooperation among agents is very important for MAS. In the paper, a two-layer cooperation structure of MAS is proposed. In the macro layer, agent based workflow control the CPD process and in the micro layer the entity agents interact with each other directly to fulfill the task. The key issues of these two cooperation layers are discussed in the paper. Component based structure of agent and an implemented case are also provided in the paper.

In the last few years there has been a growing need for organizations to build cooperative information systems. These organizations and their processes that manipulate vast quantities of information are based on heterogeneous, distributed and autonomous data sources. However, without appropriated techniques of negotiation, any execution of organizational information systems would yield to disjoining and error-prone behavior, while requiring excessive effort to build and maintain. In this paper, we present a flexible negotiation framework, which is based on social constraints and conversation plans. The infrastructure of this framework may well fit the negotiation needs of organizational information systems in a highly dynamic and unpredictable environment. This framework has been implemented as a negotiation service in our cooperative application environment. Some examples are given from manufacturing applications.

This article describes a successful experience on a software project of Technology Development Program (TDP) of Ministry Of Economic Affairs (MOEA) in Taiwan. It describes the design and implementation of a smart office task framework to automate system integration and to enable user-centric application. To achieve the goal of implicit knowledge accumulation and inheritance, we developed a service framework at the backend for integrating diverse systems seamlessly and an interface agent at the front for hiding complex efforts on manually binding different systems. Based on years of experience in components-based system development with multilayered flow architecture, the ACT (Advanced e-Commerce Technology) laboratory adapted Web Services to wrap various existing systems into a general RPC/RMI format, used ontology to define and integrate new functionalities semantically, and took advantage of agents to enable a user-centric office environment. The design rationale, application scenario, architecture, and future plan are presented.

In this article, agent based process management model is proposed, which is for the process management of knowledge worker and service workers in order to establish the basis for the new knowledge management system. In this article, we applied several methods from 6-Sigma and personal software process for personal process definition, process execution and process measurement. This study attempts to improve the process execution accuracy through process visualisation and standardisation and to accumulate the base data to improve the process through measuring the process execution. We proposed guidelines and detail procedures for developing three advisor agents for guiding the process definition, process execution and process measurement. We showed the simple case study applied our guidelines.

Trustworthy and reliable applications built using intelligent software agents aim to provide improved performance using its characteristics. Agents introduced in various architectures represent its functionality as functional elements of the architecture and shows the interaction between other components present in the architecture. The Internet of things (IoT) reveals as a frequent technology that allows accessing the physical objects present in the world. IoT systems utilize wireless sensor network to transmit and receive data by establishing communication. Wireless Sensor Networks transmits digital signals to the cyber-world for analyzing and processing the information into useful data by either formulating or communicating with the intelligent and innovative system. While talking about IoT and WSN, agents introduced in such environments assist in making decisions quickly by perceiving the input from the environment. The number of agents needed for an application depends upon the complexity of the problem. Multi-Agent architectures discussed in the article describe their association, roles, functionality and interaction. This paper gives a detailed survey of various agent/multi-agent learning architectures introduced over IoT and WSN. Moreover, this survey with the performance and the SWOT analysis on the Agent-based learning architecture helps the reader and paves a way to pursue research on Agent-based architectural deployment over IoT and WSN paradigms.