Agent-based Environment Research Articles

Performance evaluation of an auction-based job allocation model for small- and medium-sized enterprises

This paper extends the application scope of a previously developed auction-based shop floor control model to the enterprise level and evaluates its performance for the coordination and collaboration of spatially distributed Small- and Medium-sized Enterprises (SMEs). The model uses the contract net protocol to facilitate the negotiation process among SMEs and consists of two main components: task proposal formulation and bid formulation. An agent-based simulation environment is developed in ProModel to evaluate the impact of two negotiation factors and due date slack of tasks on tardiness, earliness and absolute due date deviation of tasks. A mixed-level factorial Design of Experiments (DOE) is carried out by considering different levels of α, β and K, and the results of the simulation study are analysed using Analysis of Variance (ANOVA) in Minitab. This study shows that the auction-based model is effective for the task allocation among SMEs, yet it is necessary to fine-tune the negotiation factors based on the due-date-related performance measures.

A PROTOTYPE GRID FRAMEWORK FOR THE CHEMICAL PROCESS INDUSTRIES

ABSTRACT This article presents a GRID framework for distributed computations in the chemical process industries. We advocate a generic agent-based GRID environment in which chemical processes can be represented, simulated, and optimized as a set of autonomous, collaborative software agents. The framework features numerous advantages in terms of scalability, software reuse, security, and distributed resource discovery and utilization. It is a novel example of how advanced distributed techniques and paradigms can be elegantly applied in the area of chemical engineering to support distributed computations and discovery functions in chemical process engineering. A prototype implementation of the proposed framework for chemical process design is presented to illustrate the concepts.

PODC: Paradigm-oriented distributed computing

We describe an environment for distributed computing that uses the concept of well-known paradigms. The main advantage of paradigm-oriented distributed computing (PODC) is that the user only needs to specify application-specific sequential code, while the underlying infrastructure takes care of the parallelization and distribution. The main features of the proposed approach, called PODC, are the following: (1) It is intended for loosely coupled network environments, not specialized multiprocessors; (2) it is based on an infrastructure of mobile agents; (3) it supports programming in C, rather than a functional or special-purpose language, and (4) it provides an interactive graphics interface through which programs are constructed, invoked, and monitored. We discuss five paradigms presently supported in PODC: the bag-of-tasks, branch-and-bound search, genetic programming, finite difference, and individual-based simulation. We demonstrate their use, implementation, and performance within the mobile agent-based PODC environment.

Simulating the impact of a molecular ‘decision-process’ on cellular phenotype and multicellular patterns in brain tumors

Experimental evidence indicates that human brain cancer cells proliferate or migrate, yet do not display both phenotypes at the same time. Here, we present a novel computational model simulating this cellular decision-process leading up to either phenotype based on a molecular interaction network of genes and proteins. The model's regulatory network consists of the epidermal growth factor receptor (EGFR), its ligand transforming growth factor- α ( TGF α ), the downstream enzyme phospholipaseC- γ ( PLC γ ) and a mitosis-associated response pathway. This network is activated by autocrine TGF α secretion, and the EGFR-dependent downstream signaling this step triggers, as well as modulated by an extrinsic nutritive glucose gradient. Employing a framework of mass action kinetics within a multiscale agent-based environment, we analyse both the emergent multicellular behavior of tumor growth and the single-cell molecular profiles that change over time and space. Our results show that one can indeed simulate the dichotomy between cell migration and proliferation based solely on an EGFR decision network. It turns out that these behavioral decisions on the single cell level impact the spatial dynamics of the entire cancerous system. Furthermore, the simulation results yield intriguing experimentally testable hypotheses also on the sub-cellular level such as spatial cytosolic polarization of PLC γ towards an extrinsic chemotactic gradient. Implications of these results for future works, both on the modeling and experimental side are discussed.

Agent‐Based Web Services Framework and Development Environment

The current Web services technologies have not exploited sufficient semantics and approaches to dynamic service‐oriented operations in open environments. This paper argues that such operations can be realized through agent‐oriented interaction approaches. The key challenge is to develop an integration framework for the two paradigms, agent‐ and service‐oriented, in a way that capitalizes on their individual strengths. This paper proposes the notion of agent‐based Web services (AWS). We address several critical issues, including the appropriate architectural framework and the structure of its main elements (agent‐based Web services), their meta‐model, supporting technologies, integration method, and implementation approach. An integrated development environment for this framework called SOAStudio has been developed and tested by implementing a case study of a reverse auction e‐marketplace.

A Time-Bound Framework for Negotiation and Decision Makingof Virtual Manufacturing Enterprise

Virtual manufacturing has 2 characteristics as an agent-based electronic commerce environment: dynamic nature of resource status and variety of agents' decision-making (i.e., scheduling) model. To reflect the characteristics, a relevant negotiation protocol should be designed and an appropriate decision-making model should be developed. In this article, from the perspective of a sales agent that is a middle man between customers and manufacturers in a virtual manufacturing environment, we provide a case study that suggests a time-bound framework for external negotiation between sales agents and customer agents, and internal cooperation between sales agents and manufacturing agents. We assume a job shop as the production model of a virtual manufacturing enterprise and formulate the optimal order selection problem with mixed integer programming, but its computation time is not acceptable for real-world problems. For this time-constrained decision making, we develop a genetic algorithm as an anytime problem-solving method for the scheduling of the production model, which shows a reasonable computation time for real-world cases and good incremental problem-solving capability.

Optimizing agent-based meeting scheduling through preference estimation

Meeting scheduling is a routine task that needs to be performed quite regularly and frequently within any organization. Unfortunately, this task can be quite tedious and time-consuming, potentially requiring a several rounds of negotiations among many people on the meeting date, time and place before a meeting can finally be confirmed. The objective of our research is to create an agent-based environment within which meeting scheduling can be performed and optimized. For meeting scheduling, we define optimality as the solution that has the highest average preference level among all the possible choices. Our model tries to mimic real life in that an individual's preferences are not made public. Without complete information, traditional optimal algorithms, such as A * will not work. In this paper, we present a novel “preference estimation” technique that allows us to find optimal solutions to negotiations problems without needing to know the exact preference models of all the meeting participants beforehand. Instead, their preferences are “estimated” and built on the fly based on observations of their responses during negotiation. Another unique contribution is the use of “preference rules” that allow preferences to change dynamical as scheduling decisions are made. This mimics changing preferences as schedule gets filled. This paper uses two negotiation algorithms to compare the effect of “preference estimation”—one that is based on negotiation through relaxation and the other that extends this with preference estimations. Simulations were then performed to compare these algorithms.

The Effects of Image and Animation in Enhancing Pedagogical Agent Persona

The purpose of this experimental study was to test the role of image and animation on: a) learners' perceptions of pedagogical agent persona characteristics (i.e., extent to which agent was person-like, engaging, credible, and instructor-like); b) agent value; and c) performance. The primary analysis consisted of two contrast comparisons: 1) comparing the presence/absence of agent image; and 2) comparing static versus animated agent images. In the study, 75 preservice teachers developed an instructional plan for a case study involving designing economics instruction for the concepts of supply and demand within the MIMIC (Multiple Intelligent Mentors Instructing Collaboratively) agent-based environment. Overall, animation was found to be beneficial for all four persona characteristics, but not always as the single best implementation. For the agent to be perceived as instructor-like, a strong positive effect was found for the presence of agent animation. Agent credibility was facilitated by either a static or animated image, with the presence of an image being critical. Perceptions of the agent as engaging and person-like were also improved by animation, although person-like was not affected by presence/absence of image. Results are discussed in terms of implementing anthropomorphic pedagogical agents to support computer-based instruction.

ATUCG — AN AGENT–BASED ENVIRONMENT FOR AUTOMATIC ANNOTATION OF GENOMES

This work reports on the ATUCG environment (Agent-based environmenT for aUtomatiC annotation of Genomes). It consists of three layers, each having several agents in charge of performing repetitive and time-consuming tasks. Layer I aims at automating the tasks behind the process of finding ORFs (Open Reading Frames). Layer II (the core of our approach) is associated with three main tasks: extraction and formatting of data, automatic annotation of data regarding profiles or families of proteins, and generation and validation of rules to automatically annotate the Keywords field in the SWISS-PROT database. Layer III permits the user to check the correctness of the automatic annotation. This environment is being designed having the sequencing of the Mycoplasma hyopneumoniae in mind. Thus examples are presented using data of organisms of the Mycoplasmataceae family. We have concentrated the developments in layer II because this is the most general one and because it focusses on machine learning algorithms, a characteristic which is not usual in annotation systems. Results regarding this layer show that with learning (individual or colaborative), agents are able to generate rules for annotation which achieve better results than those reported in the literature.

A Security Framework for the Agent-Based Environment for Linking Simulations (ABELS)

The Agent-Based Environment for Linking Simulations (ABELS) framework is designed to allow independent and physically distributed simulations and other data resources to form a “data cloud” for the exchange of information. In particular, it uses a distributed brokering system to facilitate dynamic linkings between independently designed, autonomous participants, without requiring the use of stringent standards to which participants must conform. This article discusses the various security threats that can arise in the ABELS architecture and presents the current design and implementation of a security framework for the system.

A Security Framework for the Agent-Based Environment for Linking Simulations (ABELS)

A Security Framework for the Agent-Based Environment for Linking Simulations (ABELS)

Evaluating NDF-based negotiation mechanism within an agent-based environment

We investigate the properties of a negotiation mechanism that is based on negotiation decision functions (NDFs) in an agent-based system. The study employs both analytical and simulation approaches. Analysis and evaluation of negotiation tactics and strategies indicated that negotiation deadline significantly influences the convergence performance of the negotiation. Some important properties of the negotiation convergence are analyzed, and a set of experiments are carried out among agents for typical negotiation tactics and strategies to investigate negotiation fairness with respect to the mean difference of deal values and convergence characteristics. A preliminary study on the efficiency of NDF as compared to TAC agents and Pareto optimal is done as well.

Agent-based modelling environment for spatial decision support

The goal of Spatial Decision Support Systems (SDSS) is to assist decision-makers as they generate alternative solutions to a variety of semi-structured geographical problems, and to evaluate these solutions with the help of applicable data and analytical models. Most existing SDSS, however, support only a limited number of decision-making environments, and are not designed to utilize web-accessible repositories of spatial data and models. These limitations are overcome through the use of ‘software agents’ within an agent-oriented modelling framework, called ‘Distributed Intelligent Geographical Modelling Environment (DIGME)’. The utility of this framework is demonstrated through the development of an SDSS to evaluate the ecological and economic impacts of agricultural policy for the Cache River watershed of southern Illinois.

Agent-Based E-Learning Course Recommendation: Matching Learner Characteristics with Content Attributes

In this article an agent framework for discovery and recommendation of e-learning courses in agent-based learning environments is presented. In the context of this framework, a way to model and store the learner model and the content attributes metadata information using international specifications and standards is studied, and two methodologies for constructing a matching mechanism to select courses most suited to the learner are introduced. These mechanisms are applied to the case of a learning community within the context of the NEMO project, and the results are discussed.

STEP-based integration of feature recognition and design-by-feature for manufacturing applications in a concurrent engineering environment

Manufacturing applications typically require that a part be created in a design-by-feature system and described through STEP-based manufacturing features. This paper presents a manufacturing feature recognition processor that utilises the high-level information retrieved from a design-by-feature system to generate a STEP-based manufacturing feature model efficiently. In the processor, the interacting and interrelating relationships between features are represented and handled, so problems such as the low efficiency and error proneness of traditional feature recognition systems are overcome. Post-processing of manufacturability evaluation can dynamically evaluate the recognised features and provide feedback for the design-by-feature system to modify an initial poor design plan. The processor generates a neutral file defined in STEP EXPRESS to describe the manufacturing feature model for the downstream activities to share the information. The design feature models and feature recognition processor are wrapped in an agent-based concurrent engineering environment to exchange information and coordinate their activities in a distributed way.

Introduction

This introduction attempts to convey a sense of the possible, or near possible, with respect to the cutting edge of computer-based methods supporting social science research and inquiry. In addition, it is also intended to reflect computationally inspired perspectives, metaphors, models, and theories that may affect society. To that end, this article, through the introduction of the authors who contributed to this volume, explores the role played by agency-level computational methods, as opposed to aggregate level, in reshaping the practice and use of social science/inquiry. Agency-focused methods as used in this introduction not only emphasize the action of agents within an artificial environ but also attempt to encompass approaches beyond the cellular automata methods typically associated with agent-based modeling environments.

Intelligent agents for a lifelong learning environment in information technologies

This paper presents the design issues of CASSIEL, a lifelong learning environment developed at the Universidad de Las Americas-Puebla. CASSIEL (Computer Assisted Intelligent Environment for Learning) is a prototype that provides a lifelong learning environment that keeps the awareness in universities about the real needs and developments of software industries and in IT industries, about the latest advancements in computer science. CASSIEL represents the paradigm of a CSCL (Computer Supported Collaborative Learning) environment based on AI (Artificial Intelligence) techniques applied to the development of software agents that promote and support lifelong learning. CASSIEL has been designed as an agent-based environment where software agents have been modelled in order to support the development of the lifelong learning skills of its members and the social construction of knowledge in their community, maintaining awareness of relevant issues of IT (Information Technology) and communication and cooperation among the participants.

A Distributed Agent-based Simulation Environment for Interference Detection and Resolution

In this paper, a two-level architecture is developed and implemented for the interactive simulation and evaluation of a distributed agent-based simulation environment designed to detect and resolve interfer ence in naval radar units. The Java-based simulation is designed as a two level software architecture incor porating: (i) multiple radars on each ship controlled through a lower-level intra-ship interference control module, and (ii) multiple ships in a group coordi nated through a higher-level interference control module. Each ship's interference detection and reso lution mechanism, termed a control agent, is com posed of these two separate levels, which dynamically coordinate to detect and then resolve interference problems using three different coordination strate gies—a Master-Slave, a Locally Autonomous and a simple Negotiation mode.

Software agents and wireless E-commerce

Software agent technology will become a necessity to e-commerce (traditional or wireless) rather than luxury. We propose an agent-based environment, which we call E-Commerce through Wireless Devices (E-CWE), that allows users, stationary or mobile, to submit their requests for services offered by providers. The E-CWE environment consists of a reception platform including a repository of service descriptions. A supervisor-agent is in charge of this repository. Acting on behalf of their users, user-agents are dynamically created in this platform. In the E-CWE environment, security is achieved in two steps: securing service access and securing payment. A prototype of a Travel Planning Agent (TPA) system is proposed to evaluate the performance of the environment.

A framework for linking distributed simulations using software agents

The paper presents the basic ideas behind the use of software agent technology for distributed simulation and data assimilation. A software agent is an autonomous computer program that operates on behalf of someone or something. A mobile agent has the ability to migrate during execution from machine to machine in a heterogeneous network, while a stationary agent executes only on the system on which it began execution. To understand the role of agents in distributed simulation, note that simulations often operate on static datasets and data sources. Many simulations would produce more accurate results if they could access dynamically changing data from other sources, such as sensors or even other simulations. From the perspective of one simulation, other simulations are data resources, producing information possibly relevant to the past, present, or future of the system being modeled. Software agents allow dynamic linking between distributed simulations and efficient monitoring of and access to remote data resources. Specifically, they conserve bandwidth, provide custom operations without precompiling or preloading, and adapt to support disconnected operations. The paper describes the development of a software agent based framework for dynamically linking distributed simulations and other remote data resources. The framework called ABELS (Agent-Based Environment for Linking Simulations) allows independently designed simulations to communicate seamlessly with no a priori knowledge of the details of other simulations and data sources. We discuss our architecture and current implementation developed using the D'Agents mobile agent system.