The study of multi-agent systems (MAS) focuses on systems in which many intelligent agents interact with each other. These agents are considered to be autonomous entities such as software programs or robots. Their interactions can either be cooperative (for example as in an ant colony) or selfish (as in a free market economy). This book assumes only basic knowledge of algorithms and discrete maths, both of which are taught as standard in the first or second year of computer science degree programmes. A basic knowledge of artificial intelligence would useful to help understand some of the issues, but is not essential. The books main aims are: To introduce the student to the concept of agents and multi-agent systems, and the main applications for which they are appropriate To introduce the main issues surrounding the design of intelligent agents To introduce the main issues surrounding the design of a multi-agent society To introduce a number of typical applications for agent technology

An Introduction to MultiAgent Systems.

The purpose of this paper is to develop a collaborative agent-based web architecture and an agent-based green IS assessment tool to aid information technology (IT) practitioners in data centers assess their current green information systems (IS) practice toward attaining sustainability.,The methodology comprises that the collaborative agent-based web architecture, agents’ algorithm and the green IS assessment tool, which is validated by employing focus group questionnaire targeting IT practitioners in seven Malaysian-based enterprises that have an in-house data centers. With 105 valid samples at hand, descriptive analysis and exploratory factor analysis was utilized to determine the applicability of the implemented agent-based green IS assessment tool.,Findings reveal that the agent-based green IS assessment tool possesses the capability to evaluate benchmark and rate enterprise data centers current green IS practice. Additional findings indicate that the agent-based green IS assessment tool provide suggestions on how green IS practice can be improved in enterprise data centers.,This study only collected data from 105 IT practitioners in enterprise data centers based in Malaysia; as such results from this research cannot be generalized to other countries. Moreover, the developed collaborative agents for green IS practice assessment can only be fully deployed after domain experts has added green IS practice assessment questions and alternative answers.,This study presents an autonomous agent-based green IS assessment tool that supports the assessment of enterprise toward inclusion of sustainability considerations to enhance enterprise environmental performance.,This study provides empirical evidence for data centers efficacy leading toward a greener society for environmental conservation for future generations to come.,This study creates awareness by presenting the green IS practice to be implemented by IT practitioners in data centers. In addition, the agent-based green IS assessment tool provides a web-based platform for promoting environmental sustainability by supporting data centers toward evaluating, benchmarking and rating their current green IS practices.

A collaborative agent based green IS practice assessment tool for environmental sustainability attainment in enterprise data centers

Given recent advances in information technology and artificial intelligence, web-based education systems have became complementary and, in some cases, viable alternatives to traditional classroom teaching. The popularity of these systems stems from their ability to make education available to a large demographics (see MOOCs). However, existing systems do not take advantage of the personalization which becomes possible when web-based education is offered: they continue to be one-size-fits-all. In this paper, we aim to provide a first systematic method for designing a personalized web-based education system. Personalizing education is challenging: (i) students need to be provided personalized teaching and training depending on their contexts (e.g. classes already taken, methods of learning preferred, etc.), (ii) for each specific context, the best teaching and training method (e.g type and order of teaching materials to be shown) must be learned, (iii) teaching and training should be adapted online, based on the scores/feedback (e.g. tests, quizzes, final exam, likes/dislikes etc.) of the students. Our personalized online system, e-Tutor, is able to address these challenges by learning how to adapt the teaching methodology (in this case what sequence of teaching material to present to a student) to maximize her performance in the final exam, while minimizing the time spent by the students to learn the course (and possibly dropouts). We illustrate the efficiency of the proposed method on a real-world eTutor platform which is used for remedial training for a Digital Signal Processing (DSP) course.

pdf/etutor-online-learning-for-personalized-education-5g0znsincf.pdf

eTutor: Online Learning for Personalized Education

Education is incorporating more and more of the capabilities provided by the Internet. One such move is the incorporation of 3D virtual worlds in the learning environment. Another is the increasing development of multiagent systems that support the learner or the tutor. Integrating pedagogically based multiagent systems with 3D virtual worlds could provide a more engaging immersive learning environment. This paper explores the feasibility of integrating a 3D virtual world with a pedagogical multiagent system named QuizMASter, an educational game for elearning that helps students learn their course material through friendly competition. The integration was developed by devising, implementing and testing an approach using open source technologies, namely, Open Wonderland and JADE. The result is encouraging as the integration is technically feasible, not overly difficult and opens a door to further integration opportunities.

An Approach for Integrating 3D Virtual Worlds with Multiagent Systems

A Case-Based Reasoning (CBR) is defined as an Artificial Intelligent technique that has a problem-solving ability by reasoning previously experienced cases to solve new cases. The CBR is considered as one of the successful techniques that are applied in a widespread of problem-solving tasks and domains. However, the CBR in unknown or poorly archived cases suffers from uncertainty and imprecision. Additionally, the CBR is inefficient in performing partial reasoning and revision to distributed and dynamic problems. These problems entail flexible problem-solving and management architecture. The researchers integrate Multi-agent-systems (MAS) within the CBR to increase the ability of the CBR to solve problems that require agents' abilities such as interaction, autonomy, and flexibility. Consequently, in this paper, we have surveyed various techniques and methods that integrate MAS in CBR (CBR-MAS) to solve different challenges in different domains. The paper outcomes two main approaches of CBR-MAS: a number of agents are integrated on the reasoning steps of the CBR cycle or a CBR or CBRs is integrated on the run cycle of an individual agent or a MAS.

A Survey of Multi-agent Systems and Case-Based Reasoning Integration

In this paper we present our approach in the field of Intelligent Tutoring System (ITS), in fact the risk of dropping out for learners have emerged as crucial issues to be solved. So it is necessary to ensure an individualized and continuous learner's follow-up during learning process. Several research effort has been spent on the development of ITS. However the available literature does not generally concentrate on the individual realtime continuous follow up of learners. Our contribution in this field is to design and implement a computer system able to initiate learning and provide an individualized monitoring of learners. This approach involves 1) the use of Dynamic Case Based Reasoning to retrieve the past experiences that are similar to the learners' traces (traces in progress), and 2) the use of Multi-Agents System. Our Work focuses on the use of the learner traces. When interacting with the platform, every learner leaves his/her traces in the machine. The traces are stored in database, this operation enriches collective past experiences. Via monitoring, comparing and analyzing these traces, the system keeps a constant intelligent watch on the platform, and therefore it detects the difficulties hindering progress, and it avoids possible dropping out. The system can support any learning subject.

Intelligent tutoring systems founded of incremental dynamic case based reasoning and multi-agent systems (ITS-IDCBR-MAS)

This paper presents a system designed to help deciders manage cases of crisis. The system represents, characterises and interprets the dynamic evolution of a given situation and displays the results of its analysis. The core of the system is made of three multiagent systems (MAS): one MAS for the static and dynamic representation of the current situation, the second MAS for dynamically regrouping sets of agents of the former MAS and the upper MAS for matching results between the second MAS and scenarios stored in the persistent memory of the system in order to have a deeper analysis of the situation. The case based reasoning of this last MAS sends its results to the user as a view of the current situation linked to some views of similar situations. In this paper, we will focus on the representation MAS.

From Three MultiAgent Systems to One Decision Support System.

Towards an Architecture for the Representation of Dynamic Situations.

In E-learning, there is still the problem of knowing how to ensure an individualized and continuous learner's follow-up during learning process, indeed among the numerous tools proposed, very few systems concentrate on a real time learner's follow-up. Our work in this field develops the design and implementation of a Multi-Agent Systems Based on Dynamic Case Based Reasoning which can initiate learning and provide an individualized follow-up of learner. When interacting with the platform, every learner leaves his/her traces in the machine. These traces are stored in a basis under the form of scenarios which enrich collective past experience. The system monitors, compares and analyses these traces to keep a constant intelligent watch and therefore detect difficulties hindering progress and/or avoid possible dropping out. The system can support any learning subject. The success of a case-based reasoning system depends critically on the performance of the retrieval step used and, more specifically, on similarity measure used to retrieve scenarios that are similar to the course of the learner (traces in progress). We propose a complementary similarity measure, named Inverse Longest Common Sub-Sequence (ILCSS). To help and guide the learner, the system is equipped with combined virtual and human tutors.

Intelligent tutoring systems founded on the multi-agent incremental dynamic case based reasoning

There are many learning management systems all over the world. But these conventional E-learning platforms aim at students who are used to work on their own. Our students are young (19 years old – 22 years old), and in their first year at the university. Following extensive interviews with our students, we have designed GE3D, an E-learning platform, according to their expectations and our criteria. In this paper, we describe the students' demands, resulting from the interviews. Then, we present our virtual campus. Even if our platform uses some elements coming from the 3D games world, it is always a pedagogical tool. Using this technology, we developed a 3D representation of the real world. We also propose a process of a lecture on the Programmable Logic Controllers (PLC's) in this new universe. GE3D is a multi-users tool, with a synchronous technology, an intuitive interface for end-users and an embedded Intelligent Tutoring System to support learners.

GE3D: A virtual campus for technology-enhanced learning

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Patrick Person

Author Tools

Chat about Author