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open access Open Access

Autonomous Agents and Multi-Agent Systems — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Artificial Intelligence #102 of 227 down down by 45 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 154 Published Papers | 578 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 23/06/2020
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Related Journals

open access Open Access
recommended Recommended

SAGE

Quality:  
High
CiteRatio: 2.4
SJR: 0.286
SNIP: 0.689
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Hindawi

Quality:  
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CiteRatio: 5.0
SJR: 0.371
SNIP: 1.169
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recommended Recommended

IEEE

Quality:  
High
CiteRatio: 16.5
SJR: 1.834
SNIP: 5.821

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

1.342

5% from 2018

Impact factor for Autonomous Agents and Multi-Agent Systems from 2016 - 2019
Year Value
2019 1.342
2018 1.419
2017 1.176
2016 1.606
graph view Graph view
table view Table view

3.8

21% from 2019

CiteRatio for Autonomous Agents and Multi-Agent Systems from 2016 - 2020
Year Value
2020 3.8
2019 4.8
2018 4.2
2017 4.1
2016 4.8
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 5% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has decreased by 21% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.432

28% from 2019

SJR for Autonomous Agents and Multi-Agent Systems from 2016 - 2020
Year Value
2020 0.432
2019 0.6
2018 0.566
2017 0.429
2016 0.531
graph view Graph view
table view Table view

1.458

7% from 2019

SNIP for Autonomous Agents and Multi-Agent Systems from 2016 - 2020
Year Value
2020 1.458
2019 1.574
2018 1.541
2017 1.602
2016 1.65
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 28% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 7% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Autonomous Agents and Multi-Agent Systems

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Springer

Autonomous Agents and Multi-Agent Systems

The journal provides a leading forum for disseminating significant original research results in the foundations, theory, development, analysis, and applications of autonomous agents and multi-agent systems. Specific topics of interest include, but are not restricted to:... Read More

Artificial Intelligence

Computer Science

i
Last updated on
22 Jun 2020
i
ISSN
1387-2532
i
Impact Factor
High - 1.606
i
Acceptance Rate
Not provided
i
Frequency
Not provided
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
SPBASIC
i
Citation Type
Author Year
(Blonder et al, 1982)
i
Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

Journal Article DOI: 10.1023/A:1010071910869
The Gaia Methodology for Agent-Oriented Analysis and Design
Michael Wooldridge1, Nicholas R. Jennings2, David Kinny3

Abstract:

This article presents Gaia: a methodology for agent-oriented analysis and design. The Gaia methodology is both general, in that it is applicable to a wide range of multi-agent systems, and comprehensive, in that it deals with both the macro-level (societal) and the micro-level (agent) aspects of systems. Gaia is founded on th... This article presents Gaia: a methodology for agent-oriented analysis and design. The Gaia methodology is both general, in that it is applicable to a wide range of multi-agent systems, and comprehensive, in that it deals with both the macro-level (societal) and the micro-level (agent) aspects of systems. Gaia is founded on the view of a multi-agent system as a computational organisation consisting of various interacting roles. We illustrate Gaia through a case study (an agent-based business process management system). read more read less

Topics:

Business process management (52%)52% related to the paper
View PDF
2,144 Citations
open accessOpen access Journal Article DOI: 10.1023/B:AGNT.0000018806.20944.EF
Tropos: An Agent-Oriented Software Development Methodology
Paolo Bresciani, Anna Perini, Paolo Giorgini1, Fausto Giunchiglia1, John Mylopoulos2

Abstract:

Our goal in this paper is to introduce and motivate a methodology, called Tropos,1 for building agent oriented software systems. Tropos is based on two key ideas. First, the notion of agent and all related mentalistic notions (for instance goals and plans) are used in all phases of software development, from early analysis do... Our goal in this paper is to introduce and motivate a methodology, called Tropos,1 for building agent oriented software systems. Tropos is based on two key ideas. First, the notion of agent and all related mentalistic notions (for instance goals and plans) are used in all phases of software development, from early analysis down to the actual implementation. Second, Tropos covers also the very early phases of requirements analysis, thus allowing for a deeper understanding of the environment where the software must operate, and of the kind of interactions that should occur between software and human agents. The methodology is illustrated with the help of a case study. The Tropos language for conceptual modeling is formalized in a metamodel described with a set of UML class diagrams. read more read less

Topics:

Agent-oriented software engineering (60%)60% related to the paper, Software development (58%)58% related to the paper, Software construction (58%)58% related to the paper, Software development process (57%)57% related to the paper, Software design description (57%)57% related to the paper
View PDF
1,852 Citations
open accessOpen access Journal Article DOI: 10.1007/S10458-005-2631-2
Cooperative Multi-Agent Learning: The State of the Art
Liviu Panait1, Sean Luke1

Abstract:

Cooperative multi-agent systems (MAS) are ones in which several agents attempt, through their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among the agents, multi-agent problem complexity can rise rapidly with the number of agents or their behavioral sophistication. The challenge this pr... Cooperative multi-agent systems (MAS) are ones in which several agents attempt, through their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among the agents, multi-agent problem complexity can rise rapidly with the number of agents or their behavioral sophistication. The challenge this presents to the task of programming solutions to MAS problems has spawned increasing interest in machine learning techniques to automate the search and optimization process. We provide a broad survey of the cooperative multi-agent learning literature. Previous surveys of this area have largely focused on issues common to specific subareas (for example, reinforcement learning, RL or robotics). In this survey we attempt to draw from multi-agent learning work in a spectrum of areas, including RL, evolutionary computation, game theory, complex systems, agent modeling, and robotics. We find that this broad view leads to a division of the work into two categories, each with its own special issues: applying a single learner to discover joint solutions to multi-agent problems (team learning), or using multiple simultaneous learners, often one per agent (concurrent learning). Additionally, we discuss direct and indirect communication in connection with learning, plus open issues in task decomposition, scalability, and adaptive dynamics. We conclude with a presentation of multi-agent learning problem domains, and a list of multi-agent learning resources. read more read less

Topics:

Robot learning (66%)66% related to the paper, Reinforcement learning (65%)65% related to the paper, Active learning (machine learning) (63%)63% related to the paper, Multi-task learning (63%)63% related to the paper, Instance-based learning (63%)63% related to the paper
View PDF
1,283 Citations
open accessOpen access Journal Article DOI: 10.1007/S10458-005-6825-4
An integrated trust and reputation model for open multi-agent systems
Trung Dong Huynh1, Nicholas R. Jennings1, Nigel Shadbolt1

Abstract:

Trust and reputation are central to effective interactions in open multi-agent systems (MAS) in which agents, that are owned by a variety of stakeholders, continuously enter and leave the system. This openness means existing trust and reputation models cannot readily be used since their performance suffers when there are vari... Trust and reputation are central to effective interactions in open multi-agent systems (MAS) in which agents, that are owned by a variety of stakeholders, continuously enter and leave the system. This openness means existing trust and reputation models cannot readily be used since their performance suffers when there are various (unforseen) changes in the environment. To this end, this paper presents FIRE, a trust and reputation model that integrates a number of information sources to produce a comprehensive assessment of an agent's likely performance in open systems. Specifically, FIRE incorporates interaction trust, role-based trust, witness reputation, and certified reputation to provide trust metrics in most circumstances. FIRE is empirically evaluated and is shown to help agents gain better utility (by effectively selecting appropriate interaction partners) than our benchmarks in a variety of agent populations. It is also shown that FIRE is able to effectively respond to changes that occur in an agent's environment. read more read less

Topics:

Computational trust (68%)68% related to the paper, Reputation (55%)55% related to the paper, Multi-agent system (55%)55% related to the paper
View PDF
800 Citations
open accessOpen access Journal Article DOI: 10.1007/S10458-006-5952-X
TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources
W. T. L. Teacy1, Jigar Patel1, Nicholas R. Jennings1, Michael Luck1

Abstract:

In many dynamic open systems, agents have to interact with one another to achieve their goals. Here, agents may be self-interested, and when trusted to perform an action for another, may betray that trust by not performing the action as required. In addition, due to the size of such systems, agents will often interact with ot... In many dynamic open systems, agents have to interact with one another to achieve their goals. Here, agents may be self-interested, and when trusted to perform an action for another, may betray that trust by not performing the action as required. In addition, due to the size of such systems, agents will often interact with other agents with which they have little or no past experience. There is therefore a need to develop a model of trust and reputation that will ensure good interactions among software agents in large scale open systems. Against this background, we have developed TRAVOS (Trust and Reputation model for Agent-based Virtual OrganisationS) which models an agent's trust in an interaction partner. Specifically, trust is calculated using probability theory taking account of past interactions between agents, and when there is a lack of personal experience between agents, the model draws upon reputation information gathered from third parties. In this latter case, we pay particular attention to handling the possibility that reputation information may be inaccurate. read more read less

Topics:

Computational trust (67%)67% related to the paper, Reputation (55%)55% related to the paper
View PDF
575 Citations
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Frequently asked questions

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Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Autonomous Agents and Multi-Agent Systems citation style.

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5. Can I use a manuscript in Autonomous Agents and Multi-Agent Systems that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Autonomous Agents and Multi-Agent Systems that you can download at the end.

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13. What is Sherpa RoMEO Archiving Policy for Autonomous Agents and Multi-Agent Systems?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Autonomous Agents and Multi-Agent Systems. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Autonomous Agents and Multi-Agent Systems?

The 5 most common citation types in order of usage for Autonomous Agents and Multi-Agent Systems are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

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16. Can I download Autonomous Agents and Multi-Agent Systems in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Autonomous Agents and Multi-Agent Systems Endnote style according to Elsevier guidelines.

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