| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 235 | 162 | 215 | 23 |
Autonomous Agents and Multi-Agent Systems mostly deals with topics like Artificial intelligence, Multi-agent system, Reinforcement learning, Human–computer interaction and Knowledge management. Some problems in Artificial intelligence that were presented in it overlapped with concepts under Machine learning, Task (project management), Set (psychology) and Action (philosophy). Autonomous agent and Distributed computing are some topics wherein Multi-agent system research discussed in Autonomous Agents and Multi-Agent Systems have an impact.
The published papers mostly deal with topics like Multi-agent system, Artificial intelligence, Knowledge management, Reinforcement learning and Management science. The studies on Multi-agent system discussed at the published articles can also contribute to research in the domains of Distributed computing, Agent-oriented software engineering, Metamodeling, Key (cryptography) and Normative. The works on Artificial intelligence tackled in the journal articles bring together disciplines like Machine learning, Group decision-making, Human–computer interaction and Action (philosophy).
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Autonomous Agents and Multi-Agent Systems (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Autonomous Agents and Multi-Agent Systems (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 82.16% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 27.08% were posted by at least one author from the top 10 institutions publishing in the journal. Another 14.58% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 18.75% of all publications and 39.58% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
In addition to academic research, a background in Autonomous Agents and Multi-Agent Systems can also lead to exciting career opportunities. Occupations in this field often require knowledge and understanding of numerous sub-topics, such as artificial intelligence, knowledge management, reinforcement learning, multi-agent systems, and human-computer interaction. Professionals with these skills are highly sought after in various sectors including software development, robotics, gaming & entertainment, and even education. For instance, a career as an elementary school teacher may be an unexpected but rewarding career path for individuals with this academic background. Utilizing expertise in Autonomous Agents and Multi-Agent Systems could enhance the learning experience by integrating technology and educational methods to build next-generation teaching tools and curriculums. For more detailed information on this, check out our article on how to become an elementary school teacher in Idaho, which illustrates and explains the journey and requirements to embark on this career path with a background in Autonomous Agents and Multi-Agent Systems. Overall, the extensive potential career paths demonstrate the versatility and applicability of a background in Autonomous Agents and Multi-Agent Systems, making it a valuable field of study and practice.
Haris Aziz;Ioannis Caragiannis;Ayumi Igarashi;Toby Walsh
(2022)Roxana Radulescu;Patrick Mannion;Diederik M. Roijers;Ann Nowé
(2020)Jiachen Yang;Igor Borovikov;Hongyuan Zha
(2020)Roberta Calegari;Giovanni Ciatto;Viviana Mascardi;Andrea Omicini
(2021)Michael Fisher;Viviana Mascardi;Kristin Yvonne Rozier;Bernd-Holger Schlingloff
(2021)Candice Schumann;Jeffrey S. Foster;Nicholas Mattei;John P. Dickerson
(2020)Cinjon Resnick;Abhinav Gupta;Jakob Foerster;Andrew M. Dai
(2020)Pradeep K. Murukannaiah;Nirav Ajmeri;Catholijn M. Jonker;Munindar P. Singh
(2020)For students interested in Computer Science, pursuing related online degrees can offer flexible, accelerated paths to advanced qualifications. Options like 1 year phd programs online no dissertation provide a unique opportunity to achieve a terminal degree quickly, ideal for those aiming at research or academic careers without the lengthy commitment of traditional PhDs.
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Overall, Computer Science remains one of the best college majors due to its versatility, innovation potential, and positive job outlook. Exploring these online pathways ensures that students can balance education with other commitments while preparing for rewarding careers.
