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Algorithms
Special Issues
Social Computing and Multiagent Systems
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Social Computing and Multiagent Systems

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (28 February 2019) | Viewed by 20433

Special Issue Editors

Dr. Fernando Koch

E-Mail Website
Guest Editor
The University of Melbourne, Melbourne, Australia
Interests: social computing; multi-agent systems; ubiquitous computing; artificial intelligence
Dr. Andrew Koster

E-Mail Website
Guest Editor
IIIA-CSIC and Universitat Autònoma de Barcelona, Barcelona, Spain
Interests: social computing; multi-agent systems; agent-based modelling and simulation; educational data mining; artificial intelligence
Dr. Christian Guttmann

E-Mail Website
Assistant Guest Editor
1. Nordic AI Institute & Karolinska Institute & TIETO, Sweden
2. The University of New South Wales，Sydney, Australia
Interests: research and development in Artificial Intelligence; multi agent systems and health care

Special Issue Information

Dear Colleagues,

“Social Computing and Multiagent Systems” aims to discuss computational models of innovative social computing. Social apps aim to promote social connectedness, user friendliness through natural interfaces, contextualisation, personalisation, and the ideal of invisible computing. This issue intends to address a gap in Multi-Agent Systems, Social Computing and AI landscapes around practical application of the technology to analyse and address social issues in daily life problems, such as education, health, safety, mobility, and social connectedness. These solutions exploit a combination of social applications, ambient intelligence, social networks, and collective thinking to promote the participation of citizens, social connectedness, and social intelligence.

Collaborative Services offer promising tools to understand, model, and influence complex behaviour, group behaviour, and the impact of micro-macro actions upon the system. The combination of Social Computing and Agent Models is instrumental to create models of social behaviour that are impossible to attain by either applying individual agents or high-level mathematical models. The collaboration takes place under conditions of incomplete information, uncertainty, and bounded rationality, much of which has been previously studied in economics and artificial intelligence. However, many real-world domains are characterised by even greater complexity, including the possibility of unreliable and non-complying collaborators, complex market and incentive frameworks, and complex transaction costs and organisation structures.

We seek contributions of members in the industry and applied research in academia. The contributions shall apply Social Computing, AI and agent technology, including distributed AI, situatedness, local interaction, user profiling, social simulation, and others. The application domains include (not an exhaustive list): smart education, urban intelligence, emergency scenarios, continuous healthcare, coordination of large events, intelligent transportation, and others.

Topics of interest include (non-exhaustive list):

  • How to apply agents for the next generation Social Computing, social applications and ubiquitous computing scenarios, including Social networks, Ambient Intelligence, Urban Intelligence, Regulation of Social Behaviour, Collaborative Tasks, and others?
  • How to build a model of the features of individuals and groups in Social Computing environments applying agent-based technology?
  • How to construct agent-based models equipped to better perform in Social Computing environment?
  • How to construct agent-based models of social behaviour, aiming to understand, model, and influence complex behaviour, group behaviour, and the impact of micro-macro actions upon the system?
  • How can we make team members follow agreed procedures (Incentives? Or more fundamental, by designing a new market?)
  • How to build an effective monitoring-recognition-intervention framework in Social Computing?
  • How can we support/guide collaborative teams. How can we offer flexibility in how teams execute plans? How can we make team members follow agreed procedures? Incentives? Or more fundamental, by designing a new market?
  • How to enable agents to form and follow joint agreements, guidelines and contracts in complex organisational and market driven domains (agreement adherence)?
  • How can adherence and variation be achieved under uncertain and incomplete information (comprehensive formation/maintenance framework)?
  • How to enable an effective communication infrastructure for collaborative care (possibly including humans and agents)?
  • How can transaction costs influence the social outcome of the system which is further influenced by the organisational context under which the collaboration takes place?

We seek contributions of members in the industry and applied research in academia. The contributions shall apply agent technology, including distributed AI, situatedness, local interaction, user profiling, social simulation, and others. The application domains include (not an exhaustive list): Smart education, urban intelligence, emergency scenarios, continuous healthcare, coordination of large events, intelligent transportation, and others.

Dr. Fernando Koch
Dr. Andrew Koster
Dr. Christian Guttmann
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Social Computing
  • Multi-agent systems
  • Distributed AI
  • Collaborative services
  • Social Connectedness
  • User Friendliness
  • Social Networks
  • Collective thinking
  • Collaborative teams
  • Participatory sensing
  • Social Intelligence
  • Social behaviour modelling
  • Collaborative task solving
  • Feature modelling in Social Systems

Published Papers (3 papers)

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Research

17 pages, 966 KiB  
Article
Speech Act Theory as an Evaluation Tool for Human–Agent Communication
by Nader Hanna and Deborah Richards
Algorithms 2019, 12(4), 79; https://doi.org/10.3390/a12040079 - 17 Apr 2019
Cited by 5 | Viewed by 10826
Abstract
Effective communication in task-oriented situations requires high-level interactions. For human–agent collaboration, tasks need to be coordinated in a way that ensures mutual understanding. Speech Act Theory (SAT) aims to understand how utterances can be used to achieve actions. SAT consists of three components: [...] Read more.
Effective communication in task-oriented situations requires high-level interactions. For human–agent collaboration, tasks need to be coordinated in a way that ensures mutual understanding. Speech Act Theory (SAT) aims to understand how utterances can be used to achieve actions. SAT consists of three components: locutionary act, illocutionary act, and perlocutionary act. This paper evaluates the agent’s verbal communication while collaborating with humans. SAT was used to anatomize the structure of the agent’s speech acts (locutionary acts), the agent’s intention behind the speech acts (illocutionary acts), and the effects on the human’s mental state (perlocutionary acts). Moreover, this paper studies the impact of human perceptions of the agent’s speech acts on the perception of collaborative performance with the agent. Full article
(This article belongs to the Special Issue Social Computing and Multiagent Systems)
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23 pages, 529 KiB  
Article
Programming Agents by Their Social Relationships: A Commitment-Based Approach
by Matteo Baldoni, Cristina Baroglio, Roberto Micalizio and Stefano Tedeschi
Algorithms 2019, 12(4), 76; https://doi.org/10.3390/a12040076 - 16 Apr 2019
Cited by 3 | Viewed by 4000
Abstract
Multiagent systems can be seen as an approach to software engineering for the design and development of complex, distributed software. Generally speaking, multiagent systems provide two main abstractions for modularizing the software: the agents and the environment where agents operate. In this paper, [...] Read more.
Multiagent systems can be seen as an approach to software engineering for the design and development of complex, distributed software. Generally speaking, multiagent systems provide two main abstractions for modularizing the software: the agents and the environment where agents operate. In this paper, we argue that also the social relationships among the agents should be expressed explicitly and become first-class objects both at design- and at development-time. In particular, we propose to represent social relationships as commitments that are reified as resources in the agents’ environment and can be directly manipulated by the agents via standard operations. We demonstrate that this view induces an agent programming schema that is substantially independent of the actual agent platform, provided that commitments are available as explained. The paper exemplifies the schema on two agent platforms, JADE and JaCaMo, where commitments are made available via the 2COMM library. Full article
(This article belongs to the Special Issue Social Computing and Multiagent Systems)
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13 pages, 1501 KiB  
Article
Task Assignment of the Improved Contract Net Protocol under a Multi-Agent System
by Jiarui Zhang, Gang Wang and Yafei Song
Algorithms 2019, 12(4), 70; https://doi.org/10.3390/a12040070 - 1 Apr 2019
Cited by 18 | Viewed by 5058
Abstract
Background: The existing contract net protocol has low overall efficiency during the bidding and release period, and a large amount of redundant information is generated during the negotiation process. Methods: On the basis of an ant colony algorithm, the dynamic response threshold model [...] Read more.
Background: The existing contract net protocol has low overall efficiency during the bidding and release period, and a large amount of redundant information is generated during the negotiation process. Methods: On the basis of an ant colony algorithm, the dynamic response threshold model and the flow of pheromone model were established, then the complete task allocation process was designed. Three experimental settings were simulated under different conditions. Results: When the number of agents was 20 and the maximum load value was L max = 3 , the traffic and run-time of task allocation under the improved contract net protocol decreased. When the number of tasks and L max was fixed, the improved contract net protocol had advantages over the dynamic contract net and classical contract net protocols in terms of both traffic and run-time. Setting up the number of agents, tasks and L max to improve the task allocation under the contract net not only minimizes the number of errors, but also the task completion rate reaches 100%. Conclusions: The improved contract net protocol can reduce the traffic and run-time compared with classical contract net and dynamic contract net protocols. Furthermore, the algorithm can achieve better assignment results and can re-forward all erroneous tasks. Full article
(This article belongs to the Special Issue Social Computing and Multiagent Systems)
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