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Algorithms
Special Issues
Multi-Agent Systems Design, Analysis, and Applications
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Multi-Agent Systems Design, Analysis, and Applications

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

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 6502

Special Issue Editors

Dr. Angelo Fanelli

E-Mail Website
Guest Editor
CNRS (Centre National de la Recherche Scientifique), UMR-6211, 14000 Caen, France
Interests: game theory; social choice; artificial intelligence; data science
Prof. Dr. Gianpiero Monaco

E-Mail Website
Guest Editor
Department of Economic Studies, University of Chieti-Pescara, Via dei Vestini, 31, 66100 Chieti, CH, Italy
Interests: algorithms and complexity; algorithmic game theory; multi-agent systems; economic and computation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luca Moscardelli

E-Mail Website
Guest Editor
Department of Economic Studies, University of Chieti-Pescara, Viale Pindaro 42, 65127 Pescara, Italy
Interests: algorithmic game theory; combinatorial optimization; communication networks; social networks

Special Issue Information

Dear Colleagues,

Multiagent systems have received tremendous attention in different disciplines, including computer science, artificial intelligence, civil engineering, medicine, etc. These systems are composed of self-governing and intelligent parts, called agents, which are autonomous, socially intelligent, reactive, and/or pro-active. They interact with each other, situated in a common environment, eventually participating to or building an organization. Each agent decides on a proper action to solve the task using multiple inputs, e.g., history of actions, interactions with other agents, or its own goal.

This Special Issue solicits papers addressing original research on foundations, theory, development, analysis, and applications of multiagent systems composed by autonomous agents. Topics of interest include economic paradigms (cooperative and non-cooperative algorithmic game theory); social choice and voting; mechanism design; cooperation and teamwork; distributed problem solving; coalition formation; agent societies and societal issues; social networks; trust and reputation; ethical and legal issues; privacy, safety and security; and learning (evolutionary algorithms, multiagent learning, reinforcement learning, deep learning).

Dr. Angelo Fanelli
Prof. Dr. Gianpiero Monaco
Prof. Dr. Luca Moscardelli
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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

  • Economic paradigms
  • Algorithmic game theory (cooperative and non-cooperative)
  • Social choice and voting
  • Mechanism design
  • Cooperation and teamwork
  • Coalition formation
  • Social networks
  • Privacy, safety, and security
  • Trust and reputation
  • Distributed problem solving
  • Learning
  • Evolutionary algorithms
  • Multiagent learning
  • Reinforcement learning
  • Deep learning

Published Papers (2 papers)

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Research

14 pages, 343 KiB  
Article
Network Creation Games with Traceroute-Based Strategies
by Davide Bilò, Luciano Gualà, Stefano Leucci and Guido Proietti
Algorithms 2021, 14(2), 35; https://doi.org/10.3390/a14020035 - 26 Jan 2021
Cited by 2 | Viewed by 1762
Abstract
Network creation games have been extensively used as mathematical models to capture the key aspects of the decentralized process that leads to the formation of interconnected communication networks by selfish agents. In these games, each user of the network is identified by a [...] Read more.
Network creation games have been extensively used as mathematical models to capture the key aspects of the decentralized process that leads to the formation of interconnected communication networks by selfish agents. In these games, each user of the network is identified by a node and selects which link to activate by strategically balancing his/her building cost with his/her usage cost (which is a function of the distances towards the other player in the network to be built). In these games, a widespread assumption is that players have a common and complete information about the evolving network topology. This is only realistic for small-scale networks as, when the network size grows, it quickly becomes impractical for the single users to gather such a global and fine-grained knowledge of the network in which they are embedded. In this work, we weaken this assumption, by only allowing players to have a partial view of the network. To this aim, we borrow three popular traceroute-based knowledge models used in network discovery: (i) distance vector, (ii) shortest-path tree view, and (iii) layered view. We settle many of the classical game theoretic questions in all of the above models. More precisely, we introduce a suitable (and unifying) equilibrium concept which we then use to study the convergence of improving and best response dynamics, the computational complexity of computing a best response, and to provide matching upper and lower bounds to the price of anarchy. Full article
(This article belongs to the Special Issue Multi-Agent Systems Design, Analysis, and Applications)
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14 pages, 1138 KiB  
Article
Pseudo Random Number Generation through Reinforcement Learning and Recurrent Neural Networks
by Luca Pasqualini and Maurizio Parton
Algorithms 2020, 13(11), 307; https://doi.org/10.3390/a13110307 - 23 Nov 2020
Cited by 3 | Viewed by 3095
Abstract
A Pseudo-Random Number Generator (PRNG) is any algorithm generating a sequence of numbers approximating properties of random numbers. These numbers are widely employed in mid-level cryptography and in software applications. Test suites are used to evaluate the quality of PRNGs by checking statistical [...] Read more.
A Pseudo-Random Number Generator (PRNG) is any algorithm generating a sequence of numbers approximating properties of random numbers. These numbers are widely employed in mid-level cryptography and in software applications. Test suites are used to evaluate the quality of PRNGs by checking statistical properties of the generated sequences. These sequences are commonly represented bit by bit. This paper proposes a Reinforcement Learning (RL) approach to the task of generating PRNGs from scratch by learning a policy to solve a partially observable Markov Decision Process (MDP), where the full state is the period of the generated sequence, and the observation at each time-step is the last sequence of bits appended to such states. We use Long-Short Term Memory (LSTM) architecture to model the temporal relationship between observations at different time-steps by tasking the LSTM memory with the extraction of significant features of the hidden portion of the MDP’s states. We show that modeling a PRNG with a partially observable MDP and an LSTM architecture largely improves the results of the fully observable feedforward RL approach introduced in previous work. Full article
(This article belongs to the Special Issue Multi-Agent Systems Design, Analysis, and Applications)
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