Game Theory and Artificial Intelligence

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 10918

Special Issue Editor


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Guest Editor
Ganzfried Research, Miami Beach, FL 33139, USA
Interests: artificial intelligence; game theory

Special Issue Information

Dear Colleagues,

Artificial intelligence concerns creating agents for solving large-scale real-world problems. Typically, these problems are very challenging from a theoretical complexity perspective (e.g., NP-complete). The best approaches often involve a combination of theoretically justified algorithms and heuristics, as well as general domain-independent approaches and approaches that utilize domain-specific expertise. Game theory is the study of strategic interactions. In order to create strong agents for complex multiagent environments, new AI approaches are needed.

This Special Issue welcomes submissions that fall at the intersection of game theory and artificial intelligence, broadly construed (note that submissions can focus more on either GT or AI; they do not need to focus equally on both). Submissions can be primarily theoretical, applied, or a combination of both. We welcome original research articles as well as comprehensive review articles on a relevant topic. See the keywords for a list of specific topics of interest.

Dr. Sam Ganzfried
Guest Editor

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • Nash equilibrium computation and approximation
  • computation of equilibrium refinements
  • alternative solution concepts and their computation
  • game abstraction
  • learning in games
  • bounded rationality
  • opponent modeling
  • stochastic games
  • imperfect information
  • continuous games
  • Bayesian games
  • applications including security, medicine, education, law, political science

Published Papers (5 papers)

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Research

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25 pages, 3685 KiB  
Article
Optimum Route and Transport Mode Selection of Multimodal Transport with Time Window under Uncertain Conditions
by Lin Li, Qiangwei Zhang, Tie Zhang, Yanbiao Zou and Xing Zhao
Mathematics 2023, 11(14), 3244; https://doi.org/10.3390/math11143244 - 24 Jul 2023
Cited by 1 | Viewed by 1676
Abstract
Aiming at the problem of multimodal transport path planning under uncertain environments, this paper establishes a multi-objective fuzzy nonlinear programming model considering mixed-time window constraints by taking cost, time, and carbon emission as optimization objectives. To solve the model, the model is de-fuzzified [...] Read more.
Aiming at the problem of multimodal transport path planning under uncertain environments, this paper establishes a multi-objective fuzzy nonlinear programming model considering mixed-time window constraints by taking cost, time, and carbon emission as optimization objectives. To solve the model, the model is de-fuzzified by the fuzzy expectation value method and fuzzy chance-constrained planning method. Combining the game theory method with the weighted sum method, a cooperative game theory-based multi-objective optimization method is proposed. Finally, the effectiveness of the algorithm is verified in a real intermodal network. The experimental results show that the proposed method can effectively improve the performance of the weighted sum method and obtain the optimal multimodal transport path that satisfies the time window requirement, and the path optimization results are better than MOPSO and NSGA-II, effectively reducing transportation costs and carbon emissions. Meanwhile, the influence of uncertainty factors on the multimodal transport route planning results is analyzed. The results show that the uncertain factors will significantly increase the transportation cost and carbon emissions and affect the choice of route and transportation mode. Considering uncertainty factors can increase the reliability of route planning results and provide a more robust and effective solution for multimodal transportation. Full article
(This article belongs to the Special Issue Game Theory and Artificial Intelligence)
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35 pages, 9607 KiB  
Article
Action-Based Digital Characterization of a Game Player
by Damijan Novak, Domen Verber, Jani Dugonik and Iztok Fister, Jr.
Mathematics 2023, 11(5), 1243; https://doi.org/10.3390/math11051243 - 04 Mar 2023
Viewed by 944
Abstract
Games can be more than just a form of entertainment. Game spaces can be used to test different research ideas quickly, simulate real-life environments, develop non-playable characters (game agents) that interact alongside human players and much more. Game agents are becoming increasingly sophisticated [...] Read more.
Games can be more than just a form of entertainment. Game spaces can be used to test different research ideas quickly, simulate real-life environments, develop non-playable characters (game agents) that interact alongside human players and much more. Game agents are becoming increasingly sophisticated as the collaboration between game agents and humans only continues to grow, and there is an increasing need to better understand game players’ workings. Therefore, this work addresses the digital characterization (DC) of various game players based on the game feature values found in a game space, and based on the actions gathered from player interactions with the game space. High-confidence actions are extracted from rules created with association rule mining, utilizing advanced evolutionary algorithms (e.g., differential evolution) on the dataset of feature values. These high-confidence actions are used in the characterization process, resulting in the DC description of each player. The main research agenda of this study is to determine whether DCs manage to capture the essence of players’ action style behavior. Experiments reveal that characterizations do indeed capture behavior nuances, and consequently open up many research possibilities in the domains of player modeling, analyzing the behavior of different players and automatic policy creation, which can possibly be used for utilization in future simulations. Full article
(This article belongs to the Special Issue Game Theory and Artificial Intelligence)
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14 pages, 3263 KiB  
Article
Approaches That Use Domain-Specific Expertise: Behavioral-Cloning-Based Advantage Actor-Critic in Basketball Games
by Taehyeok Choi, Kyungeun Cho and Yunsick Sung
Mathematics 2023, 11(5), 1110; https://doi.org/10.3390/math11051110 - 22 Feb 2023
Cited by 3 | Viewed by 1616
Abstract
Research on the application of artificial intelligence (AI) in games has recently gained momentum. Most commercial games still use AI based on a finite state machine (FSM) due to complexity and cost considerations. However, FSM-based AI decreases user satisfaction given that it performs [...] Read more.
Research on the application of artificial intelligence (AI) in games has recently gained momentum. Most commercial games still use AI based on a finite state machine (FSM) due to complexity and cost considerations. However, FSM-based AI decreases user satisfaction given that it performs the same patterns of consecutive actions in the same situations. This necessitates a new AI approach that applies domain-specific expertise to existing reinforcement learning algorithms. We propose a behavioral-cloning-based advantage actor-critic (A2C) that improves learning performance by applying a behavioral cloning algorithm to an A2C algorithm in basketball games. The state normalization, reward function, and episode classification approaches are used with the behavioral-cloning-based A2C. The results of the comparative experiments with the traditional A2C algorithms validated the proposed method. Our proposed method using existing approaches solved the difficulty of learning in basketball games. Full article
(This article belongs to the Special Issue Game Theory and Artificial Intelligence)
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23 pages, 370 KiB  
Article
Human Strategic Decision Making in Parametrized Games
by Sam Ganzfried
Mathematics 2022, 10(7), 1147; https://doi.org/10.3390/math10071147 - 02 Apr 2022
Viewed by 1314
Abstract
Many real-world games contain parameters which can affect payoffs, action spaces, and information states. For fixed values of the parameters, the game can be solved using standard algorithms. However, in many settings agents must act without knowing the values of the parameters that [...] Read more.
Many real-world games contain parameters which can affect payoffs, action spaces, and information states. For fixed values of the parameters, the game can be solved using standard algorithms. However, in many settings agents must act without knowing the values of the parameters that will be encountered in advance. Often the decisions must be made by a human under time and resource constraints, and it is unrealistic to assume that a human can solve the game in real time. We present a new framework that enables human decision makers to make fast decisions without the aid of real-time solvers. We demonstrate applicability to a variety of situations including settings with multiple players and imperfect information. Full article
(This article belongs to the Special Issue Game Theory and Artificial Intelligence)
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Review

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34 pages, 735 KiB  
Review
Recent Developments in Game-Theory Approaches for the Detection and Defense against Advanced Persistent Threats (APTs): A Systematic Review
by Mohd Nor Akmal Khalid, Amjed Ahmed Al-Kadhimi and Manmeet Mahinderjit Singh
Mathematics 2023, 11(6), 1353; https://doi.org/10.3390/math11061353 - 10 Mar 2023
Cited by 3 | Viewed by 3603
Abstract
Cybersecurity has become a prominent issue in regard to ensuring information privacy and integrity in the internet age particularly with the rise of interconnected devices. However, advanced persistent threats (APTs) pose a significant danger to the current contemporary way of life, and effective [...] Read more.
Cybersecurity has become a prominent issue in regard to ensuring information privacy and integrity in the internet age particularly with the rise of interconnected devices. However, advanced persistent threats (APTs) pose a significant danger to the current contemporary way of life, and effective APT detection and defense are vital. Game theory is one of the most sought-after approaches adopted against APTs, providing a framework for understanding and analyzing the strategic interactions between attackers and defenders. However, what are the most recent developments in game theory frameworks against APTs, and what approaches and contexts are applied in game theory frameworks to address APTs? In this systematic literature review, 48 articles published between 2017 and 2022 in various journals were extracted and analyzed according to PRISMA procedures and our formulated research questions. This review found that game-theory approaches have been optimized for the defensive performance of security measures and implemented to anticipate and prepare for countermeasures. Many have been designed as part of incentive-compatible and welfare-maximizing contracts and then applied to cyber–physical systems, social networks, and transportation systems, among others. The trends indicate that game theory provides the means to analyze and understand complex security scenarios based on technological advances, changes in the threat landscape, and the emergence of new trends in cyber-crime. In this study, new opportunities and challenges against APTs are outlined, such as the ways in which tactics and techniques to bypass defenses are likely to evolve in order to evade detection, and we focused on specific industries and sectors of high interest or value (e.g., healthcare, finance, critical infrastructure, and the government). Full article
(This article belongs to the Special Issue Game Theory and Artificial Intelligence)
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