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Special Issue "Algorithms for Games AI"

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A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 15 December 2023 | Viewed by 14738

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Special Issue Editors

Prof. Dr. Wenxin Li

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Guest Editor

Institute for Artificial Intelligence, Peking University, Beijing 100871, China
Interests: game AI; reinforcement applications; game design

Dr. Haifeng Zhang

E-Mail Website
Assistant Guest Editor

Institute of Automation, University of Chinese Academy of Science, Beijing 100190, China
Interests: multi-agent reinforcement learning; computational advertising; game agent; agent confrontation platform

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research in the area of gaming AI algorithms to this Special Issue, Algorithms for Game AI. We are looking for new and innovative approaches for solving game AI problems theoretically or empirically. Submissions are welcome both for traditional game AI algorithms (planning, tree search, etc.), as well as new algorithmss (deep reinforcement learning, etc.). Potential topics include, but are not limited to, the history of game AI, the develepment of Monte Carlo tree search algorithms or other tree search algorithms, the theoretical analysis of reinforcement learning algorithms or their application in specific games.

Prof. Dr. Wenxin Li
Dr. Haifeng Zhang
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.

Published Papers (6 papers)

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Open AccessArticle

Hierarchical Reinforcement Learning for Crude Oil Supply Chain Scheduling

and

Algorithms 2023, 16(7), 354; https://doi.org/10.3390/a16070354 - 24 Jul 2023

Viewed by 524

Abstract

Crude oil resource scheduling is one of the critical issues upstream in the crude oil industry chain. It aims to reduce transportation and inventory costs and avoid alerts of inventory limit violations by formulating reasonable crude oil transportation and inventory strategies. Two main difficulties coexist in this problem: the large problem scale and uncertain supply and demand. Traditional operations research (OR) methods, which rely on forecasting supply and demand, face significant challenges when applied to the complicated and uncertain short-term operational process of the crude oil supply chain. To address these challenges, this paper presents a novel hierarchical optimization framework and proposes a well-designed hierarchical reinforcement learning (HRL) algorithm. Specifically, reinforcement learning (RL), as an upper-level agent, is used to select the operational operators combined by various sub-goals and solving orders, while the lower-level agent finds a viable solution and provides penalty feedback to the upper-level agent based on the chosen operator. Additionally, we deploy a simulator based on real-world data and execute comprehensive experiments. Regarding the alert number, maximum alert penalty, and overall transportation cost, our HRL method outperforms existing OR and two RL algorithms in the majority of time steps. Full article

(This article belongs to the Special Issue Algorithms for Games AI)

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Open AccessArticle

Official International Mahjong: A New Playground for AI Research

Yunlong Lu

Wenxin Li

and

Wenlong Li

Algorithms 2023, 16(5), 235; https://doi.org/10.3390/a16050235 - 28 Apr 2023

Viewed by 935

Abstract

Games have long been benchmarks and testbeds for AI research. In recent years, with the development of new algorithms and the boost in computational power, many popular games played by humans have been solved by AI systems. Mahjong is one of the most popular games played in China and has been spread worldwide, which presents challenges for AI research due to its multi-agent nature, rich hidden information, and complex scoring rules, but it has been somehow overlooked in the community of game AI research. In 2020 and 2022, we held two AI competitions of Official International Mahjong, the standard variant of Mahjong rules, in conjunction with a top-tier AI conference called IJCAI. We are the first to adopt the duplicate format in evaluating Mahjong AI agents to mitigate the high variance in this game. By comparing the algorithms and performance of AI agents in the competitions, we conclude that supervised learning and reinforcement learning are the current state-of-the-art methods in this game and perform much better than heuristic methods based on human knowledge. We also held a human-versus-AI competition and found that the top AI agent still could not beat professional human players. We claim that this game can be a new benchmark for AI research due to its complexity and popularity among people. Full article

(This article belongs to the Special Issue Algorithms for Games AI)

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Open AccessArticle

Measuring the Non-Transitivity in Chess

Ricky Sanjaya

Jun Wang

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Yaodong Yang

Algorithms 2022, 15(5), 152; https://doi.org/10.3390/a15050152 - 28 Apr 2022

Cited by 5 | Viewed by 2148

Abstract

In this paper, we quantify the non-transitivity in chess using human game data. Specifically, we perform non-transitivity quantification in two ways—Nash clustering and counting the number of rock–paper–scissor cycles—on over one billion matches from the Lichess and FICS databases. Our findings indicate that the strategy space of real-world chess strategies has a spinning top geometry and that there exists a strong connection between the degree of non-transitivity and the progression of a chess player’s rating. Particularly, high degrees of non-transitivity tend to prevent human players from making progress in their Elo ratings. We also investigate the implications of non-transitivity for population-based training methods. By considering fixed-memory fictitious play as a proxy, we conclude that maintaining large and diverse populations of strategies is imperative to training effective AI agents for solving chess. Full article

(This article belongs to the Special Issue Algorithms for Games AI)

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Open AccessArticle

Research and Challenges of Reinforcement Learning in Cyber Defense Decision-Making for Intranet Security

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Algorithms 2022, 15(4), 134; https://doi.org/10.3390/a15040134 - 18 Apr 2022

Cited by 3 | Viewed by 3396

Abstract

In recent years, cyber attacks have shown diversified, purposeful, and organized characteristics, which pose significant challenges to cyber defense decision-making on internal networks. Due to the continuous confrontation between attackers and defenders, only using data-based statistical or supervised learning methods cannot cope with increasingly severe security threats. It is urgent to rethink network defense from the perspective of decision-making, and prepare for every possible situation. Reinforcement learning has made great breakthroughs in addressing complicated decision-making problems. We propose a framework that defines four modules based on the life cycle of threats: pentest, design, response, recovery. Our aims are to clarify the problem boundary of network defense decision-making problems, to study the problem characteristics in different contexts, to compare the strengths and weaknesses of existing research, and to identify promising challenges for future work. Our work provides a systematic view for understanding and solving decision-making problems in the application of reinforcement learning to cyber defense. Full article

(This article belongs to the Special Issue Algorithms for Games AI)

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Review

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Open AccessReview

Techniques and Paradigms in Modern Game AI Systems

Yunlong Lu

and

Wenxin Li

Algorithms 2022, 15(8), 282; https://doi.org/10.3390/a15080282 - 12 Aug 2022

Cited by 1 | Viewed by 2592

Abstract

Games have long been benchmarks and test-beds for AI algorithms. With the development of AI techniques and the boost of computational power, modern game AI systems have achieved superhuman performance in many games played by humans. These games have various features and present different challenges to AI research, so the algorithms used in each of these AI systems vary. This survey aims to give a systematic review of the techniques and paradigms used in modern game AI systems. By decomposing each of the recent milestones into basic components and comparing them based on the features of games, we summarize the common paradigms to build game AI systems and their scope and limitations. We claim that deep reinforcement learning is the most general methodology to become a mainstream method for games with higher complexity. We hope this survey can both provide a review of game AI algorithms and bring inspiration to the game AI community for future directions. Full article

(This article belongs to the Special Issue Algorithms for Games AI)

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Open AccessReview

A Review: Machine Learning for Combinatorial Optimization Problems in Energy Areas

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Algorithms 2022, 15(6), 205; https://doi.org/10.3390/a15060205 - 13 Jun 2022

Cited by 7 | Viewed by 3853

Abstract

Combinatorial optimization problems (COPs) are a class of NP-hard problems with great practical significance. Traditional approaches for COPs suffer from high computational time and reliance on expert knowledge, and machine learning (ML) methods, as powerful tools have been used to overcome these problems. In this review, the COPs in energy areas with a series of modern ML approaches, i.e., the interdisciplinary areas of COPs, ML and energy areas, are mainly investigated. Recent works on solving COPs using ML are sorted out firstly by methods which include supervised learning (SL), deep learning (DL), reinforcement learning (RL) and recently proposed game theoretic methods, and then problems where the timeline of the improvements for some fundamental COPs is the layout. Practical applications of ML methods in the energy areas, including the petroleum supply chain, steel-making, electric power system and wind power, are summarized for the first time, and challenges in this field are analyzed. Full article

(This article belongs to the Special Issue Algorithms for Games AI)

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