A review of attacker-defender games: Current state and paths forward

计算机科学 启发式 对抗制 领域(数学) 博弈论 国家(计算机科学) 光学(聚焦) 管理科学 运筹学 数理经济学 人工智能 算法 经济 工程类 物理 数学 光学 操作系统 纯数学
作者
Kyle Hunt,Jun Zhuang
出处
期刊:European Journal of Operational Research [Elsevier]
卷期号:313 (2): 401-417 被引量:10
标识
DOI:10.1016/j.ejor.2023.04.009
摘要

In this article, we review the literature which proposes attacker-defender games to protect against strategic adversarial threats. More specifically, we follow the systematic literature review methodology to collect and review 127 journal articles that have been published over the past 15 years. We start by briefly discussing the common application areas that are addressed in the literature, although our focus in this review lies heavily in the approaches that have been adopted to model and solve attacker-defender games. In studying these approaches, we begin by analyzing the following features of the proposed game formulations: the sequence of moves, number of players, nature of decision variables and objective functions, and time horizons. We then analyze the common assumptions of perfect rationality, risk neutrality, and complete information that are enforced within the majority of the articles, and report on state-of-the-art research which has begun relaxing these assumptions. We find that relaxing these assumptions presents further challenges, such as enforcing new assumptions regarding how uncertainties are modeled, and issues with intractability when models are reformulated to account for considerations such as risk preferences. Finally, we examine the methods that have been adopted to solve attacker-defender games. We find that the majority of the articles obtain closed-form solutions to their models, while there are also many articles that developed novel solution algorithms and heuristics. Upon synthesizing and analyzing the literature, we expose open questions in the field, and present promising future research directions that can advance current knowledge.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Ava应助无限的绿真采纳,获得10
1秒前
小马甲应助xiongdi521采纳,获得10
1秒前
科研通AI5应助陶醉觅夏采纳,获得200
4秒前
憨鬼憨切发布了新的文献求助10
4秒前
4秒前
宇宙暴龙战士暴打魔法少女完成签到,获得积分10
6秒前
7秒前
8秒前
hh应助科研通管家采纳,获得10
8秒前
科研通AI5应助科研通管家采纳,获得10
8秒前
Ava应助科研通管家采纳,获得10
8秒前
Eva完成签到,获得积分10
8秒前
传奇3应助科研通管家采纳,获得10
8秒前
斯文败类应助科研通管家采纳,获得10
8秒前
爆米花应助科研通管家采纳,获得10
9秒前
科研通AI5应助科研通管家采纳,获得10
9秒前
搜集达人应助科研通管家采纳,获得10
9秒前
思源应助科研通管家采纳,获得10
9秒前
汉堡包应助科研通管家采纳,获得10
9秒前
清爽老九应助科研通管家采纳,获得20
9秒前
传奇3应助科研通管家采纳,获得10
9秒前
greenPASS666发布了新的文献求助10
9秒前
涂欣桐应助科研通管家采纳,获得10
9秒前
英俊的铭应助科研通管家采纳,获得10
9秒前
secbox完成签到,获得积分10
10秒前
刘哈哈发布了新的文献求助30
10秒前
xyzdmmm完成签到,获得积分10
11秒前
11秒前
欢呼冰岚发布了新的文献求助30
12秒前
xiongdi521发布了新的文献求助10
12秒前
honeybee完成签到,获得积分10
14秒前
兔子完成签到,获得积分10
15秒前
汉关发布了新的文献求助10
15秒前
NexusExplorer应助WZ0904采纳,获得10
16秒前
xiongdi521完成签到,获得积分10
17秒前
17秒前
ding应助奋斗的小林采纳,获得10
17秒前
超帅曼柔完成签到,获得积分10
17秒前
酷波er应助xg采纳,获得10
18秒前
听话的亦瑶完成签到,获得积分10
19秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
Luis Lacasa - Sobre esto y aquello 700
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527998
求助须知:如何正确求助?哪些是违规求助? 3108225
关于积分的说明 9288086
捐赠科研通 2805889
什么是DOI,文献DOI怎么找? 1540195
邀请新用户注册赠送积分活动 716950
科研通“疑难数据库(出版商)”最低求助积分说明 709849