scholarly journals Network Security Defense Decision-Making Method Based on Stochastic Game and Deep Reinforcement Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zenan Wu ◽  
Liqin Tian ◽  
Yan Wang ◽  
Jianfei Xie ◽  
Yuquan Du ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Reinforcement Learning ◽  
Incomplete Information ◽  
Stochastic Game ◽  
Transition Probability ◽  
Equilibrium Strategy ◽  
Experimental Conditions ◽  
Network Attack ◽  
Attack And Defense

Aiming at the existing network attack and defense stochastic game models, most of them are based on the assumption of complete information, which causes the problem of poor applicability of the model. Based on the actual modeling requirements of the network attack and defense process, a network defense decision-making model combining incomplete information stochastic game and deep reinforcement learning is proposed. This model regards the incomplete information of the attacker and the defender as the defender’s uncertainty about the attacker’s type and uses the Double Deep Q-Network algorithm to solve the problem of the difficulty of determining the network state transition probability, so that the network system can dynamically adjust the defense strategy. Finally, a simulation experiment was performed on the proposed model. The results show that, under the same experimental conditions, the proposed method in this paper has a better convergence speed than other methods in solving the defense equilibrium strategy. This model is a fusion of traditional methods and artificial intelligence technology and provides new research ideas for the application of artificial intelligence in the field of cyberspace security.

Actor–critic-based decision-making method for the artificial intelligence commander in tactical wargames

2020 ◽  
pp. 154851292095454
Author(s):  
Junfeng Zhang ◽  
Qing Xue
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Making ◽  
Reinforcement Learning ◽  
Convolutional Neural Network ◽  
Difficult Problem ◽  
Learning Method ◽  
Rule Based ◽  
Autonomous Decision ◽  
Decision Making Problem

In a tactical wargame, the decisions of the artificial intelligence (AI) commander are critical to the final combat result. Due to the existence of fog-of-war, AI commanders are faced with unknown and invisible information on the battlefield and lack of understanding of the situation, and it is difficult to make appropriate tactical strategies. The traditional knowledge rule-based decision-making method lacks flexibility and autonomy. How to make flexible and autonomous decision-making when facing complex battlefield situations is a difficult problem. This paper aims to solve the decision-making problem of the AI commander by using the deep reinforcement learning (DRL) method. We develop a tactical wargame as the research environment, which contains built-in script AI and supports the machine–machine combat mode. On this basis, an end-to-end actor–critic framework for commander decision making based on the convolutional neural network is designed to represent the battlefield situation and the reinforcement learning method is used to try different tactical strategies. Finally, we carry out a combat experiment between a DRL-based agent and a rule-based agent in a jungle terrain scenario. The result shows that the AI commander who adopts the actor–critic method successfully learns how to get a higher score in the tactical wargame, and the DRL-based agent has a higher winning ratio than the rule-based agent.

scholarly journals Study on the Strategy of Playing Doudizhu Game Based on Multirole Modeling

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Shuqin Li ◽  
Saisai Li ◽  
Hengyang Cao ◽  
Kun Meng ◽  
Meng Ding
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Making ◽  
Incomplete Information ◽  
Computer Game ◽  
Game Problem ◽  
Role Modeling ◽  
Human Beings ◽  
National Competition ◽  
Evaluation Algorithm

Doudizhu poker is a very popular and interesting national poker game in China, and now it has become a national competition in China. As this game is a typical example of incomplete information game problem, it has received more and more attention from artificial intelligence experts. This paper proposes a multirole modeling-based card-playing framework. This framework includes three parts: role modeling, cards carrying, and decision-making strategies. Role modeling learns different roles and behaviors by using a convolutional neural network. Cards carrying can calculate reasonable rules especially for “triplet” by using an evaluation algorithm. Decision making is for implementing different card strategies for different player roles. Experimental results showed that this card-playing framework makes playing decisions like human beings, and it can to some extent learn, collaborate, and reason when facing an incomplete information game problem. This framework won the runner-up in the 2018 China Computer Game Competition.

The Machine-Learning Approach of Reinforcement Learning

2020 ◽  
pp. 105-109
Author(s):  
Thomas Boraud
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Algorithms ◽  
Model Free ◽  
Machine Learning Approach ◽  
Markov Decision ◽  
Decision Making Processes ◽  
Alternative Approaches

This chapter assesses alternative approaches of reinforcement learning that are developed by machine learning. The initial goal of this branch of artificial intelligence, which appeared in the middle of the twentieth century, was to develop and implement algorithms that allow a machine to learn. Originally, they were computers or more or less autonomous robotic automata. As artificial intelligence has developed and cross-fertilized with neuroscience, it has begun to be used to model the learning and decision-making processes for biological agents, broadening the meaning of the word ‘machine’. Theoreticians of this discipline define several categories of learning, but this chapter only deals with those which are related to reinforcement learning. To understand how these algorithms work, it is necessary first of all to explain the Markov chain and the Markov decision-making process. The chapter then goes on to examine model-free reinforcement learning algorithms, the actor-critic model, and finally model-based reinforcement learning algorithms.

scholarly journals Optimal Decision-Making Approach for Cyber Security Defense Using Game Theory and Intelligent Learning

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Yuchen Zhang ◽  
Jing Liu
Keyword(s):  
Decision Making ◽  
Cyber Security ◽  
Learning Algorithm ◽  
Stochastic Game ◽  
Learning Ability ◽  
Optimal Decision ◽  
Equilibrium Strategy ◽  
Cyber Attack ◽  
Defense Strategies ◽  
Network States

Existing approaches of cyber attack-defense analysis based on stochastic game adopts the assumption of complete rationality, but in the actual cyber attack-defense, it is difficult for both sides of attacker and defender to meet the high requirement of complete rationality. For this aim, the influence of bounded rationality on attack-defense stochastic game is analyzed. We construct a stochastic game model. Aiming at the problem of state explosion when the number of network nodes increases, we design the attack-defense graph to compress the state space and extract network states and defense strategies. On this basis, the intelligent learning algorithm WoLF-PHC is introduced to carry out strategy learning and improvement. Then, the defense decision-making algorithm with online learning ability is designed, which helps to select the optimal defense strategy with the maximum payoff from the candidate strategy set. The obtained strategy is superior to previous evolutionary equilibrium strategy because it does not rely on prior data. By introducing eligibility trace to improve WoLF-PHC, the learning speed is further improved and the defense timeliness is significantly promoted.

Perceived Effectiveness of Artificial Intelligence based Decision Support System in a Clinical Setting: A Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Avishek Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Support System ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Theoretical Perspective ◽  
Care Providers

UNSTRUCTURED Objective: The potential benefits of artificial intelligence based decision support system (AI-DSS) from a theoretical perspective are well documented and perceived by researchers but there is a lack of evidence showing its influence on routine clinical practice and how its perceived by care providers. Since the effectiveness of AI systems depends on data quality, implementation, and interpretation. The purpose of this literature review is to analyze the effectiveness of AI-DSS in clinical setting and understand its influence on clinician’s decision making outcome. Materials and Methods: This review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta- Analyses reporting guidelines. Literature will be identified using a multi-database search strategy developed in consultation with a librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining application of AI based decision support system in a clinical setting and its impact on clinician’s decision making, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes. Studies solely reporting AI accuracy an but not implemented in a clinical setting to measure its influence on clinical decision making were excluded from further review. Results: We identified 8 eligible studies that implemented AI-DSS in a clinical setting to facilitate decisions concerning prostate cancer, post traumatic stress disorder, cardiac ailment, back pain, and others. Five (62.50%) out of 8 studies reported positive outcome of AI-DSS. Conclusion: The systematic review indicated that AI-enabled decision support systems, when implemented in a clinical setting and used by clinicians might not ensure enhanced decision making. However, there are very limited studies to confirm the claim that AI based decision support system can uplift clinicians decision making abilities.

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