scholarly journals A New Decentralized Approach of Multiagent Cooperative Pursuit Based on the Iterated Elimination of Dominated Strategies Model

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Mohammed El Habib Souidi ◽  
Songhao Piao
Keyword(s):  
Game Theory ◽  
Coalition Formation ◽  
Decision Process ◽  
Pursuit Evasion ◽  
Dominated Strategies ◽  
Markov Decision ◽  
Simulation Results ◽  
The Given ◽  
Dynamic Formation ◽  
Motion Strategy

Game Theory is a promising approach to acquire coalition formations in multiagent systems. This paper is focused on the importance of the distributed computation and the dynamic formation and reformation of pursuit groups in pursuit-evasion problems. In order to address this task, we propose a decentralized coalition formation algorithm based on the Iterated Elimination of Dominated Strategies (IEDS). This Game Theory process is common to solve problems requiring the withdrawal of dominated strategies iteratively. Furthermore, we have used the Markov Decision Process (MDP) principles to control the motion strategy of the agents in the environment. The simulation results demonstrate the feasibility and the validity of the given approach in comparison with different decentralized methods.

Hybrid Method Based on Artificial Potential Field and Differential Game Theory for the UAV Path Planing

2014 ◽  
Vol 687-691 ◽  
pp. 260-264
Author(s):  
Feng Tian ◽  
Ji Feng Zou ◽  
Tong Zhang
Keyword(s):  
Game Theory ◽  
Differential Game ◽  
Potential Field ◽  
Artificial Potential Field ◽  
Complex Environment ◽  
Pursuit Evasion ◽  
Differential Game Theory ◽  
Evasion Game ◽  
Simulation Results ◽  
Dynamic Path

In this paper, a method of the UAV path planing in the complex environment by using artificial potential field and differential game theory is introduced. This article studies the pursuit evasion game of the dynamic path planing for the UAV. The fly zone of either the pursuer or the evader is divided into two categories, namely the obstacle areas and the obstacle-free areas. So we can use the artificial potential field in the obstacle areas,and we use differential game theory in the obstacle-free areas for the UAV. This method can apply to pursuit-evasion game in the complex environment for the UAV path planing.Not only the UAV can avoid obstacles but also they can find the best capture path.Finally,the simulation results verify its correctness and validity .

scholarly journals Investment and operational decisions for start-up companies: a game theory and Markov decision process approach

2019 ◽  
Author(s):  
Thomas W. Archibald ◽  
Edgar Possani
Keyword(s):  
Game Theory ◽  
Markov Decision Process ◽  
Decision Process ◽  
Net Present Value ◽  
Process Approach ◽  
Present Value ◽  
Start Up ◽  
Markov Decision ◽  
Zero Sum ◽  
Theoretical Results

Abstract This paper analyses the contract between an entrepreneur and an investor, using a non-zero sum game in which the entrepreneur is interested in company survival and the investor in maximizing expected net present value. Theoretical results are given and the model’s usefulness is exemplified using simulations. We have observed that both the entrepreneur and the investor are better off under a contract which involves repayments and a share of the start-up company. We also have observed that the entrepreneur will choose riskier actions as the repayments become harder to meet up to a level where the company is no longer able to survive.

scholarly journals A Smart Cache Content Update Policy Based on Deep Reinforcement Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Lincan Li ◽  
Chiew Foong Kwong ◽  
Qianyu Liu ◽  
Jing Wang
Keyword(s):  
Decision Process ◽  
Training Data ◽  
Q Learning ◽  
Learning Network ◽  
The Neural Network ◽  
Markov Decision ◽  
Experience Replay ◽  
Average Latency ◽  
Simulation Results ◽  
Cache Hit Ratio

This paper proposes a DRL-based cache content update policy in the cache-enabled network to improve the cache hit ratio and reduce the average latency. In contrast to the existing policies, a more practical cache scenario is considered in this work, in which the content requests vary by both time and location. Considering the constraint of the limited cache capacity, the dynamic content update problem is modeled as a Markov decision process (MDP). Besides that, the deep Q-learning network (DQN) algorithm is utilised to solve the MDP problem. Specifically, the neural network is optimised to approximate the Q value where the training data are chosen from the experience replay memory. The DQN agent derives the optimal policy for the cache decision. Compared with the existing policies, the simulation results show that our proposed policy is 56%–64% improved in terms of the cache hit ratio and 56%–59% decreased in terms of the average latency.

“Threshold-Deciding” Policy in Distributed Multiuser Opportunistic Spectrum Access

2014 ◽  
Vol 926-930 ◽  
pp. 2867-2870
Author(s):  
Yu Meng Wang ◽  
Liang Shen ◽  
Xiang Gao ◽  
Cheng Long Xu ◽  
Xiao Ya Li ◽  
...  
Keyword(s):  
Opportunistic Spectrum Access ◽  
Spectrum Access ◽  
Secondary Users ◽  
Adaptive Policy ◽  
Markov Decision ◽  
Single User ◽  
Simulation Results ◽  
Partially Observable ◽  
Time And Energy ◽  
The Given

This paper studies the problem of distributed multiuser Opportunistic Spectrum Access based on Partially Observable Markov Decision Process (POMDP). Due to the similarity of spectrum environment, secondary users may choose the same channel adopting their own single user approach, which leads to collision. Referring to the previous works, we propose a more flexible and adaptive policy named “threshold-deciding”. Firstly, the SU gets a channel by adopting the random policy. Secondly, the SU decides whether to sense the channel by comparing the available probability with the given threshold. The policy not only decreases the collisions among SUs but also reduces the consumption of time and energy. The simulation results shows that the upgrade of performance is up to 100% compared with the existing random policy, which demonstrate the advantage of the proposed policy.

Adaptive Cross-layer Optimization Based on Markov Decision Process

1970 ◽  
Vol 108 (2) ◽  
pp. 39-42
Author(s):  
Z. Velickovic ◽  
M. Jevtovic
Keyword(s):  
Markov Decision Process ◽  
Decision Process ◽  
Cross Layer ◽  
Cross Layer Design ◽  
Protocol Stack ◽  
Wireless Multimedia ◽  
Average Value ◽  
Cross Layer Optimization ◽  
Markov Decision ◽  
Simulation Results

In order to satisfy QoS demands of wireless multimedia application it is necessary to make an optimization on several ISO-OSI layers in the protocol stack. In this paper an optimization cross-layer algorithm has been applied based on Markov decision process (MDP). The wireless communication system with one user has been optimized by the transmitting policies in order to maximize the throughput along with the optimization of the average value of the engaged power, satisfying the demanded BER and the average value of rejected packets. Simulation results show that the application of cross-layer design based on MDP is justified. Ill. 2, bibl. 9 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.108.2.141

scholarly journals A Hierarchical Mapping System for Flat Identifier to Locator Resolution Based on Active Degree

2018 ◽  
Vol 10 (8) ◽  
pp. 75
Author(s):  
Jianqiang Liu ◽  
Shuai Huo ◽  
Yi Wang
Keyword(s):  
Decision Process ◽  
Network Architecture ◽  
Local Level ◽  
Global Level ◽  
Ip Address ◽  
Mapping System ◽  
Markov Decision ◽  
Simulation Results ◽  
Logical Topology ◽  
Different Levels

Overloading of IP address semantics appeals for a new network architecture based on Identifier (ID)/Locator separation. The challenge of Identifier (ID)/Locator separation is how to solve the scalability and efficiency challenges of identity-to-location resolution. By analyzing the requirements of the Identifier (ID)/Locator separation protocol, this paper proposes a hierarchical mapping architecture on active-degree (HMAA). This HMAA was divided into three levels: active local level, neutral transfer level, and inert global level. Each mapping item is dynamically allocated to different levels to ensure minimizing delay according to its activity characteristics. The top layer CHORD is constructed by the Markov Decision Process, which can keep consistency between the physical topology and the logical topology. The simulation results on delay time show that HMAA can satisfy the scalability and efficiency requirements of an Identifier (ID)/Locator separation network.

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