Non-cooperative Game Strategy Research Articles

Robust adaptive fault-tolerant control for path maneuvering of autonomous surface vehicles with actuator faults based on the noncooperative game strategy

This paper investigates a parameterized path-guided following/maneuvering control problem of an autonomous surface vehicle (ASV) subject to actuator faults via a noncooperative game strategy. Unlike the existing path maneuvering control methods, the noncooperative game of this paper includes two sub-games. On one hand, the path update is considered to be against the effort of kinematic control. On the other hand, the kinetic control is considered to be against the total disturbances consisting of actuator faults and external disturbances. A robust adaptive fault-tolerant path maneuvering controller is designed based on a noncooperative game approach. Specifically, we design a kinematic control law using an improved dynamic surface control approach to achieve the geometric objective. An improved neural predictor is constructed, in which a concurrent learning-based adaptation is designed to identify unknown fault coefficients. A path update law and a kinetic control law are calculated to cover noncooperative games by using an adaptive dynamic programming approach. Theoretical analysis shows that the closed-loop system is input-to-state stable, and the proposed method can meet the geometric objective, the dynamic objective, and the fault-tolerant objective. Finally, simulation results demonstrate the efficacy of the proposed robust adaptive fault-tolerant control method for path maneuvering.

Multi-Player Non-Cooperative Game Strategy of a Nonlinear Stochastic System with Time-Varying Parameters

This paper discusses the multi-player non-cooperative game of nonlinear stochastic time-varying systems described by Itô-type differential equations in a finite time interval. Multi-player non-cooperative game problems are represented by multi-objective Pareto (MOP) control problems to describe the fact that each player has their own goals. By applying Hamilton–Jacobi inequalities (HJIs), the criterion of upper bounds of the MOP boundary is obtained for nonlinear stochastic systems, and the corresponding strategies are designed for such games, so the MOP problem is transformed into a HJI-constrained MOP problem. In order to overcome the difficulty of solving HJIs, a global linearization method is proposed to approximate the nonlinear systems. By the proposed global linearization method, multi-player non-cooperative game problems are transformed into Riccati equation-constrained MOP problems, and the approximate solutions of HJI-constrained MOP problems are obtained. Finally, a practical example is given to illustrate the effectiveness of the proposed method.

Blockchain Framework for Cognitive Sensor Network Using Non-Cooperative Game Theory

The incorporation of Technology in healthcare and hospital management has given a new perception to the medical procedures, and drug management for patient-centric care. Emerging technologies like blockchain, Internet of Things, and cognitive computing are most adaptable for designing smart healthcare systems. However, due to the diversified tasks involved, a design approach with broader understanding involving multiple factors that represent each area of the healthcare is the need of the hour. Game theory has gained prominence in modeling multi-player problems designated by strategic interdependency. This paper proposes a non-cooperative game strategy between players (stakeholders) to inspect how rationality is exhibited among the players who seamlessly try to get benefitted from the actions of other players. To evaluate the feasibility of the proposed model, a prototype was designed based on Ethereum permissioned blockchain employing Raspberry pi IoT devices and sensor-based cognition. Use of blockchain brings in trust, security and transparency to the system. The simulation results show that the proposed model provides better outcomes in terms of latency (≈ 15 min), throughput and scalability with an increased number of transactions. The comparative analysis elucidates the fact that the proposed method outperforms the existing healthcare systems with a significant improvement of 10-15% in terms of resource utilization and provides faster and accurate patient-centric services.

Noncooperative and Cooperative Strategy Designs for Nonlinear Stochastic Jump Diffusion Systems With External Disturbance: T–S Fuzzy Approach

In this article, we consider the multiplayer $H_{\infty }$ noncooperative and cooperative game strategy designs for a class of stochastic jump diffusion systems with external disturbance. To attenuate the effect from the competitive strategies of other players and unpredictable external disturbance on the desired target tracking performance of each player, a multiplayer $H_{\infty }$ noncooperative game strategy design problem is proposed and formulated as an equivalent multiobjective optimization problem (MOP) with a Nash equilibrium solution. Also, the multiplayer $H_{\infty }$ cooperative game strategy design problem is discussed and formulated as an equivalent single-objective optimization problem. To overcome the difficulties in solving multiple Hamilton–Jacobi–Issacs inequalities (HJIIs) for noncooperative and cooperative game strategy designs, the Takagi–Sugeno fuzzy model is introduced to approximate the nonlinear stochastic system and HJII could be transformed into a set of linear matrix inequalities (LMIs). Besides, an LMI-constrained multiobjective evolution algorithm is developed to efficiently solve the MOP of a noncooperative multiplayer $H_{\infty }$ stochastic game strategy design problem. A financial market with multiple investors is provided as a simulation example to demonstrate the effectiveness of the proposed noncooperative and cooperative game investment strategies.

Noncooperative Game Strategy in Cyber-Financial Systems With Wiener and Poisson Random Fluctuations: LMIs-Constrained MOEA Approach.

The financial market is a nonlinear stochastic system with continuous Wiener and discontinuous Poisson random fluctuations. Most managers or investors hope their investment policies to be with the not only high profit but also low risk. Managers and investors involved pursue their own interests which are partly conflicting with others. Stochastic game theory has been widely applied to multiperson noncooperative decision making problem of financial market. However, for the nonlinear stochastic financial system with random fluctuations, it still lacks an analytical or computational scheme to effectively solve the complex noncooperative game strategy design problem. In this paper, the stochastic multiperson noncooperative game strategy in cyber-financial systems is transformed to a multituple Hamilton-Jacobi-Isacc inequalities (HJIIs)-constrained multiobjective optimization problem (MOP). This HJIIs-constrained MOP solution is also found to be the Nash equilibrium solution of multiperson noncooperative game strategy in nonlinear stochastic financial systems. In order to simplify design procedure by the global linearization theory, a set of local linear systems are interpolated to approximate the nonlinear stochastic financial system so that the m-tuple HJIIs-constrained MOP for noncooperative game strategy of cyber-financial system could be converted to a linear matrix inequalities (LMIs)-constrained MOP. Finally, an LMIs-constrained multiobjective evolution algorithm is explored for effectively solving the multiperson noncooperative game strategy in cyber-financial systems. Two design examples are also given for the illustration of the design procedure and the performance validation of the proposed stochastic noncooperative investment strategy in the nonlinear stochastic financial systems.

Association of Pulmonary and Cardiovascular Health Metrics with Carbon Nanotube and Nanofiber Exposure Among U.S. workers: A Cross-sectional Study

In this chapter, noncooperative and cooperative evolutionary game strategies are discussed from the stochastic Nash game perspective to investigate and compare their phenotypic robustness and network evolvability to get an insight into the evolutionary game theory of stochastic biological networks under natural selection. The noncooperative evolutionary game strategy can be transformed to an equivalent multiobjective optimization problem and significantly improve network robustness to tolerate more genetic variations and buffer more environmental disturbances to maintain the phenotypic trait longer in the evolutionary process than the cooperative evolutionary game strategy. However, the noncooperative game strategy needs to be solved with more efforts and compromises with these conflicting players than the cooperative evolutionary game strategy. In order to overcome the difficulty of solving two nonlinear stochastic evolutionary game strategies, the global linearization technique is employed to simplify the solution procedure of two evolutionary strategies. Finally, a simple example of a stochastic evolutionary metabolic pathway is given with a simulation to illustrate the solution procedure of two evolutionary game strategies and to confirm and compare their network evolutionary results in the evolutionary process.

Game theoretic approach of a novel decision policy for customers based on big data

In recent days, big data based analysis in hotel industry become popular. Merchants are attracting clients using the accurate analysis of historic data and predicting the behavior of possible clients to perform proper marketing strategy. To study the principle of the game between clients and merchants, in this work, we propose a novel two-stage game theoretic approach of decision policy for clients when choosing the suitable hotel to stay among many candidates, the merchants will provide a non-cooperative game strategy to attract the attention of potential clients. Analysis of the non-cooperative game method based on big data has been given. Simulation results indicate that, by using our proposed novel method, the average price for clients to choose a satisfied hotel is reduced and the successful rate of stay is increased for merchants, which will bring the expected income to a higher level because of the sticky phenomena of users.