A Data Intrusion Tolerance Model Based on an Improved Evolutionary Game Theory for the Energy Internet

Malicious attacks against data are unavoidable in the interconnected,open and shared Energy Internet(EI),Intrusion tolerant techniques are critical to the data security of EI.Existing intrusion tolerant techniques suffered from problems such as low adaptability,policy lag,and difficulty in determining the degree of tolerance.To address these issues,we propose a novel adaptive intrusion tolerance model based on game theory that enjoys two-fold ideas:(1)it constructs an improved replica of the intrusion tolerance model of the dynamic equation evolution game to induce incentive weights;and (2)it combines a tournament competition model with incentive weights to obtain optimal strategies for each stage of the game process.Extensive experiments are conducted in the IEEE 39-bus system,whose results demonstrate the feasibility of the incentive weights,confirm the proposed strategy strengthens the system’s ability to tolerate aggression,and improves the dynamic adaptability and response efficiency of the aggression-tolerant system in the case of limited resources.

Risk assessment of high-speed railway CTC system based on improved game theory and cloud model

Purpose-In order to solve the problem of inaccurate calculation of index weights,subjectivity and uncertainty of index assessment in the risk assessment process,this study aims to propose a scientific and reasonable centralized traffic control(CTC)system risk assessment method.Design/methodologylapproach-First,system-theoretic process analysis(STPA)is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis.Then,to enhance the accuracy of weight calculation,the fuzzy analytical hierarchy process(FAHP),fuzzy decision-making trial and evaluation laboratory(FDEMATEL)and entropy weight method are employed to calculate the subjective weight,relative weight and objective weight of each index.These three types of weights are combined using game theory to obtain the combined weight for each index.To reduce subjectivity and uncertainty in the assessment process,the backward cloud generator method is utilized to obtain the numerical character(NC)of the cloud model for each index.The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system.This cloud model is used to obtain the CTC system's comprehensive risk assessment.The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud.Finally,this process yields the risk assessment results for the CTC system.Findings-The cloud model can handle the subjectivity and fuzziness in the risk assessment process well.The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/value-This study provides a cloud model-based method for risk assessment of CTC systems,which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment,achieving effective risk assessment of CTC systems.It can provide a reference and theoretical basis for risk management of the CTC system.

Game Theory Based Model for Predictive Analytics Using Distributed Position Function

This paper presents a game theory-based method for predicting the outcomes of negotiation and group decision-making problems. We propose an extension to the BDM model to address problems where actors’ positions are distributed over a position spectrum. We generalize the concept of position in the model to incorporate continuous positions for the actors, enabling them to have more flexibility in defining their targets. We explore different possible functions to study the role of the position function and discuss appropriate distance measures for computing the distance between the positions of actors. To validate the proposed extension, we demonstrate the trustworthiness of our model’s performance and interpretation by replicating the results based on data used in earlier studies.

Modelling of a Human Driver’s Interaction with Vehicle Automated Steering Using Cooperative Game Theory

The introduction of automated driving systems raised questions about how the human driver interacts with the automated system. Non-cooperative game theory is increasingly used for modelling and understanding such interaction, while its counterpart, cooperative game theory is rarely discussed for similar applications despite it may be potentially more suitable. This paper describes the modelling of a human driver’s steering interaction with an automated steering system using cooperative game theory. The distributed Model Predictive Control approach is adopted to derive the driver’s and the automated steering system’s strategies in a Pareto equilibrium sense, namely their cooperative Pareto steering strategies. Two separate numerical studies are carried out to study the influence of strategy parameters, and the influence of strategy types on the driver’s and the automated system’s steering performance. It is found that when a driver interacts with an automated steering system using a cooperative Pareto steering strategy, the driver can improve his/her performance in following a target path through increasing his/her effort in pursuing his/her own interest under the driver-automation cooperative control goal. It is also found that a driver’s adoption of cooperative Pareto steering strategy leads to a reinforcement in the driver’s steering angle control, compared to the driver’s adoption of non-cooperative Nash strategy. This in turn enables the vehicle to return from a lane-change maneuver to straight-line driving swifter.

Multi-agent system application in accordance with game theory in bi-directional coordination network model

The multi-agent system is the optimal solution to complex intelligent problems. In accordance with the game theory, the concept of loyalty is introduced to analyze the relationship between agents' individual income and global benefits and build the logical architecture of the multi-agent system. Besides, to verify the feasibility of the method, the cyclic neural network is optimized, the bi-directional coordination network is built as the training network for deep learning, and specific training scenes are simulated as the training background. After a certain number of training iterations, the model can learn simple strategies autonomously. Also,as the training time increases, the complexity of learning strategies rises gradually. Strategies such as obstacle avoidance, firepower distribution and collaborative cover are adopted to demonstrate the achievability of the model. The model is verified to be realizable by the examples of obstacle avoidance, fire distribution and cooperative cover. Under the same resource background, the model exhibits better convergence than other deep learning training networks, and it is not easy to fall into the local endless loop.Furthermore, the ability of the learning strategy is stronger than that of the training model based on rules, which is of great practical values.

Anti-rollover Artificial Potential Field Motion Planning Method of an Autonomous Heavy Truck Optimised by Game Theory

Anti-rollover is a critical factor to consider when planning the motion of autonomous heavy trucks.This paper proposed a method for autonomous heavy trucks to generate a path that avoids collisions and minimizes rollover risk.The corresponding rollover index is deduced from a 5-DOF heavy truck dynamic model that includes longitudinal motion,lateral motion,yaw motion,sprung mass roll motion,unsprung mass roll motion,and an anti-rollover artificial potential field(APF)is proposed based on this.The motion planning method,which is based on model predictive control(MPC),combines trajectory tracking,anti-rollover APF,and the improved obstacle avoidance APF and considers the truck dynamics constraints,obstacle avoidance,and anti-rollover.Furthermore,by using game theory,the coefficients of the two APF functions are optimised,and an optimal path is planned.The effectiveness of the optimised motion planning method is demonstrated in a variety of scenarios.The results demonstrate that the optimised motion planning method can effectively and efficiently avoid collisions and prevent rollover.

Game Theory Model and Equilibrium Analysis of Peasant's Production Decision

Unbalanced agricultural production decision becomes the great block that influences the effective distribution of social resources, national grain security, social stability and economic development. This paper took the game theory as an analyzed tool to describe the interactional processes among the peasants, and set up the game theory model of independent decision and joint decision by peasants. It was shown that the government＇s positive guide and the market environment macroscopically controlled by the government could effectively increased the peasants＇ income

Network Defense Decision-Making Based on Deep Reinforcement Learning and Dynamic Game Theory

Existing researches on cyber attackdefense analysis have typically adopted stochastic game theory to model the problem for solutions,but the assumption of complete rationality is used in modeling,ignoring the information opacity in practical attack and defense scenarios,and the model and method lack accuracy.To such problem,we investigate network defense policy methods under finite rationality constraints and propose network defense policy selection algorithm based on deep reinforcement learning.Based on graph theoretical methods,we transform the decision-making problem into a path optimization problem,and use a compression method based on service node to map the network state.On this basis,we improve the A3C algorithm and design the DefenseA3C defense policy selection algorithm with online learning capability.The experimental results show that the model and method proposed in this paper can stably converge to a better network state after training,which is faster and more stable than the original A3C algorithm.Compared with the existing typical approaches,Defense-A3C is verified its advancement.

Computation Offloading in Edge Computing for Internet of Vehicles via Game Theory

With the rapid advancement of Internet of Vehicles(IoV)technology,the demands for real-time navigation,advanced driver-assistance systems(ADAS),vehicle-to-vehicle(V2V)and vehicle-to-infrastructure(V2I)communications,and multimedia entertainment systems have made in-vehicle applications increasingly computingintensive and delay-sensitive.These applications require significant computing resources,which can overwhelm the limited computing capabilities of vehicle terminals despite advancements in computing hardware due to the complexity of tasks,energy consumption,and cost constraints.To address this issue in IoV-based edge computing,particularly in scenarios where available computing resources in vehicles are scarce,a multi-master and multi-slave double-layer game model is proposed,which is based on task offloading and pricing strategies.The establishment of Nash equilibrium of the game is proven,and a distributed artificial bee colonies algorithm is employed to achieve game equilibrium.Our proposed solution addresses these bottlenecks by leveraging a game-theoretic approach for task offloading and resource allocation in mobile edge computing(MEC)-enabled IoV environments.Simulation results demonstrate that the proposed scheme outperforms existing solutions in terms of convergence speed and system utility.Specifically,the total revenue achieved by our scheme surpasses other algorithms by at least 8.98%.

Prospect Theory Based Individual Irrationality Modelling and Behavior Inducement in Pandemic Control

Understanding and modeling individuals’behaviors during epidemics is crucial for effective epidemic control.However,existing research ignores the impact of users’irrationality on decision-making in the epidemic.Meanwhile,existing disease control methods often assume users’full compliance with measures like mandatory isolation,which does not align with the actual situation.To address these issues,this paper proposes a prospect theorybased framework to model users’decision-making process in epidemics and analyzes how irrationality affects individuals’behaviors and epidemic dynamics.According to the analysis results,irrationality tends to prompt conservative behaviors when the infection risk is low but encourages risk-seeking behaviors when the risk is high.Then,this paper proposes a behavior inducement algorithm to guide individuals’behaviors and control the spread of disease.Simulations and real user tests validate our analysis,and simulation results show that the proposed behavior inducement algorithm can effectively guide individuals’behavior.

Comparative study on phase transition behaviors of fractional molecular field theory and random-site Ising model

Fractional molecular field theory(FMFT)is a phenomenological theory that describes phase transitions in crystals with randomly distributed components,such as the relaxor-ferroelectrics and spin glasses.In order to verify the feasibility of this theory,this paper fits it to the Monte Carlo simulations of specific heat and susceptibility versus temperature of two-dimensional(2D)random-site Ising model(2D-RSIM).The results indicate that the FMFT deviates from the 2D-RSIM significantly.The main reason for the deviation is that the 2D-RSIM is a typical system of component random distribution,where the real order parameter is spatially heterogeneous and has no symmetry of space translation,but the basic assumption of FMFT means that the parameter is spatially uniform and has symmetry of space translation.

Product family modeling technology for customized cosmetic packaging design based on basic-element theory

Background:As the market demands change,SMEs(small and medium-sized enterprises)have long faced many design issues,including high costs,lengthy cycles,and insufficient innovation.These issues are especially noticeable in the domain of cosmetic packaging design.Objective:To explore innovative product family modeling methods and configuration design processes to improve the efficiency of enterprise cosmetic packaging design and develop the design for mass customization.Methods:To accomplish this objective,the basic-element theory has been introduced and applied to the design and development system of the product family.Results:By examining the mapping relationships between the demand domain,functional domain,technology domain,and structure domain,four interrelated models have been developed,including the demand model,functional model,technology model,and structure model.Together,these models form the mechanism and methodology of product family modeling,specifically for cosmetic packaging design.Through an analysis of a case study on men’s cosmetic packaging design,the feasibility of the proposed product family modeling technology has been demonstrated in terms of customized cosmetic packaging design,and the design efficiency has been enhanced.Conclusion:The product family modeling technology employs a formalized element as a module configuration design language,permeating throughout the entire development cycle of cosmetic packaging design,thus facilitating a structured and modularized configuration design process for the product family system.The application of the basic-element principle in product family modeling technology contributes to the enrichment of the research field surrounding cosmetic packaging product family configuration design,while also providing valuable methods and references for enterprises aiming to elevate the efficiency of cosmetic packaging design for the mass customization product model.

An Improved CREAM Model Based on DS Evidence Theory and DEMATEL

Cognitive Reliability and Error Analysis Method(CREAM)is widely used in human reliability analysis(HRA).It defines nine common performance conditions(CPCs),which represent the factors thatmay affect human reliability and are used to modify the cognitive failure probability(CFP).However,the levels of CPCs are usually determined by domain experts,whichmay be subjective and uncertain.What’smore,the classicCREAMassumes that the CPCs are independent,which is unrealistic.Ignoring the dependence among CPCs will result in repeated calculations of the influence of the CPCs on CFP and lead to unreasonable reliability evaluation.To address the issue of uncertain information modeling and processing,this paper introduces evidence theory to evaluate the CPC levels in specific scenarios.To address the issue of dependence modeling,the Decision-Making Trial and Evaluation Laboratory(DEMATEL)method is used to process the dependence among CPCs and calculate the relative weights of each CPC,thus modifying the multiplier of the CPCs.The detailed process of the proposed method is illustrated in this paper and the CFP estimated by the proposed method is more reasonable.

Complex adaptive system theory,agent-based modeling,and simulation in dominant technology formation

Dominant technology formation is the key for the hightech industry to“cross the chasm”and gain an established foothold in the market(and hence disrupt the regime).Therefore,a stimulus-response model is proposed to investigate the dominant technology by exploring its formation process and mechanism.Specifically,based on complex adaptive system theory and the basic stimulus-response model,we use a combination of agent-based modeling and system dynamics modeling to capture the interactions between dominant technology and the socio-technical landscape.The results indicate the following:(i)The dynamic interaction is“stimulus-reaction-selection”,which promotes the dominant technology’s formation.(ii)The dominant technology’s formation can be described as a dynamic process in which the adaptation intensity of technology standards increases continuously until it becomes the leading technology under the dual action of internal and external mechanisms.(iii)The dominant technology’s formation in the high-tech industry is influenced by learning ability,the number of adopting users and adaptability.Therein,a“critical scale”of learning ability exists to promote the formation of leading technology:a large number of adopting users can promote the dominant technology’s formation by influencing the adaptive response of technology standards to the socio-technical landscape and the choice of technology standards by the socio-technical landscape.There is a minimum threshold and a maximum threshold for the role of adaptability in the dominant technology’s formation.(iv)The socio-technical landscape can promote the leading technology’s shaping in the high-tech industry,and different elements have different effects.This study promotes research on the formation mechanism of dominant technology in the high-tech industry,presents new perspectives and methods for researchers,and provides essential enlightenment for managers to formulate technology strategies.

Game theory model of exit selection in pedestrian evacuation considering visual range and choice firmness

Exit choice is one of the most important pedestrian behaviors during evacuation.Distance to the exit is a generally recognized factor influencing expected moving time to the exit.Visual range determines how much information a pedestrian can perceive,thus the number of pedestrians within the visual field can be used to estimate waiting time at the exit.Besides,the choice firmness that reflects the degree to which a pedestrian would persist in his/her previous choice of exit is proposed.By integrating game theory into a cellular automata simulation framework,the pedestrian exit choice mechanism is investigated and explicitly modeled in this paper.A systematic analysis of the key factors influencing pedestrian evacuation is conducted,including visual radius and choice firmness of a pedestrian,initial crowd distribution of the room,exit layout as well as exit width.It is found that low choice firmness level can lead to unnatural pedestrian behavior such as wandering,which is adverse to evacuation.The longer the pedestrian's visual radius,the earlier the pedestrian can determine his/her final selection of the exit.Compared with the scenario where the pedestrians are randomly distributed,pedestrians clustered together in a corner of the room lead to high crowd density and imbalanced use of exits.Furthermore,the exit layout and exit width also have a certain influence on pedestrian evacuation process.The results of this paper may be of benefit to the formulation of behavioral rules in other pedestrian simulation models.

Simple Real Time Non-Co-Operative Game Theoritic Model for Energy Cost Optimization in Developing Countries

This paper describes the significant cost saving opportunities for consumers in developing countries by the use of a simple non-cooperative game theoretic mathematical model for demandside management techniques to mitigate the massive use of diesel back-up during grid outages and also other cost optimization schemes. Application of real time load scheduling optimization is investigated during power outages, for residential consumer in India. This method involves a beautiful formulation of a non–cooperation behavior between the diesel generator & the residential consumer during power outages. This involves a tree model with a two player game, where in player one is the diesel generator & player two is the consumer. Depending on the duration of the outage & the consumers limit on the cost for energy different cost optimization strategies can be generated. The load types modeled include passive loads and schedulable, i.e. typically heavy loads. It is found that this DSM schemes show excellent benefits to the consumer. The maximum diesel savings for the consumer due to strategy formulation can be approximately ranging from 45% to as high as 75% for a flat-tariff grid. The study also showed that the actual savings potential depends on the timing of power outage, duration and the specific load characteristics.

Problem-solving model guided by stimulus-organism-response theory:State of mind and coping styles of depressed mothers after cesarean delivery

BACKGROUND The use of a problem-solving model guided by stimulus-organism-response(SOR)theory for women with postpartum depression after cesarean delivery may inform nursing interventions for women with postpartum depression.AIM To explore the state of mind and coping style of women with depression after cesarean delivery guided by SOR theory.METHODS Eighty postpartum depressed women with cesarean delivery admitted to the hospital between January 2022 and October 2023 were selected and divided into two groups of 40 cases each,according to the random number table method.In the control group,the observation group adopted the problem-solving nursing model under SOR theory.The two groups were consecutively intervened for 12 weeks,and the state of mind,coping styles,and degree of post-partum depression were analyzed at the end of the intervention.RESULTS The Edinburgh Postnatal Depression Scale and Hamilton Depression Scale-24-item scores of the observation group were lower than in the control group after care,and the level of improvement in the state of mind was higher than that of the control group(P<0.05).The level of coping with illness in the observation group after care(26.48±3.35)was higher than that in the control group(21.73±3.20),and the level of avoidance(12.04±2.68)and submission(8.14±1.15)was lower than that in the control group(15.75±2.69 and 9.95±1.20),with significant differences(P<0.05).CONCLUSION Adopting the problem-solving nursing model using SOR theory for postpartum depressed mothers after cesarean delivery reduced maternal depression,improved their state of mind,and coping level with illness.

Modelling the Iranian Petroleum Contract fiscal regime using bargaining game theory to guide contract negotiators

Based on Iran's sixth development plan,the country's oil and gas industry requires an investment of about$200 bn in the next five years to increase production.The Iranian government,to attract and motivate international oil company investment in their oil and gas fields,has presented a new type of risk service contract:the Iranian Petroleum Contract(IPC).This paper summarizes the features of the IPC and presents mathematical models of its fiscal regime for the benefit and guidance of both the National Iranian Oil Company(NIOC)and the contractors.Next,adopting bargaining game theory provides a mathematical model for reaching a win-win situation between the NIOC and the contractor.Finally,a numerical example is given and a sensitivity analysis performed to illustrate the implementation of the proposed models.The contractor and the NIOC may use these models when preparing their proposal and in the course of actual negotiations to calculate their internal rate of return,remuneration fee,and net present value for developing the fields at different conditions of their bargaining power,and derive a logical bargain to protect their best possible interests.

Solving the Olbers’s Paradox, Explaining the “Red-Shift”, and Challenging the Relativities by “Sun Matters Theory” and “Sun Model of Universe”, an Evolution of the Einstein’s Static Universe Model

Olbers’s paradox, known as the dark night paradox, is an argument in astrophysics that the darkness of the night sky conflicts with the assumption of an infinite and eternal static universe. Big-Bang theory was used to partially explain this paradox, while introducing new problems. Hereby, we propose a better theory, named Sun Matters Theory, to explain this paradox. Moreover, this unique theory supports and extended the Einstein’s static universe model proposed by Albert Einstein in 1917. Further, we proposed our new universe model, “Sun Model of Universe”. Based on the new model and novel theory, we generated innovative field equation by upgrading Einstein’s Field Equation through adding back the cosmological constant, introducing a new variable and modifying the gravitationally-related concepts. According to the Sun Model of Universe, the dark matter and dark energy comprise the so-called “Sun Matters”. The observed phenomenon like the red shift is explained as due to the interaction of ordinary light with Sun Matters leading to its energy and frequency decrease. In Sun Model, our big universe consists of many universes with ordinary matter at the core mixed and surrounded with the Sun Matters. In those universes, the laws of physics may be completely or partially different from that of our ordinary universe with parallel civilizations. The darkness of night can be easily explained as resulting from the interaction of light with the Sun Matters leading to the sharp decrease in the light intensity. Sun Matters also scatter the light from a star, which makes it shining as observed by Hubble. Further, there is a kind of Sun Matters named “Sun Waters”, surrounding every starts. When lights pass by the sun, the Sun Waters deflect the lights to bend the light path. According to the Sun Model, it is the light bent not the space bent that was proposed in the theory of relativities.

Game Theory Optimization via Diverse Genetic Crossover Intelligence

Game theory is explored via a maze application where combinatorial optimization occurs with the objective of traversing through a defined maze with an aim to enhance decision support and locate the optimal travel sequence while minimizing computation time. This combinatorial optimization approach is initially demonstrated by utilizing a traditional genetic algorithm (GA), followed by the incorporation of artificial intelligence utilizing embedded rules based on domain-specific knowledge. The aim of this initiative is to compare the results of the traditional and rule-based optimization approaches with results acquired through an intelligent crossover methodology. The intelligent crossover approach encompasses a two-dimensional GA encoding where a second chromosome string is introduced within the GA, offering a sophisticated means for chromosome crossover amongst selected parents. Additionally, parent selection intelligence is incorporated where the best-traversed paths or population members are retained and utilized as potential parents to mate with parents selected within a traditional GA methodology. A further enhancement regarding the utilization of saved optimal population members as potential parents is mathematically explored within this literature.