172 results about "Game strategy" patented technology

The invention discloses a multi-agent cluster cooperative attack and defense game method based on near-end strategy optimization, and relates to the technical field of automatic control, and the method comprises the steps: constructing a partial considerable Markov decision process, and generating a multi-agent decision model; establishing a cooperative attack and defense game simulation framework including an attacker agent, a defender agent, a target area and irregular obstacles in the preset task area; parallel trajectory sampling is executed, an observation-action-reward sequence of each agent is collected, and a training data set is generated; a centralized state is constructed by cascading and splicing local observation of each agent, a time sequence difference error and a dominant function are calculated, network parameter updating is carried out by adopting cutting strategy gradient and value function regression, and a trained cooperative attack and defense game strategy network is generated when a reward curve converges. And the multi-agent cluster realizes intelligent cooperative attack and defense game and autonomous obstacle avoidance in a task environment with irregular obstacles.

The invention belongs to the field of mobile edge computing, and particularly relates to a resource pricing problem based on a Stackelberg game in mobile edge computing. The method comprises the following steps: constructing a mobile edge computing model, wherein the mobile edge computing model comprises a server base station, task equipment and cooperative equipment; in a task execution period, for a server base station profit maximization problem of resource pricing based on a Stackelberg game in mobile edge computing, delay brought by task unloading is fully considered in problem modeling, and the delay requirement of a task is met to the maximum extent; analyzing and establishing a dynamic punishment and redistribution mechanism for task unloading failure of the cooperative equipment, wherein the mechanism comprises a punishment cost calculation module, a task value reevaluation module and a game strategy updating module; and obtaining an unloading strategy of the task equipment, a task allocation strategy of the server base station and a resource pricing strategy after the server base station and the cooperative equipment carry out the Stackelberg game by utilizing a greedy strategy, thereby obtaining a final task allocation scheme for maximizing the profit of the server base station. According to the method, the optimal task execution and resource allocation strategy of the server base station is formulated, and the relationship among the task value, the resource cost and the additional resource purchase cost is balanced as required, so that more flexible and efficient task scheduling service can be provided.

The invention discloses a heterogeneous agent distributed multi-alliance game control method, device and equipment and a medium, and relates to the field of multi-agent system cooperative control and game theory cross application, and the method comprises the steps: dividing agents in a heterogeneous unmanned cluster system into a plurality of alliances; establishing a high-order heterogeneous linear state dynamical model for the intelligent agent in each alliance; constructing an internal and external double-layer alliance communication topological graph according to an actual communication condition; and on the basis of the model and the topological graph, through a distributed Nash equilibrium search algorithm containing a search layer and an output adjustment layer, performing iterative optimization on an agent decision state to obtain a multi-alliance game strategy, and controlling the agent according to the strategy. According to the method, the cooperative control problem of the heterogeneous agents in a multi-alliance game scene can be effectively solved, the Nash equilibrium point is quickly searched, the decision state is adjusted to be optimal, and the cooperative control performance and task execution efficiency of a multi-agent system are improved.

The invention discloses an incomplete information game intelligent processing method and system based on secure multi-party computing, a terminal and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining a game participant set, an initial game state, a historical action set, a terminal historical set, and a preset game strategy based on a secure near-end strategy optimization algorithm, an action set, an information set and a revenue function corresponding to each participant are obtained; according to the participant set, the initial game state, the historical action set, the terminal historical set, the game strategy, and the action set, the information set and the revenue function corresponding to each participant, carrying out modeling based on a modeling framework of an extended game tree, and constructing a security extended game model; and according to a preset optimization target and leaf nodes in the security extension type game model, determining a game result between the participants and outputting the game result. Therefore, the data security can be improved, and the security of the game process can be improved.

The invention discloses an Internet of Things monitoring management method and system based on deep learning, and relates to the related technical field of Internet of Things monitoring management, and the method comprises the steps: reading node multi-source data through an intelligent agent, constructing a deep causal structure field through cross-modal deep causal reasoning, and revealing the relationship between environmental inducements, state paths and risk propagation; constructing a space-time causal tensor representing dynamic risk distribution of a monitoring area, quantitatively evaluating monitoring income, energy consumption cost and risk exposure, executing game analysis under a multi-party game strategy, and establishing an intelligent agent behavior strategy; and updating the monitoring network topology, executing deep space-time-cause and effect joint reasoning, generating an exception explanation result, and performing exception report management. According to the invention, the technical problems of lack of causal interpretability, resource allocation rigidness and insufficient abnormal response capability of Internet of Things monitoring in the prior art are solved, and the technical effects of accurate abnormal management of Internet of Things nodes and improvement of resource utilization efficiency and risk decision interpretability are achieved.

The invention discloses a game strategy model generation method, a game agent behavior strategy generation method, corresponding devices, electronic equipment and a storage medium. The game strategy model generation method comprises the steps of obtaining a preset data set, wherein the preset data set comprises a first state feature sequence of a game agent in a game and a second state feature sequence of a player character; according to the first state feature sequence and the second state feature sequence, training a to-be-trained reward model until a preset training ending condition is met, and obtaining a trained reward model; inputting the target state feature of the game agent into the trained reward model to obtain first reward data corresponding to the game agent; and training a to-be-trained strategy model according to the first reward data, wherein the strategy model is used for generating a behavior strategy for a game agent in the game. According to the method, the stability and scene adaptability of the generated game strategy model can be improved.

The invention discloses a global intelligent marketing method based on multi-modal information fusion. The method comprises the following steps: firstly, collecting multi-source heterogeneous data, extracting text, vision and behavior characteristics by using deep learning, and mapping the text, vision and behavior characteristics to a public semantic space for fusion through an attention mechanism; and then, constructing a dynamic user portrait based on the fusion features and identifying a user intention stage. On the basis, the click rate is estimated by using a double-tower network, and a dynamic bidding strategy is generated in combination with a reinforcement learning DQN model and PID control. And finally, aiming at customer inquiry, generating a quoted price in combination with a cost model or performing automatic negotiation by using a time-dependent game strategy, and updating model parameters in real time through online incremental learning. Through full-link intelligent closed-loop control, accurate user intention recognition, efficient advertisement putting and automatic business negotiation are realized, the marketing conversion rate is effectively improved, and the operation cost is reduced.

The invention discloses a supply chain supply and demand dynamic matching method based on a multi-agent game, and relates to the technical field of supply chain management, and the method comprises the following steps: constructing a multi-agent system, dividing agents, carrying an edge module, interacting with an MQTT protocol through a block chain, and encrypting sensitive data; sensing supply and demand information in real time, acquiring parameters according to dynamic frequency, and synchronizing the parameters to a sharing middle station after filtering and cleaning; generating a game strategy, constructing a model by taking Nash equilibrium as a target, and iteratively generating an initial strategy; dynamic matching is executed, requirements are distributed according to priorities, deviation is monitored, and negotiation and adjustment are carried out; evaluating a matching effect in multiple dimensions, and calculating a comprehensive score by using an analytic hierarchy process; and iteratively optimizing a strategy, adjusting parameters according to an evaluation result, and updating a strategy library to adapt to dynamic change. The method breaks the supply chain information island, balances the interests of all participants to achieve overall optimization, improves the supply and demand matching precision and dynamic adaptation capability, and enhances the efficiency and stability of the supply chain.

The invention discloses an APT (Advanced Persistent Threat) attack defense method based on edge intelligence, which aims to improve the dynamic perception capability, strategy response capability and resource adaptability of an edge network to APT attacks, and comprises the following steps of: firstly, constructing a topological adjacency matrix of an edge node communication network and defining five states and state conversion processes of a life cycle of edge equipment; the method comprises the following steps: firstly, designing an attack defense model based on optimal control and differential game, then introducing a system evolution model based on hidden confrontation, and accurately simulating dynamic interaction of attack and defense in an actual edge network, secondly, designing an attack defense model based on optimal control and differential game, and finally, adopting a Nash strategy reinforcement learning mechanism based on a multi-agent deep Q network, optimizing an edge game strategy, and finally, obtaining an attack defense result. And the attack detection performance is improved. According to the method disclosed by the invention, the modeling precision and defense effectiveness of the system in a complex APT attack scene are remarkably improved, and the method is suitable for key infrastructure network environments with relatively high requirements on safety, timeliness and expandability, such as industrial Internet and Internet of Things.

The invention discloses a multi-chess game robot control method and system based on behavior and emotion adaptive game difficulty adjustment. The method comprises the following steps: initializing a multi-chess game scene; interaction object behavior and emotion perception; calculating the instantaneous state time sequence of the interaction object; performing two-way mapping calculation on the difficulty coefficient to obtain a final difficulty coefficient; and according to the output final difficulty coefficient state, a game strategy of a preset AlphaZero reinforcement learning model and kinematics parameters of the mechanical arm are adjusted, so that dual anthropomorphism of the game level and the action performance of the robot is achieved. Executing control and safety detection; according to the method, behaviors and emotions of the interaction object are sensed in real time, self-adaptive adjustment of the game difficulty is achieved in the two dimensions of the game strategy and the physical action, and the interaction experience and the intelligent level of man-machine game are effectively improved.

The invention discloses a zero-sum game-based two-degree-of-freedom helicopter safety controller design method, which relates to the technical field of two-degree-of-freedom helicopter control, and comprises the steps of constructing a system equation, introducing an attack signal and a preset performance function, decomposing into two stages of subsystems, and designing a virtual controller, an adaptive law and a self-triggering mechanism. And solving a Hamiltonian-Jacobian-Exophone equation by using a zero-sum game theory, and finally verifying the stability of the system through a Lyapunov function. According to the invention, it can be ensured that the tracking error converges to the steady-state error interval within the predetermined time, and the transient and steady-state performance of the system is significantly improved; and meanwhile, a Nussbaum function and a zero-sum game strategy are introduced to improve the attack resistance, the communication burden and actuator abrasion are reduced through a self-triggering mechanism, and the sesame effect is avoided.

The invention provides an intelligent network connection vehicle confluence area cooperative control system and method, and relates to the technical field of intelligent traffic systems. The method comprises the following steps: collecting vehicle trajectory data in a confluence area, and carrying out conflict risk quantitative evaluation and risk grade division based on TTC and PET composite indexes; a maximum value selection operator of a traditional game enhanced DQN is replaced by mixed strategy Nash equilibrium distribution, and multi-agent collaborative decision optimization is realized; predicting a vehicle trajectory based on a space-time attention mechanism, and generating a control scheme, a vehicle control command and an execution time sequence in combination with a risk level and a game strategy; and carrying out real-time interaction on information of the road side unit and the CAV vehicle, and pushing a control scheme, a vehicle control command and an execution time sequence to complete cooperative control of the intelligent network connection vehicle confluence area. The method overcomes the defects that in the prior art, vehicle control is conducted only according to a traditional reinforcement learning algorithm, learning is unstable, and strategy conflicts exist in the multi-agent environment.

The invention provides an adversarial game strategy real-time optimization method and system based on reinforcement learning. The method comprises the following steps: acquiring historical game situation characteristics; capturing dynamic motion parameter data of the target object in real time to generate a physical dynamic vector; inputting the physical dynamic vector into a preset real-time data compression circuit for high-dimensional signal processing, and generating a low-dimensional electrical response signal; performing correlation analysis on the historical decision characteristics and the historical game situation characteristics of a preset to-be-optimized confrontation game strategy to generate strategy evolution characteristics; the strategy evolution characteristics and the time sequence continuity characteristics of the low-dimensional electrical response signals are fused, and dynamic confrontation environment state data are generated; and inputting the dynamic confrontation environment state data into a preset reinforcement learning model to generate an optimized confrontation game strategy. According to the method, cross-domain collaborative closed loop from physical motion data to the decision strategy is realized, the response delay bottleneck of traditional game strategy optimization is broken through, and millisecond-level updating of the confrontation strategy along with the motion state of the target object is ensured.

The invention provides a postoperative patient supervision system for neural interventional therapy, and relates to the technical field of medical big data and artificial intelligence, and the system comprises a multi-modal data collection center, an interference feature decoupling unit, a trust capital quantification unit, a game strategy arbitration unit and a supervision execution unit. The multi-modal data acquisition center is configured to call time sequence monitoring data of a monitoring object; the trust capital quantification unit performs trust loss evaluation analysis on historical interaction feedback data; and the game strategy arbitration unit is configured to perform comparative analysis on the current clinical trust capital index and a preset trust threshold. According to the system, time-frequency domain matching is carried out on the residual error sequence and behavior state marking data, it is ensured that the system only carries out risk evolution prediction on neurogenic hemodynamic changes, and therefore the false alarm rate caused by external interference in a complex postoperative monitoring environment is remarkably reduced, and pure pathological feature components are extracted.

The invention provides a cloud security game method and device based on a trust mechanism and application, and relates to the technical field of network security protection. The processing method comprises the following steps: acquiring game information in a cloud network; the cloud network comprises a plurality of game stages, each game stage correspondingly has game information, and the game information can reflect a game process and / or a game result corresponding to at least one game stage; according to the game information, determining a game stage in which the cloud network is located, and an attacking agent and a guarding agent participating in the game in the game stage; at least two participants exist in the attacking party main body or the guarding party main body; and adjusting a game strategy corresponding to the game stage based on the trust mechanism between the at least two participants. According to the invention, the game process and / or game result of the game stage are / is determined according to the game information, so that the game strategy is deployed according to the obtained conditions of the attacker main body and the keeper main body to ensure the security of the cloud network.

The invention discloses a spacecraft game control method and device under incomplete information based on adaptive dynamic programming, and belongs to the technical field of spaceflight. The method comprises the following steps: establishing a spacecraft pursuit game relative motion model, and estimating an unknown strategy and spatial disturbance of a non-cooperative target through a fixed time expansion state observer; constructing an incomplete information pursuit game model based on the estimation state, designing an adaptive dynamic programming game strategy, and approaching an optimal game strategy by using a neural network; an Ada-delta method is introduced to adaptively adjust the learning rate of the neural network, so that the convergence efficiency is improved; an escape spacecraft shape model is constructed based on a super-quadric surface, and collision avoidance is ensured in combination with a potential function; a time constraint problem is solved through a fixed time compensation item, and an approximate optimal game strategy in fixed time is realized. According to the method, the problems of time constraint and strategy optimization of spacecraft game control under incomplete information are effectively solved while the safety is ensured.

Systems and methods are described for determining team formations, player performance relating to adherence to predetermined team formations, and coach performance during a live sports event. Methods include selectively obtaining and analyzing a video feed of the sports event to determine a player position with respect to field of play, another player from the same team, and / or a player from the opposing team. Methods divide the field into zones and identify predetermined formations (such as offense or defense formations) based on a current formation on the field of play. Distance differential vectors between current and identified locations / formations of a player or the team may be calculated, and used to determine if the player's current position on the field of play is aligned with an identified formation. If so, then one or more enriched user interface features may be provided, which include displaying current team formations and player statistics.

The invention provides an endoscope sharing control method based on a human body signal and a game theory, and belongs to the field of medical instruments, and the endoscope sharing control method comprises the following steps: obtaining eyeball movement data; based on the eyeball movement data, determining eye movement control position data used for representing a manual control intention through gaze thermodynamic diagram analysis; based on the eyeball movement data, through quantitative analysis of sight, eye opening degree and pupil size, obtaining reference indexes used for representing intention integrating degree and manual control burden; machine control position data used for representing the expected moving distance of the endoscope are obtained by analyzing the position relation between a target object and the endoscope in an endoscope image, and the endoscope image is collected in real time through the endoscope; based on the machine control position data, the eye movement control position data and the reference index, determining shared control position data through a cooperative game strategy; movement of the endoscope is controlled based on the shared control position data.

This invention discloses a multi-agent game-theoretic collaborative planning method for airport integrated energy systems under physical-information-market coupling. The invention constructs a physical-market coupling model of the interaction between the integrated building energy system and the smart grid; defines independent decision-making entities such as the smart grid, multiple integrated building energy system operators, and renewable energy providers, and their multi-period objective functions; divides the planning period into multiple stages, establishing a dynamic game framework considering long-term investment and short-term operational coordination; uses subgame refined Nash equilibrium as the solution objective, and solves the inter-agent game strategies through mathematical programming and equilibrium constraints, and multi-agent reinforcement learning algorithms; generates multi-period equipment investment time-series diagrams for multi-agent collaboration and outputs typical daily scheduling schemes. The advantages of this invention are that it solves the problem of multi-agent interest conflicts and long-term / short-term decision coupling in airport integrated energy systems, reduces total system cost and carbon emissions, avoids redundant investment, and improves the renewable energy absorption capacity.

Embodiments of the present disclosure relate to a method and apparatus for adjusting an operation area, a computer device and a storage medium. The main steps of the method include: displaying an elimination operation area in a game field; in response to a first type of elimination operation on the elimination operation area, selecting a virtual object associated with the first type of elimination operation; determining a placement area of the selected virtual object; and adjusting the occupation area of the elimination operation area in the game field when there is an overlapping area between the placement area and the elimination operation area. By using the method, the placement area and the number of virtual objects can be improved, the number of virtual objects that can be placed can be increased while ensuring the richness of virtual objects, and the user can place virtual objects in a reasonable area according to the game strategy, thereby improving the game experience.

An object of a system according to an embodiment is to efficiently collect and appropriately provide game mastery information.SOLUTION: A system includes a reception unit, an analysis unit, and a provision unit. The receiving unit receives information. The analysis unit analyzes the information received by the reception unit. The providing unit provides strategy information based on the information obtained by the analysis unit.SELECTED DRAWING: Figure 1

The application provides a multi-unmanned agent-oriented cooperative firepower attack strategy generation method, and belongs to the field of intelligent game strategy generation. The method comprises the following steps: constructing an adversarial environment, generating an initial strategy population, performing parallel calculation on the fitness of the strategy population, performing crossover and mutation on the strategy population, and iteratively evolving the strategy population. The application solves the problems of large calculation amount and long training time of the intelligent game strategy generation method, represents the strategy as an action sequence, constructs fitness based on game winning rate, uses parallel multi-opening evolutionary calculation method, and intelligently, automatically and efficiently generates a cooperative firepower attack strategy with the fitness as the optimization target.

The application discloses a network game strategy generation method based on node average path constraint, and the method comprises the following steps: acquiring the topological structure of a network, determining the strategy set of an attack party and a defense party, and constructing a basic model of the network game; calculating the shortest path between nodes in each attack strategy and each defense strategy, taking the average value, and obtaining the average path corresponding to each attack strategy and each defense strategy; setting a constraint function based on the average path, so that the selection probability of each attack strategy and each defense strategy is less than the corresponding function value, and obtaining the strategy constraint set of the attack party and the strategy constraint set of the defense party; representing the network performance by a maximum connected piece scale index, calculating the income of the attack party and the defense party under each strategy profile, and obtaining the income matrix of the network game model; and replacing the basic model of the network game with a linear programming problem for solving, and obtaining the mixed strategy Nash equilibrium solution of the attack party and the defense party.

This invention discloses a method and system for regenerative braking energy storage scheduling based on dynamic prediction. The method includes: collecting data on vehicle operation, line conditions, environment, energy storage status, grid load, and real-time carbon price; generating predicted values ​​for train braking energy, multi-vehicle conflict probability, and energy storage health status through a prediction model, and calculating expected carbon emission reduction benefits; inputting the above results into a decision model, which uses trains, energy storage systems, the grid, and carbon trading nodes as game players, and performs collaborative optimization through multi-agent reinforcement learning fusion game strategies, outputting charging and discharging scheduling commands; issuing execution commands through a cloud-edge-device architecture, with edge nodes autonomously responding to emergencies in case of communication interruption. The system includes corresponding modules. This invention achieves proactive collaborative optimization from passive storage to "prediction-game theory-maintenance-carbon value-added," improving energy utilization and economic efficiency.

According to the network threat analysis method and system based on the improved evolutionary game, shared expected benefits are analyzed through modeling, quantitative analysis is carried out through the learning evolutionary game, a reasonable incentive strategy is obtained, meanwhile, a user selects a game strategy through autonomous learning, returned incentive is corrected, and the network threat analysis efficiency is improved. Optimal screening of threat intelligence is realized, sharing and utilization efficiency of network threat intelligence is better promoted, network security defense capability is improved, and the defects that the prior art is lack of reinforcement learning capability, needs to depend on a large number of intelligence resources, can only passively receive incentives and intelligence returned by communities, and is poor in security defense capability are overcome. And protection and defense cannot be fully carried out according to the self condition maximization.

The invention provides a game data processing method, which is characterized in that a graphical user interface is provided through a terminal device, the graphical user interface comprises a virtual scene and a first virtual object located in the virtual scene, and the method comprises the following steps: responding to a trigger instruction for a target skill, and controlling the target virtual object to release an effect corresponding to the target skill, wherein the target skill configures a corresponding target condition and a target skill effect, the target virtual object comprises at least one of the following objects: a first virtual object, a second virtual object in the same formation as the first virtual object, and a third virtual object in different formation from the first virtual object, and detecting whether a game event meeting the target condition exists or not; and when the game event meeting the target condition exists, controlling to add a gain effect to the first virtual object. The skill effect can be dynamically adjusted according to the game event, a user can select a proper skill use opportunity according to the event in the game, and the strategy and interestingness of the game are improved.

The application belongs to the technical field of aerospace, and relates to a distance-keeping game control framework and method for a maneuvering non-cooperative target. The framework comprises a rolling control window, an event perception window arranged in each rolling control window, and an event triggering mechanism arranged in each event perception window. The event perception window predicts an orbit state through a two-body dynamics model, the event triggering mechanism calculates a relative distance between an active satellite and a target satellite at each discrete point according to an orbit prediction result, and judges whether the relative distance constraint is met. If the relative distance constraint is met, an impulse maneuver strategy of the active satellite as a decision output is calculated through a game strategy optimization algorithm. Simulation results show that the application can realize long-time distance keeping, and the success rate reaches 98% in a 12-hour maintenance task, which is increased by 308.3% compared with a traditional algorithm.

The application provides a tunnel fan group cooperative control dynamic optimization method and system. In the application, the dynamic data of vehicles in the tunnel is tracked through the lightning and vision fusion technology, the data is analyzed by the edge computing node, and the environmental monitoring index is combined to construct a multi-dimensional game parameter set. Based on the dynamic game theory model, the fan cooperative response strength is dynamically adjusted, the smoke exhaust efficiency and the evacuation passage wind speed constraint are balanced, the fan control priority is calculated, the differentiated rotating speed and deflection angle control are driven, the environmental feedback data is generated, and the game strategy weight is iteratively optimized, so that the rolling time domain control is realized until the vehicle movement and the environmental index reach the stable interval. The technical scheme provided by the application realizes the dynamic cooperative optimization of tunnel smoke exhaust and evacuation safety, and improves the ventilation response efficiency and evacuation passage safety in emergency scenes.

The invention discloses a game information interaction method, a storage medium and an electronic device. The method comprises the steps that a first client responds to a route drawing operation and receives a target command route drawn by a user in a virtual scene; the first client responds to the route configuration operation and configures route information for the target command route, the route information comprises game strategy information and target object information of the target command route, and the target object information comprises the second client; the first client sends a notification message to the second client based on the target command route and the route information; and the second client receives the notification message and displays the target command route in the virtual scene. The technical problems of weak game immersion and low command efficiency of a game command mode provided in the related technology are solved.

Embodiments of the present application provide a vehicle control method, device, equipment, medium and product, traffic environment information of a game participant is obtained; based on the traffic environment information of the game participant, first decision information of a first vehicle, second decision information of a second vehicle and a game result of each game strategy in K*L game strategies are determined; based on the traffic environment information of the game participant, the first decision information, the second decision information and the game result of each game strategy in the K*L game strategies, an expected utility function corresponding to the first vehicle is constructed; and the first vehicle is controlled to perform a target behavior. Embodiments of the present application improve the reliability of vehicle decision.