Multi-party Resource Orchestration via Semi-Cooperative Nash Equilibrium
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Solution Overview
Problem
Current systems lack an efficient method to distribute resources, such as autonomous vehicles, across a geographical area to meet service demands while optimizing metrics like revenue, traffic flow, and customer experience, especially in dynamic and complex environments like cities.
Innovation Solution
The method establishes a semi-cooperative Nash equilibrium using AI, neural networks, and reinforcement learning to determine the optimal distribution of resources by considering real-time traffic situations and optimization policies of multiple parties, allowing for adaptive and efficient allocation of resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional resource distribution methods are used, then implementation is simple, but resource allocation efficiency and service demand fulfillment deteriorate in dynamic urban environments
Solution Approach 1:
The patent implements dynamic resource distribution by continuously adjusting the positions of autonomous vehicles based on real-time demand patterns, traffic conditions, and service requests. The system transitions from static allocation to dynamic optimization, where vehicle locations are constantly adapted to current urban conditions, thereby improving resource allocation efficiency without requiring overly complex manual intervention
Solution Approach 2:
The system incorporates feedback mechanisms by monitoring service demands, traffic situations, and vehicle positions in real-time. This feedback loop enables the optimization module to continuously refine resource distribution strategies, learning from past performance and adapting to changing conditions, thus achieving high productivity through iterative improvement rather than static planning
Solution Approach 3:
The autonomous vehicles and optimization system operate autonomously, with the optimization module independently making distribution decisions based on accumulated data and learned patterns. The system serves itself by automatically adjusting resource allocation without requiring complex external coordination, reducing operational complexity while maintaining high efficiency
2Adaptability or versatility
If real-time optimization is implemented to meet dynamic service demands, then service quality improves, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing historical service demand data, traffic patterns, and vehicle performance metrics. This pre-computed knowledge base enables faster real-time decision-making, as the optimization module can leverage pre-analyzed patterns rather than computing everything from scratch, thus achieving high adaptability with reduced computational burden during critical optimization moments
Solution Approach 2:
The optimization module focuses on optimizing critical parameters and key vehicle positions rather than simultaneously optimizing all possible variables. By concentrating computational resources on the most impactful decisions (partial action) or by considering a broader set of possibilities than strictly necessary (excessive action), the system achieves effective adaptability while managing computational complexity through selective optimization
3Productivity
If multiple parties with different optimization policies are coordinated, then overall system performance improves, but achieving equilibrium and coordination becomes more difficult
Solution Approach 1:
The optimization module serves as an intermediary that coordinates between multiple parties (e.g., different autonomous vehicle operators, service providers, and urban management systems). This mediator translates and reconciles different optimization policies into a unified resource distribution strategy, finding equilibrium points that satisfy multiple stakeholders' interests while improving overall service delivery, thus easing the coordination burden that would otherwise exist between independent parties
Data Source
AI summary
Distributing resources in a predetermined geographical area, including: retrieving a set of metrics indicative of factors of interest related to operation of the resources for at least two parties, each having a plurality of resources, retrieving optimization policies indicative of preferred metric values for each party, retrieving at least one model including strategies for distributing resources in the predetermined area, the at least one model based on learning from a set of scenarios for distributing resources, retrieving context data from real time systems indicative of at least a present traffic situation, establishing a Nash equilibrium between the metrics in the optimization policies of the at least two parties taking into account the at least one model and the context data, distributing the resources in the geographical area according to the outcome of the established Nash equilibrium.