Generative AI Multi-Agent Controllers for Coordinated Problem Solving
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In heterogeneous AI environments, traditional AI search techniques fail to guarantee goal attainment due to uncertainty and conflicting goals among agents, leading to suboptimal decision-making and scalability issues in Multi-Agent Reinforcement Learning (MARL) problems, where agents have partial observability and non-stationary environments.
Innovation Solution
The development of AI models that generate diverse, explainable, and coordinated multi-agent controllers using generative neural networks, optimized for quadratic utility functions, which can communicate and coordinate actions among agents to achieve common goals, simplifying collaborative problem-solving by focusing on overall team performance rather than individual task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional AI search techniques are used in heterogeneous AI environments, then individual agents can solve their sub-problems independently, but goal attainment cannot be guaranteed due to uncertainty and conflicting goals among agents
Solution Approach 1:
The patent merges multiple independent AI agents into a unified multi-agent system where agents communicate and coordinate through a shared environment. The generative AI model integrates individual agent policies into a coordinated team behavior that guarantees goal attainment while maintaining individual agent autonomy.
Solution Approach 2:
The patent implements feedback mechanisms where agents observe each other's actions and outcomes in a shared environment. This feedback loop enables agents to adapt their policies dynamically, resolving conflicts and coordinating efforts to ensure collective goal attainment while preserving independent problem-solving capabilities.
2Adaptability or versatility
If Multi-Agent Reinforcement Learning is used to learn optimal policies, then agents can adapt to conflicting goals, but the learning objective becomes multidimensional and convergence cannot be guaranteed
Solution Approach 1:
The patent segments the multidimensional learning objective into manageable components by using generative AI to synthesize training data and scenarios. This segmentation allows agents to learn optimal policies for specific sub-tasks while the overall system converges toward coordinated team behavior, maintaining adaptability to conflicting goals while ensuring learning convergence.
Solution Approach 2:
The patent applies preliminary action by pre-training agents on synthesized data generated by the generative AI model before deploying them in the actual multi-agent environment. This preliminary training phase establishes stable policy foundations that enable subsequent convergence while maintaining the ability to adapt to conflicting goals during deployment.
3Productivity
If AI agents improve their policies according to their own rewards concurrently, then individual performance improves, but the environment becomes non-stationary and estimated potential reward becomes inaccurate
Solution Approach 1:
The patent introduces a shared environment as an intermediary that mediates between individual agent rewards and the overall system state. This intermediary provides a common reference frame that maintains stationarity despite individual policy improvements, enabling accurate reward estimation while preserving individual agent productivity and policy improvement capabilities.
4Adaptability or versatility
If the joint action space increases with the number of AI agents, then more complex coordinated behaviors can be achieved, but scalability issues arise due to the combinatorial nature of the problem
Solution Approach 1:
The patent segments the exponentially growing joint action space into individual agent action spaces by using generative AI to synthesize training scenarios that teach agents coordinated behaviors. This segmentation reduces computational complexity from exponential to polynomial scaling while maintaining the ability to achieve complex coordinated behaviors through learned policies.
Solution Approach 2:
The patent uses copying by generating synthetic training data and scenarios through the generative AI model that replicate complex multi-agent interactions. This allows agents to learn coordinated behaviors from synthesized examples without experiencing the full combinatorial explosion of the actual joint action space, improving scalability while maintaining behavior complexity.
Data Source
AI summary
In general, the disclosure describes techniques for Artificial Intelligence (AI) models that can automatically generate diverse, explainable, interpretable, reactive, and coordinated behaviors for a team. In an example, a method includes receiving multimodal input data within a simulator configured to simulate solving a predefined problem by a team including a plurality of agents; generating one or more generative neural network models based on the multimodal input data and based on a predetermined threshold of success of problem solving in the simulator; outputting, by the one or more generative neural network models, one or more multi-agent controllers, wherein each of the one or more multi-agent controllers comprises recommended behaviors for each of the plurality of agents to solve the predefined problem in a manner that is consistent with the multimodal input data.