Agent Behavior Prediction Using Latent State Moments
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for predicting the behavior of agents in dynamic systems with multiple interacting agents are inefficient in terms of computing resources and do not effectively consider the latent states and interactions between agents.
Innovation Solution
A computer-implemented method that determines the moments of distributions modeling latent states of agents, using recursive calculations and context variables to predict behavior efficiently, considering deterministic and stochastic changes, and the interactions between agents through a graph-based approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods for predicting agent behavior are used, then prediction capability is achieved, but computing resource consumption is high
Solution Approach 1:
The patent segments the prediction problem by modeling each agent's behavior independently through its own latent state distribution, rather than modeling the entire system simultaneously. This allows parallel computation across agents and reduces the computational complexity from O(N^2) to O(N) where N is the number of agents.
Solution Approach 2:
The patent changes the representation from explicit detailed state vectors to latent state distributions characterized by moments (mean, variance). This parameter transformation compresses the state representation and enables more efficient computation while preserving essential behavioral characteristics.
2Measurement precision
If detailed modeling of agent interactions is performed, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent extracts the essential interaction information into the latent state distributions themselves, which capture statistical dependencies between agents. This separates the interaction modeling from the explicit calculation, allowing accurate prediction without complex interaction terms.
Solution Approach 2:
The patent uses partial modeling by focusing computation only on the necessary moments (first and second moments) of the latent state distributions rather than computing the complete joint distribution. This provides sufficient accuracy for prediction while significantly reducing computational requirements.
Data Source
AI summary
A computer-implemented method for predicting a behavior of agents in a dynamic system with a multiplicity of interacting agents depending on the latent state thereof. For a plurality of components and for a plurality of time points up to a prediction time point, a value of a first moment of a first distribution, which models the latent state of the agents, is determined for each component. A value of a second moment of the first distribution is determined. An expected value for a first moment of a second distribution at the prediction time point is determined for each component depending on the value of the first moment of the first distribution at the prediction time point and depending on the value of the second moment of the first distribution at the prediction time point. The second distribution models the behavior of the agents depending on the latent state thereof.