Multi-Model Agent Behavior Generation for Data-Scarce Interactions
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Solution Overview
Problem
Existing methods struggle to efficiently generate realistic and physically accurate behaviors for interactions between agents, particularly in rare scenarios, requiring extensive training data and lacking efficient knowledge transfer across domains.
Innovation Solution
A method involving pre-trained neural networks to map descriptions of agent behaviors onto representations, using multiple models to refine and generate output variables, allowing for less data-intensive training and domain knowledge transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing methods are used to generate realistic agent behaviors, then behavior realism can be achieved, but extensive training data is required
Solution Approach 1:
The patent segments the behavior generation process into multiple independent models: a first model for mapping descriptions to representations, a second model for mapping representations to trajectories, and a third model for mapping trajectories to controller parameters. This segmentation allows each model to be trained on smaller, specialized datasets rather than requiring one large comprehensive dataset, thereby reducing the overall training data volume while maintaining behavior realism.
Solution Approach 2:
The patent applies preliminary action by pre-training the first and third models independently before training the second model. The first model is pre-trained to map descriptions to representations, and the third model is pre-trained to map trajectories to controller parameters. This preliminary preparation reduces the data requirements for the main training phase, as these models already have learned mappings from their pre-training on smaller datasets.
2Productivity
If traditional training methods are used, then models can be trained, but data-intensive training is required
Solution Approach 1:
The training process is segmented into independent phases for different models. The first model is trained separately on description-representation pairs, the third model is trained separately on trajectory-controller parameter pairs, and only the second model requires training on representation-trajectory pairs. This segmentation improves training efficiency by allowing parallel training and reducing the total data volume required compared to training a single comprehensive model.
Solution Approach 2:
The patent changes the parameter space by using different representation formats for different models. The first model outputs a first representation, the second model maps this to a second representation (trajectory), and the third model maps the trajectory to controller parameters. This parameter transformation approach allows each model to operate in its optimal parameter space, improving training efficiency and reducing data requirements.
3Reliability
If comprehensive behavior coverage is achieved, then interaction quality improves, but training complexity increases
Solution Approach 1:
The patent divides the complex behavior generation task into three specialized models, each responsible for a specific transformation. This segmentation reduces training complexity for each individual model while achieving comprehensive behavior coverage through their composition. Each model can be trained independently on smaller datasets, making the overall system more manageable despite the comprehensive coverage achieved.
Solution Approach 2:
The patent creates a universal framework where the three models work together to handle diverse interaction scenarios. The first model handles description-to-representation mapping, the second model handles representation-to-trajectory mapping, and the third model handles trajectory-to-controller mapping. This multi-functional system achieves comprehensive behavior coverage while keeping each component relatively simple and manageable.
Data Source
AI summary
A device and a method for training at least one model or for generating a first agent, in particular for an interaction between the first agent and a second agent. A description of a behavior of the first agent, in particular in the interaction between the first agent and the second agent, is mapped onto a first representation using a first model; the first representation is mapped onto a second representation by means of a second model; the second representation is mapped onto an output variable for influencing the behavior of the first agent using a third model. The description is specified in natural language, in text form or audio form, or in formal language or in digital graphic form, wherein the behavior of the first agent, in particular in the interaction between the first agent and the second agent, is specified depending on the output variable.