Neuro-symbolic AI Agent Functional Maps for Generalization
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Solution Overview
Problem
Artificial intelligence agents, particularly embodied mobile-manipulation agents like robots, face challenges in generalizing to unseen environments due to overfitting from restricted training data, leading to confusion with new geospatial scenes and objects, and inability to perform well in navigation and question-answering tasks.
Innovation Solution
The implementation of a neuro-symbolic approach that combines symbolic reasoning and statistical prediction, using external domain knowledge like spatial and commonsense knowledge to aid agents in reasoning and pattern-matching, enabling active sub-goal discovery, scene priors, and commonsense reasoning for multimodal question answering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine-learning models are trained with restricted training data (few geospatial scenes, limited objects, simple dialogues), then performance is maximized within the training domain, but the models become confused and unable to generalize to new geospatial scenes and objects
Solution Approach 1:
The patent introduces functional maps as intermediary structures that connect image inputs to region and object labels. These functional maps serve as a bridge between the training data and unseen environments, allowing the model to generalize by mapping new inputs through the same functional relationships learned during training, rather than requiring direct exposure to all possible scenarios.
Solution Approach 2:
The patent segments the question-answering task into multiple components: extracting functional maps from images, identifying keywords from questions, generating action predictions, and retrieving relevant information. This segmentation allows each component to be optimized independently while working together to solve the generalization problem across diverse environments.
2Productivity
If machine-learning models rely solely on experience and pattern-matching strategies, then they can perform well on familiar tasks, but they fail when presented with noisy instructions or questions with new grammatical structure
Solution Approach 1:
The patent replaces traditional mechanical pattern-matching approaches with a neuro-symbolic system that combines neural network-based image understanding with symbolic reasoning components. This substitution enables the system to handle new grammatical structures and noisy instructions by using functional maps and structured reasoning rather than relying solely on statistical patterns from training data.
3Loss of time
If embodied agents are trained with limited geospatial scenes and objects, then training is computationally efficient, but the agents cannot locate or interact with new objects in unseen environments
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing functional maps that capture the relationships between images, regions, and objects during training. These pre-computed functional maps can be efficiently retrieved and applied to new environments without requiring retraining, thus maintaining computational efficiency while enabling recognition of new objects through the established functional relationships.
Data Source
AI summary
A method includes receiving input dialog including a text string corresponding to at least one question and extracting at least one keyword from the text string. The method also includes generating at least one action prediction and providing one or more sub-questions associated with the at least one question. The method also includes receiving one or more answers to the one or more sub-questions, generating at least one sub-goal based on the one or more answers, and traversing an environment based on the at least one sub-goal. The method also includes receiving one or more images associated with the environment, predicting, using the one or more images, an answer to the at least one question, and providing, at an output mechanism, the answer to the at least one question.