Planning Ontology Learning for Complex Scenario Generalization
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Solution Overview
Problem
Traditional planning systems struggle with handling complex scenarios involving uncertainty, partial observability, and dynamic environments due to their reliance on fixed metadata, limiting their ability to generalize across various domains.
Innovation Solution
A data-based approach that extracts and integrates metadata from past execution experiences into ontologies, enhancing planner performance by utilizing metadata extractors, macros, goal ordering, and heuristic structures to improve planning robustness and interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional planning systems use fixed metadata (preconditions, effects), then the system structure is simple and easy to implement, but the system cannot handle complex scenarios involving uncertainty, partial observability, and dynamic environments
Solution Approach 1:
The patent transforms fixed, static metadata into dynamic, learnable representations. The system continuously updates metadata from execution experiences, allowing the planner to adapt to new scenarios and environments dynamically rather than relying on pre-defined fixed structures.
Solution Approach 2:
The patent changes the parameters of metadata from fixed values to learnable, evolving parameters. By extracting metadata from past execution experiences and integrating them into the planning system, the parameters become adaptable to different contexts while maintaining a structured framework.
2Adaptability or versatility
If traditional planning systems rely on fixed metadata, then the implementation is straightforward, but the system lacks the ability to generalize across various domains
Solution Approach 1:
The patent implements a feedback loop where execution experiences are continuously monitored, analyzed, and used to update metadata. This feedback mechanism enables the system to learn from past performances and improve its generalization capability across different domains while retaining essential domain-specific knowledge.
Solution Approach 2:
The patent performs preliminary extraction and organization of metadata from execution experiences before they are needed for planning. By pre-processing and structuring this knowledge in advance, the system can quickly generalize to new domains without losing important domain-specific information during the planning process.
3Reliability
If planners extract and integrate metadata from past execution experiences, then planning robustness and interpretability improve, but the data processing and ontology integration complexity increases
Solution Approach 1:
The patent segments the complex task of metadata integration into distinct modules: metadata extraction from execution experiences, ontology representation, and planner configuration. This segmentation allows each component to be processed independently, improving robustness while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary ontology layer that bridges raw execution experiences and planner configurations. This intermediary structure standardizes and organizes extracted metadata, making the integration process more reliable while reducing the overall complexity of data processing through a unified representation framework.
Data Source
AI summary
Described herein are systems and methods for a data-based automatic planning approach that extracts and integrates various metadata from past execution experiences that are learnt and organized into ontologies wherein this metadata can then be used by planners to improve performance by providing a more robust and interpretable solution for automated planning, enabling planners to handle complex scenarios and achieve generalized planning.