Symbolic Domain Model Generation from Multimodal Data
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Solution Overview
Problem
Manual generation of symbolic models in AI planning is labor-intensive and time-consuming, requiring significant expertise and often limited by the availability of reference resources that may not cover all aspects of a task or provide detailed sequences of events.
Innovation Solution
A computer-implemented method that generates formal planning domain descriptions by extracting domain actions and attributes from text-based and audio-visual inputs, using semantic parsing and deep learning to infer missing information, and constructing finite state machines to create symbolic models in a formal planning language like PDDL, allowing for user interaction and model adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual generation of symbolic models is performed by software developers, then model accuracy and completeness can be ensured, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The system enables automatic generation of symbolic domain models by processing natural language descriptions and audio-visual content through semantic parsing and deep learning algorithms. The computer system autonomously extracts domain actions, attributes, and preconditions without requiring manual software developer intervention, while still producing accurate models through multiple validation mechanisms including user feedback loops.
Solution Approach 2:
The patent introduces an intermediate processing layer that transforms unstructured natural language and audio-visual data into structured symbolic models. This intermediary system uses semantic parsing modules and deep learning algorithms to bridge the gap between raw input data and formal planning domain descriptions, reducing both manual effort and model inaccuracies.
2Loss of information
If manual model generation is performed with reference to available databases, then some domain information can be obtained, but the process remains time-consuming and expertise-dependent
Solution Approach 1:
The system performs preliminary extraction of domain actions, attributes, and preconditions from natural language descriptions and audio-visual content before formal model construction. This preliminary processing prepares structured data that can be quickly transformed into symbolic models, reducing both the time required and the need for extensive manual research into domain databases.
Solution Approach 2:
The patent incorporates multiple input dimensions including natural language text, audio content, and visual elements to comprehensively capture domain information. This multi-dimensional approach ensures complete domain information extraction without relying solely on traditional text-based databases, significantly reducing information loss while accelerating the process.
3Adaptability or versatility
If detailed sequences of events are provided in reference resources, then comprehensive domain models can be created, but such detailed resources are often unavailable for many tasks
Solution Approach 1:
The patent replaces manual mechanical processes of model creation with automated computational systems. Deep learning algorithms and semantic parsing automatically infer domain actions, attributes, and preconditions from limited input data, eliminating the need for detailed reference resources while maintaining model completeness and reducing creation difficulty.
Solution Approach 2:
The system changes the parameters of information extraction by using advanced natural language processing and audio-visual analysis to derive implicit domain knowledge from limited explicit information. This parameter change enables the system to generate comprehensive models even when detailed sequences of events are not provided in reference resources.
Data Source
AI summary
A computer generates a formal planning domain description. The computer receives a first text-based description of a domain in an AI environment. The domain includes an action and an associated attribute, and the description is written in natural language. The computer receives the first text-based description of the domain and extracts a first set of domain actions and associated action attributes. The computer receives audio-visual elements depicting the domain, generates a second text-based description, and extracts a second set of domain actions and associated action attributes. The computer constructs finite state machines corresponding to the extracted actions and attributes. The computer converts the FSMs into a symbolic model, written in a formal planning language, that describes the domain.