Cognitive Dialog System Multi-Domain Problem Solving
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Solution Overview
Problem
Current dialog systems are unable to provide effective problem-solving capabilities across multiple domains, limiting their ability to assist users in a wide range of problems across various knowledge domains such as calendar management, trip planning, shopping, and educational domains.
Innovation Solution
A cognitive dialog system that uses machine learning and natural language processing to identify user problems, generate problem instances, and invoke solvers to provide solutions, with the ability to recognize domains and learn from interactions, enabling modular expansion into new domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current dialog systems are used, then system simplicity is maintained, but problem-solving capability across multiple domains is limited
Solution Approach 1:
The dialog system is segmented into distinct modular components: domain identification module, problem instance generator, solver selector, and solution provider. Each module handles specific tasks independently, allowing the system to achieve multi-domain problem-solving capability while maintaining manageable complexity through clear separation of concerns.
Solution Approach 2:
The dialog system implements a universal problem-solving framework that can handle multiple domains (calendar management, trip planning, shopping, educational domains) through a single integrated architecture. The system uses domain-agnostic problem instance templates and solver selection mechanisms that work across different problem types, enabling one system to serve multiple functions.
2Measurement precision
If domain-specific systems are created for each problem type, then problem-solving accuracy is improved, but system complexity and integration difficulty increase
Solution Approach 1:
The system merges multiple domain-specific problem-solving capabilities into a single unified dialog system. By combining domain identification, problem instance generation, solver selection, and solution provision into one integrated framework, the system achieves both high accuracy (through specialized solvers for each domain) and low integration complexity (through a standardized interface layer).
Solution Approach 2:
The problem instance template serves as an intermediary layer between domain-specific solvers and the universal dialog interface. This mediator standardizes how different domains represent their problems, allowing accurate domain-specific processing while maintaining a consistent user-facing interface and simplifying system integration.
3Adaptability or versatility
If the system covers multiple domains, then user assistance scope is expanded, but system reliability and maintenance difficulty worsen
Solution Approach 1:
The system segments domain knowledge into independent, interchangeable modules. Each domain (calendar, travel, shopping, education) is handled by dedicated solvers that can be maintained, updated, or replaced independently without affecting other domains. This segmentation improves reliability by isolating potential failure points and simplifying maintenance through modular updates.
Data Source
AI summary
Various embodiments are provided for integrating multiple domain learning and personalization in a dialog system for a user in a computing environment by a processor. One or more problem instances may be defined for multiple domains according to a problem instance template, identified user intent, links to one or more problem solvers associated with the multiple domains, or a combination thereof. A dialog plan may be determined to further define the one or more problem instances in response to user input. A solution may be provided to the user for the one or more problem instances.