17548672. KNOWLEDGE AUGMENTED SEQUENTIAL DECISION-MAKING UNDER UNCERTAINTY simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)
Contents
KNOWLEDGE AUGMENTED SEQUENTIAL DECISION-MAKING UNDER UNCERTAINTY
Organization Name
INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor(s)
KNOWLEDGE AUGMENTED SEQUENTIAL DECISION-MAKING UNDER UNCERTAINTY - A simplified explanation of the abstract
This abstract first appeared for US patent application 17548672 titled 'KNOWLEDGE AUGMENTED SEQUENTIAL DECISION-MAKING UNDER UNCERTAINTY
Simplified Explanation
The patent application describes a system and method for outputting an optimal decision policy based on informal knowledge input. Here are the key points:
- The system includes a memory and a processor that execute computer executable components.
- The analysis component analyzes an input dataset that contains a constraint expressed in natural language.
- The augmentation component generates an influence mapping based on the constraint input, which includes a constraint variable.
- The input dataset used for the influence mapping includes time-stamped tuple data with state, action, and reward information.
- An inference engine generates an output policy based on the constraint input and constraint variable.
Potential applications of this technology:
- Decision-making systems: This technology can be applied in various decision-making systems, such as recommendation systems, autonomous vehicles, and financial trading systems.
- Natural language processing: The system can analyze and interpret natural language constraints, allowing for more intuitive and user-friendly interactions with computer systems.
- Data analysis: The input dataset and influence mapping can be used to analyze and optimize decision-making processes based on historical data.
Problems solved by this technology:
- Complex decision-making: The system helps in solving complex decision-making problems by providing an optimal decision policy based on informal knowledge input.
- Natural language understanding: By analyzing natural language constraints, the system improves the understanding and interpretation of user inputs.
- Data-driven decision-making: The input dataset and influence mapping enable data-driven decision-making by utilizing historical data to generate optimal policies.
Benefits of this technology:
- Improved decision-making: The system outputs an optimal decision policy, leading to improved decision-making processes.
- Enhanced user experience: By understanding natural language constraints, the system provides a more intuitive and user-friendly interaction.
- Data-driven insights: The analysis of the input dataset and influence mapping provides valuable insights into the decision-making process based on historical data.
Original Abstract Submitted
One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to outputting an optimal decision policy base on informal knowledge input. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise an analysis component that analyzes an input dataset comprising a constraint in a natural language form, and an augmentation component that generates an influence mapping comprising a constraint variable based on the constraint input. In an embodiment, an input dataset employed to support the influence mapping can comprise time-stamped tuple data comprising a state, an action and a reward. In an embodiment, an inference engine can generate an output policy in response to the constraint input and which output policy can be based on the constraint input and constraint variable.
