18066270. ENABLING CLASSICAL PLANNING IN INTERACTIVE ENVIRONMENTS WITH INCOMPLETE MODELS simplified abstract (International Business Machines Corporation)

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ENABLING CLASSICAL PLANNING IN INTERACTIVE ENVIRONMENTS WITH INCOMPLETE MODELS

Organization Name

International Business Machines Corporation

Inventor(s)

Don Joven Ravoy Agravante of Tokyo (JP)

Michiaki Tatsubori of Tokyo (JP)

ENABLING CLASSICAL PLANNING IN INTERACTIVE ENVIRONMENTS WITH INCOMPLETE MODELS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18066270 titled 'ENABLING CLASSICAL PLANNING IN INTERACTIVE ENVIRONMENTS WITH INCOMPLETE MODELS

Simplified Explanation:

The patent application describes a method in a model-based reinforcement learning system where an agent can switch between a probabilistic planning mode and an information gathering mode. In the probabilistic planning mode, the agent computes a predictive state representation to score action candidates based on resolved plans to achieve a goal state. In the information gathering mode, the agent scores action candidates based on the expected value of information to be gathered.

  • The method involves switching an agent between a probabilistic planning mode and an information gathering mode.
  • In the probabilistic planning mode, the agent computes a predictive state representation to score action candidates based on resolved plans to achieve a goal state.
  • In the information gathering mode, the agent scores action candidates based on the expected value of information to be gathered.

Key Features and Innovation:

  • Switching between probabilistic planning and information gathering modes.
  • Computing predictive state representation for scoring action candidates.
  • Scoring based on resolved plans and expected value of information.

Potential Applications:

  • Robotics for decision-making processes.
  • Autonomous vehicles for adaptive planning.
  • Game AI for strategic decision-making.

Problems Solved:

  • Enhancing decision-making in dynamic environments.
  • Improving goal achievement through planning.
  • Optimizing information gathering for better outcomes.

Benefits:

  • Increased efficiency in decision-making.
  • Enhanced goal achievement.
  • Adaptive and flexible agent behavior.

Commercial Applications:

The technology can be applied in various industries such as autonomous vehicles, robotics, and gaming for improved decision-making processes and goal achievement.

Questions about Model-Based Reinforcement Learning:

1. How does the agent switch between the probabilistic planning and information gathering modes? 2. What are the potential limitations of this model-based reinforcement learning system?


Original Abstract Submitted

A computer-implemented method in a model-based reinforcement learning (RL) system with logic states includes switching an agent between a first mode and a second mode, the first mode being a probabilistic planning mode and the second mode being an information gathering mode. In response to the agent being in the probabilistic planning mode, the agent computes a predictive state representation, given a history of observations and actions taken, and the agent scores action candidates based on planning with the predictive state representation so that actions with resolved plans with confidence to achieve a goal state are scored higher than actions without resolved plans. In response to the agent being in the information gathering mode, the agent scores action candidates based on a Q function of a value of expected information to be gathered from a given pair of state and action.

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