Pessimistic Tree Search Cost Distribution for Real-Time Decision Making
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Solution Overview
Problem
Real-time applications of Monte Carlo tree search are hindered by its high computational cost, especially in scenarios where agents must consider dynamic environments and changing behaviors of other agents.
Innovation Solution
A computer-implemented method simulates next states for each possible action of a target object based on a pessimistic scenario generated by sampling costs from a distribution of cost, identifying safety areas, and evaluating actions accordingly, using a hardware processor to efficiently assess potential future positions and ensure safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Monte Carlo tree search is used for sequential decision making, then decision quality is improved by considering dynamic environments, but computational cost increases making real-time application impossible
Solution Approach 1:
The patent pre-generates a distribution of cost values representing possible future states before actual decision-making occurs. This preliminary action stores pre-computed cost information that can be quickly sampled during real-time operations, avoiding the need for full Monte Carlo simulations at decision time.
Solution Approach 2:
The patent creates a simplified copy of the complex environment dynamics through a learned cost distribution model. Instead of simulating full environmental interactions, the system uses sampled cost values from the distribution to approximate future states, dramatically reducing computational requirements while maintaining decision quality.
2Measurement precision
If full Monte Carlo simulation is performed for each possible action, then accuracy of action evaluation is improved, but processing speed decreases preventing real-time operation
Solution Approach 1:
The patent performs partial simulations by sampling only a limited number of cost values from the pre-computed distribution rather than executing full Monte Carlo simulations. This partial action provides sufficient accuracy for real-time decisions without the excessive computational cost of complete simulations.
Solution Approach 2:
The patent changes the parameter representation from full simulation trajectories to sampled cost values from a distribution. By transforming the problem from simulating complete action sequences to sampling scalar cost parameters, the system achieves both accuracy and computational efficiency.
Data Source
AI summary
A computer-implemented method is provided for evaluating a next action of a target object in an environment. The method includes simulating, by a hardware processor for each of possible actions of the target object in the environment, a next state occurring thereafter to obtain a plurality of simulated next states, based on a pessimistic scenario which is randomly generated by sampling a plurality of costs from a distribution of cost. The distribution of cost is an area where the target object is likely to visit in a near future. The method further includes identifying, by the hardware processor, a safety area for the target object in each of the plurality of simulated next states. The method also includes evaluating, by the hardware processor, each of the possible actions of the target object, based on the safety area.