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Preparation of DMMTAV and DMDTAV Using DMAV for Environmental Applications: Synthesis, Purification, and Confirmation

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Scenario-Based Verification of Uncertain MDPs.

Murat Cubuktepe¹, Nils Jansen², Sebastian Junges³

¹The University of Texas at Austin, Austin, USA.

Tools and Algorithms for the Construction and Analysis of Systems : 26Th International Conference, TACAS 2020, Held As Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020, Dublin, Ireland, April 25-30,

|August 6, 2020

Summary

This summary is machine-generated.

This study addresses uncertainty in Markov decision processes (MDPs) by using scenario optimization to estimate the probability of satisfying specifications, even with unknown parameter distributions.

Keywords:

MDP Scenario optimisation Uncertainty Verification

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Area of Science:

Artificial Intelligence
Operations Research
Control Theory

Background:

Markov decision processes (MDPs) are widely used for decision-making under uncertainty.
Computing probabilities for temporal logic specifications in MDPs with unknown parameters is generally undecidable.
Existing methods struggle with unknown probability distributions and complex state spaces.

Purpose of the Study:

To develop a method for computing the probability of satisfying temporal logic specifications in MDPs with uncertain parameters.
To address the undecidability of this problem by employing scenario optimization techniques.
To provide a computationally tractable approach for probabilistic verification of MDPs.

Main Methods:

Utilizes scenario optimization based on a finite number of samples of uncertain parameters.
Each sample induces a specific MDP, allowing for problem reformulation.
Solves a finite-dimensional convex optimization problem to estimate the desired probability.

Main Results:

The proposed method provides a high-confidence estimate of the probability of satisfying the specification.
The number of samples required is independent of the number of states and random parameters.
Experiments demonstrate that a few thousand samples yield high-quality confidence bounds.

Conclusions:

Scenario optimization offers a viable approach to probabilistic verification of MDPs with unknown parameters.
The method is computationally efficient and scales well with problem size.
This work advances the ability to analyze complex systems with inherent uncertainties.