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Multiplicity Automata Learning Library (MALL)

We apply a probably approximately correct learning algorithm for multiplicity automata to generate quantitative models of system behaviors with a statistical guarantee. Using the generated model, we give two analysis algorithms to estimate the minimum and average values of system behaviors. We show how to apply the learning algorithm even when the alphabet is not fixed. The experimental result is encouraging; the estimation made by our approach is almost as precise as the exact reference answer obtained by a brute-force enumeration.

For more detail information, please see our thesis "Quantitative Analysis using Multiplicity Automata Learning."

1. Where to Download?
2. How to Install?
3. How to Use?
4. How to Run the Experiment Results?

## Where to Download?

MALL consists of the following 3 folders:

Folder	Description
Src/main	The core library code in Matlab
Src/experiment	The experiment results presented in the thesis.
Src/enumeration	The code to generate the enumeration results.

You can download the latest version of BULL from git:

git clone https://github.com/fmlab-iis/ma-learning.git

## How to Install?

Prerequisites

1. Installed Matlab 2016a with Symbolic Math Toolbox.
2. Perl 5.24.1

Installation

The codes on git are ready to use. You can simply add the path of the directory in the Matlab command window.