An Example of the SAM+ Algorithm for Learning Action Models for Stochastic Worlds

TL;DR

This paper demonstrates the application of the SAM+ algorithm to learn stochastic action models in a simplified Coffee planning domain, illustrating its process and results.

Contribution

It provides a detailed example of implementing the SAM+ algorithm for learning stochastic action models in a specific domain.

Findings

Successfully learned action models for the simplified Coffee domain

Illustrated the step-by-step process of running SAM+

Provided insights into the algorithm's effectiveness in stochastic settings

Abstract

In this technical report, we provide a complete example of running the SAM+ algorithm, an algorithm for learning stochastic planning action models, on a simplified PPDDL version of the Coffee problem. We provide a very brief description of the SAM+ algorithm and detailed description of our simplified version of the Coffee domain, and then describe the results of running it on the simplified Coffee domain.