# Featured Weighted Automata

## TL;DR

This paper introduces featured weighted automata, combining feature-annotated transition systems with weighted automata, enabling the analysis of quantitative properties across all feature configurations simultaneously.

## Contribution

It extends weighted automata to include feature annotations, providing algorithms to compute properties for all feature sets at once, with applications to reachability and energy analysis.

## Key findings

- Algorithms for featured weighted automata are developed.
- Methods extend weighted automata techniques to feature-rich models.
- Applications include minimum reachability and energy property analysis.

## Abstract

A featured transition system is a transition system in which the transitions are annotated with feature expressions: Boolean expressions on a finite number of given features. Depending on its feature expression, each individual transition can be enabled when some features are present, and disabled for other sets of features. The behavior of a featured transition system hence depends on a given set of features. There are algorithms for featured transition systems which can check their properties for all sets of features at once, for example for LTL or CTL properties. Here we introduce a model of featured weighted automata which combines featured transition systems and (semiring-) weighted automata. We show that methods and techniques from weighted automata extend to featured weighted automata and devise algorithms to compute quantitative properties of featured weighted automata for all sets of features at once. We show applications to minimum reachability and to energy properties.