Temporal Decision Trees: Model-based Diagnosis of Dynamic Systems On-Board

TL;DR

This paper introduces temporal decision trees that incorporate time-dependent information for model-based diagnosis of dynamic systems, enabling real-time decision-making in embedded applications.

Contribution

It extends existing decision tree methods to handle temporal data and provides an algorithm for compiling these trees from model-based reasoning systems.

Findings

Temporal decision trees improve diagnosis accuracy over static models

The proposed algorithm efficiently compiles trees from models

Application to dynamic systems demonstrates practical effectiveness

Abstract

The automatic generation of decision trees based on off-line reasoning on models of a domain is a reasonable compromise between the advantages of using a model-based approach in technical domains and the constraints imposed by embedded applications. In this paper we extend the approach to deal with temporal information. We introduce a notion of temporal decision tree, which is designed to make use of relevant information as long as it is acquired, and we present an algorithm for compiling such trees from a model-based reasoning system.