Coupling Machine Learning with Ontology for Robotics Applications

TL;DR

This paper proposes a practical method for integrating machine learning algorithms with ontology-based knowledge bases to enhance autonomous systems' risk-awareness, demonstrating linear time complexity in experiments.

Contribution

It introduces a novel two-tier architecture coupling ML with ontologies, enabling knowledge extraction when prior knowledge is unavailable in dynamic scenarios.

Findings

The two-tier approach is computationally valid.

Time complexity is linear with data and knowledge size.

Experiments confirm improved risk-awareness in autonomous systems.

Abstract

In this paper I present a practical approach for coupling machine learning (ML) algorithms with knowledge bases (KB) ontology formalism. The lack of availability of prior knowledge in dynamic scenarios is without doubt a major barrier for scalable machine intelligence. My view of the interaction between the two tiers intelligence is based on the idea that when knowledge is not readily available at the knowledge base tier, more knowledge can be extracted from the other tier, which has access to trained models from machine learning algorithms. To analyse this hypothesis, I create two experiments based on different datasets, which are related directly to risk-awareness of autonomous systems, analysed by different machine learning algorithms (namely; multi-layer feedforward backpropagation, Naive Bayes, and J48 decision tree). My analysis shows that the two-tiers intelligence approach for coupling ML and KB is computationally valid and the time complexity of the algorithms during the robot mission is linear with the size of the data and knowledge.