Coupling Machine Learning with Ontology for Robotics Applications
This addresses the challenge of prior knowledge availability in dynamic scenarios for autonomous systems, but it appears incremental as it builds on existing methods without major breakthroughs.
The paper tackles the problem of integrating machine learning with knowledge bases for robotics by proposing a two-tier intelligence approach, showing that it is computationally valid with linear time complexity relative to data and knowledge size.
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.