Learning from Depth Sensor Data using Inductive Logic Programming
| Autor: | Petar Vračar, Josip Musić, Miha Drole, Ante Panjkota, Matjaz Kukar, Ivo Stančić, Igor Kononenko |
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| Přispěvatelé: | Ribić, Samir, Zajko, Ernedin, Sadžak, Aida |
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Computer science
business.industry Supervised learning Statistical relational learning Multi-task learning Machine learning computer.software_genre Robot learning Inductive programming supervised learning context awareness assistive devices knowlegde discovery Inductive transfer Inductive logic programming Artificial intelligence business computer Logic programming |
| Zdroj: | ICAT |
| Popis: | The problem of detecting objects and their movements in sensor data is of crucial importance in providing safe navigation through both indoor and outdoor environments for the visually impaired. In our setting we use depth- sensor data obtained from a simulator and use inductive logic programming (ILP), a subfield of machine learning that deals with learning concept descriptions, to learn how to detect borders, find the border that is nearest to some point of interest, and border correspondence through time. We demonstrate how ILP can be used to tackle this problem in an incremental manner by using previously learned predicates to construct more complex ones. The learned concept descriptions show high (> 90%) accuracy and their natural language interpretation closely matches an intuitive understanding of their meaning. |
| Databáze: | OpenAIRE |
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