A Generalizable Knowledge Framework for Semantic Indoor Mapping Based on
Markov Logic Networks and Data Driven MCMC
- URL: http://arxiv.org/abs/2002.08402v1
- Date: Wed, 19 Feb 2020 19:30:10 GMT
- Title: A Generalizable Knowledge Framework for Semantic Indoor Mapping Based on
Markov Logic Networks and Data Driven MCMC
- Authors: Ziyuan Liu, Georg von Wichert
- Abstract summary: We propose a generalizable knowledge framework for data abstraction.
Based on these abstract terms, intelligent autonomous systems should be able to make inference according to specific knowledge base.
We show in detail how to adapt this framework to a certain task, in particular, semantic robot mapping.
- Score: 2.4214518935746185
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we propose a generalizable knowledge framework for data
abstraction, i.e. finding compact abstract model for input data using
predefined abstract terms. Based on these abstract terms, intelligent
autonomous systems, such as a robot, should be able to make inference according
to specific knowledge base, so that they can better handle the complexity and
uncertainty of the real world. We propose to realize this framework by
combining Markov logic networks (MLNs) and data driven MCMC sampling, because
the former are a powerful tool for modelling uncertain knowledge and the latter
provides an efficient way to draw samples from unknown complex distributions.
Furthermore, we show in detail how to adapt this framework to a certain task,
in particular, semantic robot mapping. Based on MLNs, we formulate
task-specific context knowledge as descriptive soft rules. Experiments on real
world data and simulated data confirm the usefulness of our framework.
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