High-Level MLN-Based Approach for Spatial Context Disambiguation

In this paper, we propose a probabilistic MLN-based model for spatial context disambiguation. This model serves as a solution for the problem of incomplete knowledge in High-level task planning. By applying the state of the art MLN probabilistic reasoning such as MCSAT, we determine the concept clas...

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Bibliographic Details
Main Authors: Adjali, Omar, Ramdane-Cherif, Amar
Format: Conference Proceeding
Language:English
Subjects:
Context modeling
Object recognition
Probabilistic logic
Robot sensing systems
Semantics
Online Access:Request full text
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Summary:In this paper, we propose a probabilistic MLN-based model for spatial context disambiguation. This model serves as a solution for the problem of incomplete knowledge in High-level task planning. By applying the state of the art MLN probabilistic reasoning such as MCSAT, we determine the concept class of the current spatial context of the robot and contribute by combining semantic spatial relations with observed data at different timesteps. The inherent uncertainty of robot dynamic environments makes the proposed approach suitable to deal with partial observability and sensing limitations of robots. Simulation experiments and evaluation results are presented to validate our model.
ISSN:2577-087X
DOI:10.1109/ICRA.2018.8460923