IJCAI 2020poster0 citations
ProbAnch: a Modular Probabilistic Anchoring Framework
Andreas Persson, Pedro Zuidberg Dos Martires, Luc de Raedt, Amy Loufti
Abstract
Modeling object representations derived from perceptual observations, in a way that is also semantically meaningful for humans as well as autonomous agents, is a prerequisite for joint human-agent understanding of the world. A practical approach that aims to model such representations is perceptual anchoring, which handles the problem of mapping sub-symbolic sensor data to symbols and maintains these mappings over time. In this paper, we present ProbAnch, a modular data-driven anchoring framework, whose implementation requires a variety of well-orchestrated components, including a probabilistic reasoning system.
Computer Vision: generalUncertainty in AI: general
BibTeX
@inproceedings{ijcai2020p771,
title = {ProbAnch: a Modular Probabilistic Anchoring Framework},
author = {Persson, Andreas and Martires, Pedro Zuidberg Dos and Raedt, Luc de and Loufti, Amy},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Christian Bessiere},
pages = {5285--5287},
year = {2020},
month = {7},
note = {Demos},
doi = {10.24963/ijcai.2020/771},
url = {https://doi.org/10.24963/ijcai.2020/771},
}