TY - GEN
T1 - Combining knowledge and reasoning through probabilistic soft logic for image puzzle solving
AU - Aditya, Somak
AU - Yang, Yezhou
AU - Baral, Chitta
AU - Aloimonos, Yiannis
N1 - Funding Information:
The support of the National Science Foundation under the Robust Intelligence Program (1816039 and 1750082) is gratefully acknowledged.
Publisher Copyright:
© 2018 by Association For Uncertainty in Artificial Intelligence (AUAI) All rights reserved.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - The uncertainty associated with human perception is often reduced by one's extensive prior experience and knowledge. Current datasets and systems do not emphasize the necessity and benefit of using such knowledge. In this work, we propose the task of solving a genre of image-puzzles ("image riddles") that require both capabilities involving visual detection (including object, activity recognition) and, knowledge-based or commonsense reasoning. Each puzzle involves a set of images and the question "what word connects these images?". We compile a dataset of over 3k riddles where each riddle consists of 4 images and a groundtruth answer. The annotations are validated using crowd-sourced evaluation. We also define an automatic evaluation metric to track future progress. Our task bears similarity with the commonly known IQ tasks such as analogy solving, sequence filling that are often used to test intelligence. We develop a Probabilistic Reasoning-based approach that utilizes commonsense knowledge about words and phrases to answer these riddles with a reasonable accuracy. Our approach achieves some promising results for these riddles and provides a strong baseline for future attempts. We make the entire dataset and related materials publicly available to the community (bit.ly/22f9Ala).
AB - The uncertainty associated with human perception is often reduced by one's extensive prior experience and knowledge. Current datasets and systems do not emphasize the necessity and benefit of using such knowledge. In this work, we propose the task of solving a genre of image-puzzles ("image riddles") that require both capabilities involving visual detection (including object, activity recognition) and, knowledge-based or commonsense reasoning. Each puzzle involves a set of images and the question "what word connects these images?". We compile a dataset of over 3k riddles where each riddle consists of 4 images and a groundtruth answer. The annotations are validated using crowd-sourced evaluation. We also define an automatic evaluation metric to track future progress. Our task bears similarity with the commonly known IQ tasks such as analogy solving, sequence filling that are often used to test intelligence. We develop a Probabilistic Reasoning-based approach that utilizes commonsense knowledge about words and phrases to answer these riddles with a reasonable accuracy. Our approach achieves some promising results for these riddles and provides a strong baseline for future attempts. We make the entire dataset and related materials publicly available to the community (bit.ly/22f9Ala).
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M3 - Conference contribution
AN - SCOPUS:85059425637
T3 - 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018
SP - 238
EP - 248
BT - 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018
A2 - Globerson, Amir
A2 - Globerson, Amir
A2 - Silva, Ricardo
PB - Association For Uncertainty in Artificial Intelligence (AUAI)
T2 - 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018
Y2 - 6 August 2018 through 10 August 2018
ER -