167,073 research outputs found
Uncertainty Management and Evidential Reasoning with Structured Knowledge
This research addresses two intensive computational problems of reasoning under uncertainty in artificial intelligence. The first problem is to study the strategy for belief propagation over networks. The second problem is to explore properties of operations which construe the behaviour of those factors in the networks. In the study of operations for computing belief combination over a network model, the computational characteristics of operations are modelled by a set of axioms which are in conformity with human inductive and deductive reasoning. According to different topological connection of networks, we investigate four types of operations. These operations successfully present desirable results in the face of dependent, less informative, and conflicting evidences. As the connections in networks are complex, there exists a number of possible ways for belief propagation. An efficient graph decomposition technique has been used which converts the complicated networks into simply connected ones. This strategy integrates the logic and probabilistic aspects inference, and by using the four types of operations for its computation it gains the advantage of better description of results (interval-valued representation) and less information needed. The performance of this proposed techniques can be seen in the example for assessing civil engineering structure damage and results are in tune with intuition of practicing civil engineers
Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks
This paper presents to the best of our knowledge the first end-to-end object
tracking approach which directly maps from raw sensor input to object tracks in
sensor space without requiring any feature engineering or system identification
in the form of plant or sensor models. Specifically, our system accepts a
stream of raw sensor data at one end and, in real-time, produces an estimate of
the entire environment state at the output including even occluded objects. We
achieve this by framing the problem as a deep learning task and exploit
sequence models in the form of recurrent neural networks to learn a mapping
from sensor measurements to object tracks. In particular, we propose a learning
method based on a form of input dropout which allows learning in an
unsupervised manner, only based on raw, occluded sensor data without access to
ground-truth annotations. We demonstrate our approach using a synthetic dataset
designed to mimic the task of tracking objects in 2D laser data -- as commonly
encountered in robotics applications -- and show that it learns to track many
dynamic objects despite occlusions and the presence of sensor noise.Comment: Published in The Thirtieth AAAI Conference on Artificial Intelligence
(AAAI-16), Video: https://youtu.be/cdeWCpfUGWc, Code:
http://mrg.robots.ox.ac.uk/mrg_people/peter-ondruska
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