2 research outputs found
Event Model Learning from Complex Videos using ILP
Learning event models from videos has applications ranging from abnormal event detection to content based video retrieval. Relational learning techniques such as Inductive Logic Programming (ILP) hold promise for building such models, but have not been successfully applied to the very large datasets which result from video data. In this paper we present a novel supervised learning framework to learn event models from large video datasets (~2.5 million frames) using ILP. Efficiency is achieved via the learning from interpretations setting and using a typing system. This allows learning to take place in a reasonable time frame with reduced false positives. The experimental results on video data from an airport apron where events such as Loading, Unloading, Jet-Bridge Parking etc are learned suggests that the techniques are suitable to real world scenarios
Physical Reasoning for Intelligent Agent in Simulated Environments
Developing Artificial Intelligence (AI) that is capable of
understanding and interacting with the real world in a
sophisticated way has long been a grand vision of AI. There is an
increasing number of AI agents coming into our daily lives and
assisting us with various daily tasks ranging from house cleaning
to serving food in restaurants. While different tasks have
different goals, the domains of the tasks all obey the physical
rules (classic Newtonian physics) of the real world. To
successfully interact with the physical world, an agent needs to
be able to understand its surrounding environment, to predict the
consequences of its actions and to draw plans that can achieve a
goal without causing any unintended outcomes. Much of AI
research over the past decades has been dedicated to specific
sub-problems such as machine learning and computer vision, etc.
Simply plugging in techniques from these subfields is far from
creating a comprehensive AI agent that can work well in a
physical environment. Instead, it requires an integration of
methods from different AI areas that considers specific
conditions and requirements of the physical environment.
In this thesis, we identified several capabilities that are
essential for AI to interact with the physical world, namely,
visual perception, object detection, object tracking, action
selection, and structure planning. As the real world is a highly
complex environment, we started with developing these
capabilities in virtual environments with realistic physics
simulations. The central part of our methods is the combination
of qualitative reasoning and standard techniques from different
AI areas. For the visual perception capability, we developed a
method that can infer spatial properties of rectangular objects
from their minimum bounding rectangles. For the object detection
capability, we developed a method that can detect unknown objects
in a structure by reasoning about the stability of the structure.
For the object tracking capability, we developed a method that
can match perceptually indistinguishable objects in visual
observations made before and after a physical impact. This method
can identify spatial changes of objects in the physical event,
and the result of matching can be used for learning the
consequence of the impact. For the action selection capability,
we developed a method that solves a hole-in-one problem that
requires selecting an action out of an infinite number of actions
with unknown consequences. For the structure planning capability,
we developed a method that can arrange objects to form a stable
and robust structure by reasoning about structural stability and
robustness