3 research outputs found
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
Trend-Based Prediction of Spatial Change
The capability to predict changes of spatial regions is important for an intelligent system that interacts with the physical world. For example, in a disaster management scenario, predicting potentially endangered areas and inferring safe zones is essential for planning evacuations and countermeasures. Existing approaches usually predict such spatial changes by simulating the physical world based on specific models. Thus, these simulation-based methods will not be able to provide reliable predictions when the scenario is not similar to any of the models in use or when the input parameters are incomplete. In this paper, we present a prediction approach that overcomes the aforementioned problem by using a more general model and by analysing the trend of the spatial changes. The method is also flexible to adopt to new observations and to adapt its prediction to new situations
Trend-based prediction of spatial change
The capability to predict changes of spatial regions is important for an intelligent system that interacts with the physical world. For example, in a disaster management scenario, predicting potentially endangered areas and inferring safe zones is essential for planning evacuations and countermeasures. Existing approaches usually predict such spatial changes by simulating the physical world based on specific models. Thus, these simulation-based methods will not be able to provide reliable predictions when the scenario is not similar to any of the models in use or when the input parameters are incomplete. In this paper, we present a prediction approach that overcomes the aforementioned problem by using a more general model and by analysing the trend of the spatial changes. The method is also flexible to adopt to new observations and to adapt its prediction to new situations