278 research outputs found
Learning Bayesian network equivalence classes using ant colony optimisation
Bayesian networks have become an indispensable tool in the modelling of uncertain
knowledge. Conceptually, they consist of two parts: a directed acyclic graph called the
structure, and conditional probability distributions attached to each node known as the
parameters. As a result of their expressiveness, understandability and rigorous mathematical basis, Bayesian networks have become one of the first methods investigated,
when faced with an uncertain problem domain. However, a recurring problem persists
in specifying a Bayesian network. Both the structure and parameters can be difficult for
experts to conceive, especially if their knowledge is tacit.To counteract these problems, research has been ongoing, on learning both the structure
and parameters of Bayesian networks from data. Whilst there are simple methods for
learning the parameters, learning the structure has proved harder. Part ofthis stems from
the NP-hardness of the problem and the super-exponential space of possible structures.
To help solve this task, this thesis seeks to employ a relatively new technique, that has
had much success in tackling NP-hard problems. This technique is called ant colony
optimisation. Ant colony optimisation is a metaheuristic based on the behaviour of ants
acting together in a colony. It uses the stochastic activity of artificial ants to find good
solutions to combinatorial optimisation problems. In the current work, this method is
applied to the problem of searching through the space of equivalence classes of Bayesian
networks, in order to find a good match against a set of data. The system uses operators
that evaluate potential modifications to a current state. Each of the modifications is
scored and the results used to inform the search. In order to facilitate these steps, other
techniques are also devised, to speed up the learning process. The techniques includeThe techniques are tested by sampling data from gold standard networks and learning
structures from this sampled data. These structures are analysed using various goodnessof-fit measures to see how well the algorithms perform. The measures include structural
similarity metrics and Bayesian scoring metrics. The results are compared in depth
against systems that also use ant colony optimisation and other methods, including
evolutionary programming and greedy heuristics. Also, comparisons are made to well
known state-of-the-art algorithms and a study performed on a real-life data set. The
results show favourable performance compared to the other methods and on modelling
the real-life data
Road Planning for Slums via Deep Reinforcement Learning
Millions of slum dwellers suffer from poor accessibility to urban services
due to inadequate road infrastructure within slums, and road planning for slums
is critical to the sustainable development of cities. Existing re-blocking or
heuristic methods are either time-consuming which cannot generalize to
different slums, or yield sub-optimal road plans in terms of accessibility and
construction costs. In this paper, we present a deep reinforcement learning
based approach to automatically layout roads for slums. We propose a generic
graph model to capture the topological structure of a slum, and devise a novel
graph neural network to select locations for the planned roads. Through masked
policy optimization, our model can generate road plans that connect places in a
slum at minimal construction costs. Extensive experiments on real-world slums
in different countries verify the effectiveness of our model, which can
significantly improve accessibility by 14.3% against existing baseline methods.
Further investigations on transferring across different tasks demonstrate that
our model can master road planning skills in simple scenarios and adapt them to
much more complicated ones, indicating the potential of applying our model in
real-world slum upgrading. The code and data are available at
https://github.com/tsinghua-fib-lab/road-planning-for-slums.Comment: KDD'2
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