[PlanetKR] Fully funded PhD positions on Knowledge Representation for Learning and Uncertainty (University of Edinburgh)

[Vaishak Belle]

Fully funded PhD positions on Knowledge Representation for Learning and
Uncertainty (University of Edinburgh)

Short link: http://bit.ly/2f4tYS5

Application deadline: 9 December 2016 (**see below for more information**)

The overall goal of these projects is to develop new methods and formal
languages that can effectively bridge the areas of knowledge
representation, probabilistic reasoning and machine learning. Formal
languages and symbolic techniques have a long and distinguished history in
AI, and have widely impacted many scientific and commercial endeavors in
diverse areas such as verification, robotics, planning, logistics and
human-level commonsense reasoning. However, many of the applications in
these areas often need to handle inherent uncertainty, complemented by an
increased prominence of data-oriented algorithms and statistical
techniques. From a foundational perspective, the question of how knowledge
representation languages need to be augmented to handle these complex
notions of uncertainty is an open and challenging one. From a practical
perspective, enriching existing machine learning algorithms by
human-readable representations and background knowledge can be very useful.

Sample PhD projects include:
- First-order logic has enormous expressive power to represent things like
objects, relations, dependencies, hierarchies and temporal assertions.
Advances in robotics and machine learning, in contrast, learn features of
the world using probabilistic graphical models. An exciting new trend in AI
is to investigate languages (e.g., relational graphical models) and methods
(e.g., model counting) that combine the best of both worlds. In this
context, motivated by vision and language models, where a system encounters
new unknown objects on the fly and is plagued by numerical and qualitative
uncertainty, the research project aims to push the representational
expressiveness and algorithms of existing models to handle unbounded
domains, identity uncertainty, and so on.

- Research in first-order logic for dynamical systems has to led to a large
body of work on high-level programming languages. Building on an ontology
of physical and sensing actions, these languages are interpreted with
respect to a first-order logical database. To be able to apply these
languages to robots, where sensors are typically noisy, the natural
question then is: how can the characterization of the actions and
background knowledge incorporate stochastic uncertainty? What is the
relation between such enriched languages and probabilistic programming, and
probabilistic computation, more generally? This line of work can be seen to
contribute to verifiable behaviors for robotics.

- Automated planning is a major endeavor in AI, where we seek to synthesize
a sequence of actions to enable goal conditions. A recent effort in
automated planning considers the synthesis of plans with rich control
structures such as loops and branches. To be able to apply these languages
to robots, as above, what are the algorithms needed to reason about
stochastic uncertainty while synthesizing such plans?

- A number of more specialized topics on the investigation of symbolic
techniques for machine learning and numerical optimization, and the
application of state-of-the-art constraint solving technology for
stochastic uncertainty, are also possible.

These positions are an opportunity to combine cutting edge research at the
intersection of knowledge representation and machine learning.
We envision the application of these methods to challenging problems
arising in logistics, planning, robotics and commonsense reasoning.

Background Required
The project is suitable for a student with a top MSc or first-class
bachelor's degree in computer science, mathematical logic, statistics,
physics, or a related numerate discipline.

Previous coursework or experience in machine learning and mathematical
logic/knowledge representation is desirable, although we do not expect
students to have both of these.

We envision the development of new software tools that demonstrate the
languages and methods involved, and the application of these methods to
challenging problems arising in logistics, planning, robotics and/or
commonsense reasoning. Therefore, a programming background is desirable.

Why Edinburgh
The School of Informatics at the University of Edinburgh has one of the
largest concentrations of computer science research in Europe, with over
100 faculty members and 275 PhD students. The school is particularly strong
in the research area of artificial intelligence. Our strength in these
areas have been recognized by award of EPSRC Centre for Doctoral Training
in Data Science. The University of Edinburgh is one of the founding
partners of the Alan Turing Institute, the UK's national research institute
for data science.

Funding Information
The scholarship consists of an annual bursary up to a maximum of three
years. Overseas applicants are advised to apply before the standard
informatics deadlines and apply for other scholarships. See
http://www.ed.ac.uk/schools-departments/informatics/postgraduate/fees  and
http://www.ed.ac.uk/informatics/postgraduate/apply/key-dates.

Applicants can also consider applying for a combined MSc + PhD programme in
our centre for doctoral training in Data Science and/or Robotics
and Autonomous Systems; see http://datascience.inf.ed.ac.uk and
http://www.edinburgh-robotics.org

Application Information
For informal enquiries about the positions, please contact Vaishak Belle <
vaishak at ed.ac.uk>. Formal application must be through the School's normal
PhD application process:
http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=494

For more information on CISA, see http://web.inf.ed.ac.uk/cisa/study-with-us

For full consideration, please apply by Dec 9, 2016.

Best,
Vaishak
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