David Poole

Professor
Email: poole [at] cs [dot] ubc [dot] ca
Office: ICCS 109
Phone: 604 822-6254
Lab(s):
LCI

Curriculum Vitae

Ph.D., Australian National Univ. (1984); Post Doctoral Fellow, Univ. of Waterloo (1983-85); Assistant Professor, Univ. of Waterloo (1985-88); Assistant Professor, University of British Columbia (1988-93); Associate Professor, University of British Columbia (1993-98); Professor, University of British Columbia (1998-); Scholar, Canadian Inst. for Advanced Research (1992-95).

Keywords

artificial intelligence
logic
probability
decision theory
decision-making
diagnosis
design
planning
preference elicitation
reasoning under uncertainty
semantic science
statistical relationa AI

Interests

My goal is to understand the principles behind action in natural and artificial agents. I consider the question: what should an agent do based in its preferences (or goals), its background knowledge, (limited) perception of the environment, previous experience and limited computation?

Currently, my main research interests consider theoretical and empirical investigations of automated reasoning built on the foundations of logic, probability and decision theory for tasks such as diagnosis/recognition, design, planning and reacting with partial knowledge and imperfect sensors. I ask what is a natural compact representation and how can aspects of it be exploited computationally.

My main recent work has been in probabilistic reasoning and assumption-based logical reasoning. This includes developing a coherent theory, and empirical studies based on building efficient implementations, and developing methodologies for various applications. Different behaviour can be characterised by who chooses the assumptions: where nature chooses assumptions we have a logic-based characterisation of recognition/diagnosis incorporating probabilistic models such as Bayesian networks; much of the work on non-monotonic (default) reasoning can be seen as assumption-based reasoning where an adversary chooses the assumptions; where an agent can choose the assumptions we have a characterisation of design and planning. Combining these into a coherent framework that will look something like a logic-based decision/game theory with algorithms that exploit properties of various domains is a medium-term goal of my research.

Other interests include: algorithms for probabilistic reasoning, diagnosis and decision theory; preference elicitation; robotics; user modeling; foundations of diagnosis and recognition; planning under uncertainty and learning.

Latest CS Courses

2018 Winter

CPSC 312  –  Functional and Logic Programming
CPSC 522  –  Artificial Intelligence II

2017 Winter

CPSC 312  –  Functional and Logic Programming
CPSC 538G  –  Topics in Computer Systems
CPSC 322  –  Introduction to Artificial Intelligence

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