Faster Decision-Theoretic Planning using Boolean-Constraint Propagation




Decision-theoretic planning is about getting computers to synthesize strategies for acting in an uncertain environment.

State-of-the-art planners and solution algorithms are terribly inefficient, because they do not reason about or learn good "control knowledge". In other words, they are inefficient because: (1) they do not leverage symbolic knowledge provided about their environment, and (2)  because they do not learn from their mistakes

The project will focus on one or more of the following types of control knowledge.

- Landmark detection and/or exploitation

 - Nogood detection and/or exploitation

 - Representation and exploitation of nogoods

Your supervisor has (co-)authored a number of decision-theoretic planning systems, in C++. If you choose to focus on empirical work, you can leverage those code bases if you so choose.





Theoretical: demonstrate exponential separation

Empirical: demonstrate compelling efficiency gains in 1 or more benchmark problems


Advanced knowledge about artificial intelligence planning.


artificial intelligence, decision-theoretic planning, simulation, nogoods, uncertainty

Updated:  10 August 2021/Responsible Officer:  Dean, CECS/Page Contact:  CECS Marketing