Abstract:

"Filtering" denotes any method whereby an agent updates its belief
state -- its knowledge of the state of the world -- from a sequence of
actions and observations.  In "logical filtering", the belief state is
a logical formula describing possible world states and the agent has a
(possibly nondeterministic) logical model of its environment and
sensors.

In this talk I will present efficient logical filtering algorithms
that maintain a compact belief state representation indefinitely, for
a broad range of environment classes. These include nondeterministic,
partially observable STRIPS environments and environments in which
actions permute the state space. Efficient filtering is also
possible when the belief state is represented using prime implicates,
or when it is approximated by a logically weaker formula.
I will discuss applications of these algorithms to agents acting in
virtual worlds and to learning partially observable world models.

Joint work with Stuart Russell.