Título: Short-Sighted Probabilistic Planning

Palestrante: Felipe W. Trevizan


Resumo:
Planning is an essential part of intelligent behavior and a ubiquitous task
for both humans and rational agents. One framework for planning in the presence of
uncertainty is probabilistic planning, in which actions are described by a
probability distribution over their possible outcomes. Probabilistic planning
has been applied to different real-world scenarios such as public health,
sustainability and robotics; however, the usage of probabilistic planning in
practice is limited due to the poor performance of existing planners.

In this talk, I will present a novel approach to effectively solve
probabilistic planning problems by relaxing them into short-sighted problems. A
short-sighted problem is a relaxed problem in which the state space of the original problem
is pruned and artificial goals are added to heuristically estimate the cost of
reaching an original goal from the pruned states. Differently from previously
proposed relaxations, short-sighted problems maintain the original structure
of actions and no restrictions are imposed in the maximum number of actions that
can be executed. Therefore, the solutions for short-sighted problems take into
consideration all the probabilistic outcomes of actions and their
probabilities. I will also present different planning algorithms that use short-sighted
problems in order to solve probabilistic planning problems. These algorithms
iteratively generate and execute optimal policies for short-sighted problems
until a goal state of the original problem is reached.

Felipe W. Trevizan fez seu bacharelado e mestrado em Ciência da Computação no
IME - USP e doutorado na Carnegie Mellon University, departamento de Machine
Learning. Atualmente ele faz pos-doutorado no IME.