Department of Computer, Control and Management Engineering

TITLE: Automated Planning

INSTRUCTOR: Hector Geffner (DTIC, Universitat Pompeu Fabra, Barcelona, Spain)

Planning is the model-based approach to autonomous behavior in AI. Given a compact
description of a model representing the actions, the sensors, and goals of an agent,
a planner must automatically produce the control for driving the agent to its goal.
The approach contrasts with model-free approaches, where the agent behavior results
from learning, or programming-based approaches, where the agent behavior is hardwired.

In this course, we will look at the variety of models used in AI planning for representing
actions, sensors, and goals, and the techniques that have been developed for solving these
models. In the simplest of these models, actions have deterministic effects and the initial
state of the environment is known (classical planning); in the most complex ones, actions have
stochastic effects and sensing is partial and noisy (POMDP planning). All the models are intractable
in the worst case, and hence, the key challenge in planning is computational: how to scale up to
solve large models. This is a research oriented course, where we will review the state of the art in AI planning,
what has been accomplished so far, and what problems are still open.

LOGISTICS:
The course will consist of around 20 hours (Category B: Internal PhD courses) to be held at DIS,
in Via Ariosto 25 with the following calendar:

Mon. 19 July, 10.00 -- 13.00, room A4 (aula A4)
Tue. 20 July, 10.00 -- 13.00, room A4 (aula A4)
Wed. 21 July, 10.00 -- 13.00, room A4 (aula A4)

Mon. 26 July, 10.00 -- 13.00, room A4 (aula A4)
Tue. 27 July, 10.00 -- 13.00, room A4 (aula A4)
Wed. 28 July, 10.00 -- 13.00, room A4 (aula A4)