SUMMARY:
In this course we will teach the challenges of building autonomous
agents that need to continuously plan, execute their actions, and
learn from their interactions with the environment.
In its essence, the autonomous agents need to "select actions" to
achieve its objectives (and act, and monitor their execution, etc).
Questions of study in AI planning include: How to represent and change
the planning action model? How to generate plans efficiently? How to
produce plans of good quality? How to deal with the uncertainty of
the world? How to dynamically combine planning, scheduling, and
execution?
This course will cover in depth the main issues and algorithms in AI
planning and learning, namely action and task modelling and
representation, plan generation algorithms, heuristic learning and
reuse of experience, and largely open research topics, such as dynamic
integration of planning, scheduling, and execution, and multiagent
planning.
EVALUATION:
This is a lecture course. There is no
textbook, but students will study research papers and use existing
planners. There will be homeworks, programming projects, and a final
exam.
CONTENTS: The course will be divided into the following main parts:
I. Deliberative Planning