This course will serve as an introduction to three key foundational problems in AI: planning, search, and reasoning under uncertainty. We will investigate how to define planning domains, including representations for world states and actions, covering both symbolic and path planning. We will study algorithms to efficiently find valid plans with or without optimality, and partially ordered, or fully specified solutions. In transitioning from classical to modern approaches to planning, we will cover decision-making processes and their applications to real-world problems with complex autonomous systems. We will investigate how in planning domains with finite state lengths, solutions can be found efficiently via search. Finally, to effectively plan and act in the real world, we will study how to reason about sensing, actuation, and model uncertainty. Throughout the course, we will relate how classical approaches provided early solutions to these problems, and how modern machine learning builds on, and complements such classical approaches.

Suggested text books:
Artificial Intelligence: A Modern Approach
Planning Algorithms

Links

• Piazza (For Q&A, Announcements): https://piazza.com/utexas/spring2023/cs395t
• Canvas (For report submissions, and grading): https://utexas.instructure.com/courses/1331843

Lectures, Office Hours

Lectures: Tuesdays and Thursdays, 9:30 - 10:50 AM, GDC 2.210

Instructor
Joydeep Biswas, joydeepb@cs.utexas.edu
Office hours: Mondays 9-10AM, GDC 3.512

Teaching Assitant
Eric Hsiung, ehsiung@utexas.edu
Office hours: Tuesdays 11AM-12PM, GDC 5.802C

Assignments and Course Project

Assignments and the course project will be completed in groups of two.

The project will require instructor approval, and must consist of either original research or a reproduction study of a topic on either planning, adversarial search, or reasoning under uncertainty.