Planning has been an area of research in Artificial Intelligence for over three decades. It is concerned with the synthesis of sequences of actions (plans) that can be used by agents to achieve their desired goals. Planning involves the representation of actions and world models, reasoning about the effects of actions, and techniques for efficiently searching the space of possible plans. Planning has been used to automate a variety of tasks including robotics, process planning, information gathering, transportation planning, experiment planning in molecular genetics, and spacecraft mission control.

This course will focus on the basic foundations and techniques in planning and survey a wide variety of planning systems. The class will be run as a lecture course with lots of hands-on experience with state-of-the-art planning systems. The topics covered in the course will include: action and plan representation, generative planning, reactive systems, hierarchical and abstraction planning, case-based planning, machine learning in planning, multi-agent planning, interacting with the environment, temporal planning and scheduling, planning under uncertainty, decision theory in planning, and real-world planning applications.

Prerequisites: CS561a -- Introduction to AI

Grading: grades will be based on homework assignments [30%], midterm and final exams [30%], a course project [30%], class participation and quizzes [10%].

• Action and plan representations, historical overview, STRIPS (Aug 28)

• Plan generation and causal-link planning (Aug 30 & Sep 6)
• Hierarchical task network (HTN) planning (Sep 11)
• Skeletal planning, case based planning (Sep 13)
• Plan graph search (Sep 18)
• Planning as constraint satisfaction, planning as satisfiability (Sep 20)
• Planning using temporal logics (Sep 25)
• Heuristic search planning (Sep 27)
• Reactive systems (Oct 2)

• Knowledge representation for planning, ontologies, description logics (Oct 4)
• Reasoning about time: temporal reasoning and scheduling (Oct 11)

• Complexity of planning problems (Oct 9)
• Abstraction, macros, hierarchical planning (Oct 16)
• Learning search control knowledge (Oct 18)

• Multi-agent planning architectures (Oct 23)
• Reasoning about other agents (Oct 25)
• Planning within a team (Oct 30)
• Learning from an external environment (Nov 1)

• Representations (Nov 6)
• Exogenous events (Nov 8)
• Decision theory, Markov decision processes (Nov 13)

• Planning in training simulation environments (Nov 15)
• Mixed-initiative planning (Nov 20)
• Robotics (Nov 22)
• Space (Nov 27)

• Student presentations of course projects (Nov 29 & Dec 4)

• (Dec 6)

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