Dana Nau: Lecture slides for Automated Planning Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike License: http://creativecommons.org/licenses/by-nc-sa/2.0/ 1 Review for the Midterm Exam Lecture slides for Automated Planning: Theory and Practice Dana S. Nau University of Maryland 4:54 PM March 19, 2012Dana Nau: Lecture slides for Automated Planning Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike License: http://creativecommons.org/licenses/by-nc-sa/2.0/ 2 The Chapters We’ve Covered ● Chapter 1: Introduction ● Chapter 2: Representations for Classical Planning ● Chapter 3: Complexity of Classical Planning ● Chapter 4: State-Space Planning ● Chapter 5: Plan-Space Planning ● Chapter 6: Planning-Graph Techniques ● Chapter 7: Propositional Satisfiability Techniques ● Chapter 16: Planning based on MDPs ● Chapter 17: Planning based on Model CheckingDana Nau: Lecture slides for Automated Planning Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike License: http://creativecommons.org/licenses/by-nc-sa/2.0/ 3 Chapter 1: Introduction and Overview ● 1.1: First Intuitions on Planning ● 1.2: Forms of planning ● 1.3: Domain-Independent Planning ● 1.4: Conceptual Model for Planning ● 1.5: Restricted Model ● 1.6: Extended Models ● 1.7: A Running Example: Dock-Worker Robots No questions on Chapter 1Dana Nau: Lecture slides for Automated Planning Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike License: http://creativecommons.org/licenses/by-nc-sa/2.0/ 4 2: Representations for Classical Planning ● 2.1: Introduction ● 2.2: Set-Theoretic Representation ◆ 2.2.1: Planning Domains, Problems, and Solutions ◆ 2.2.2: State Reachability ◆ 2.2.3: Stating a Planning Problem ◆ 2.2.4: Properties of the Set-theoretic Representation ● 2.3: Classical Representation ◆ 2.3.1: States ◆ 2.3.2: Operators and Actions ◆ 2.3.3: Plans, Problems, & Solutions ◆ 2.3.4: Semantics of Classical Reps ● 2.4: Extending the Classical Rep. ◆ 2.4.1: Simple Syntactical Extensions ◆ 2.4.2: Conditional Planning Operators ◆ 2.4.3: Quantified Expressions ◆ 2.4.4: Disjunctive Preconditions ◆ 2.4.5: Axiomatic Inference ◆ 2.4.6: Function Symbols ◆ 2.4.7: Attached Procedures ◆ 2.4.8: Extended Goals ● 2.5: State-Variable Representation ◆ 2.5.1: State Variables ◆ 2.5.2: Operators and Actions ◆ 2.5.3: Domains and Problems ◆ 2.5.4: Properties ● 2.6: Comparisons No questions on these topics:Dana Nau: Lecture slides for Automated Planning Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike License: http://creativecommons.org/licenses/by-nc-sa/2.0/ 5 Chapter 3: Complexity of Classical Planning ● 3.1: Introduction ● 3.2: Preliminaries ● 3.3: Decidability and Undecidability Results ● 3.4: Complexity Results ◆ 3.4.1: Binary Counters ◆ 3.4.2: Unrestricted Classical Planning ◆ 3.4.3: Other results ● 3.5: Limitations You don’t need to know the details of the complexity tables, but you should know the basic concepts, e.g.: - What does it mean to allow or disallow function symbols, negative effects, etc.? - What’s the difference between giving the operators in the input or in advance?Dana Nau: Lecture slides for Automated Planning Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike License: http://creativecommons.org/licenses/by-nc-sa/2.0/ 6 Chapter 4: State-Space Planning ● 4.1: Introduction ● 4.2: Forward Search ◆ 4.2.1: Formal Properties ◆ 4.2.2: Deterministic Implementations ● 4.3: Backward Search ● 4.4: The STRIPS Algorithm ● 4.5: Domain-Specific State-Space Planning ◆ 4.5.1: The Container-Stacking Domain ◆ 4.5.2: Planning Algorithm No questions on this topicDana Nau: Lecture slides for Automated Planning Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike License: http://creativecommons.org/licenses/by-nc-sa/2.0/ 7 ● 5.1: Introduction ● 5.2: The Search Space of Partial Plans ● 5.3: Solution Plans ● 5.4: Algorithms for Plan Space Planning ◆ 5.4.1: The PSP Procedure ◆ 5.4.2: The PoP Procedure ● 5.5: Extensions ● 5.6: Plan Space Versus State Space Planning Chapter 5: Plan-Space Planning No questions on these topicsDana Nau: Lecture slides for Automated Planning Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike License: http://creativecommons.org/licenses/by-nc-sa/2.0/ 8 Chapter 6: Planning-Graph Techniques ● 6.1: Introduction ● 6.2: Planning Graphs ◆ 6.2.1: Reachability Trees ◆ 6.2.2: Reachability with Planning Graphs ◆ 6.2.3: Independent Actions and Layered Plans ◆ 6.2.4: Mutual Exclusion Relations ● 6.3: The Graphplan Planner ◆ 6.3.1: Expanding the Planning Graph ◆ 6.3.2: Searching the Planning Graph ◆ 6.3.3: Analysis of Graphplan ● 6.4: Extensions and Improvements of Graphplan ◆ 6.4.1: Extending the Language ◆ 6.4.2: Improving the Planner ◆ 6.4.3: Extending the Independence Relation use my lecture notes No questions on these topicsDana Nau: Lecture slides for Automated Planning Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike License: http://creativecommons.org/licenses/by-nc-sa/2.0/ 9 7: Propositional Satisfiability Techniques ● 7.1: Introduction ● 7.2: Planning problems as Satisfiability problems ◆ 7.2.1: States as propositional formulas ◆ 7.2.2: State transitions as propositional formulas ◆ 7.2.3: Planning problems as propositional formulas ● 7.3: Planning by Satisfiability ◆ 7.3.1: Davis-Putnam ◆ 7.3.2: Stochastic Procedures ● 7.4: Different Encodings ◆ 7.4.1: Action Representation ◆ 7.4.2: Frame axioms No questions on these topics No questions on these topicsDana Nau: Lecture slides for Automated Planning Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike License: http://creativecommons.org/licenses/by-nc-sa/2.0/ 10 Chapter 16: Planning Based on MDPs ● 16.1: Introduction ● 16.2: Planning in Fully Observable Domains ◆ 16.2.1: Domains, Plans, and Planning Problems