1. 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 Part III Heuristics and Control Strategies Dana S. Nau Dept. of Computer Science, and Institute for Systems Research University of Maryland Lecture slides for Automated Planning: Theory and Practice

2. 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/ 2 Motivation for Part 3 of the Book l Domain-independent planners suffer from combinatorial complexity u Planning is in the worst case intractable u Need ways to control the search l Search heuristics (Chapter 9) l Pruning rules (Chapter 10) l HTN decomposition (Chapter 11)

3. 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/ 3 Abstract Search Procedure l Here is a general framework for describing classical and neoclassical planners u Details may differ from one planner to another u e.g., the steps don’t have to be in this order

4. 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/ 4 Search Heuristics l Chapter 9: Heuristics in Planning u Heuristics for deciding what part of the space to search next u The heuristics are domain-independent within classical planning Chapter 9

5. 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 Pruning Techniques l Chapter 10: search-control rules l Chapter 11: hierarchical task decomposition l The planning algorithms are domain-configurable u Domain-independent planning engine u Domain-specific information to control the search Chapter 10 Chapter 11

6. 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 Plan-Space Planning l Branching/pruning: u select a flaw, find its resolvers l Refinement: u apply a resolver

7. 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/ 7 State-Space Planning Forward-search u Branching: apply  to a state Backward-search u Branching: apply  –1 to a goal l Cycle detection is a type of pruning

8. 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/ 8 State-Space Planning l Two different views of the block-stacking algorithm in Chapter 4 u Branching: only generate actions that tear down “bad” stacks or build up “good” ones (Chapter 11) u Pruning: prune actions that build up “bad” stacks or tear down “good” ones (Chapter 10)

9. 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/ 9 Planning-Graph Planning Abstract-search with iterative deepening wrapped around it u Refinement (planning-graph extension): propagate constraints u Branching (in solution extraction): actions that achieve subgoals u Pruning (in solution extraction): use mutex info to prune actions for k = 1, 2, …