lecture 21 of 41
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Lecture 21 of 41. Classical Planning: STRIPS/ABSTRIPS and POP. Wednesday, 08 October 2004 William H. Hsu Department of Computing and Information Sciences, KSU http://www.kddresearch.org http://www.cis.ksu.edu/~bhsu Reading: Sections 11.5 – 11.9, Russell and Norvig. Lecture Outline. - PowerPoint PPT PresentationTRANSCRIPT
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Kansas State University
Department of Computing and Information SciencesCIS 730: Introduction to Artificial Intelligence
Lecture 21 of 41Lecture 21 of 41
Wednesday, 08 October 2004
William H. Hsu
Department of Computing and Information Sciences, KSUhttp://www.kddresearch.org
http://www.cis.ksu.edu/~bhsu
Reading:
Sections 11.5 – 11.9, Russell and Norvig
Classical Planning:STRIPS/ABSTRIPS and POP
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Kansas State University
Department of Computing and Information SciencesCIS 730: Introduction to Artificial Intelligence
Lecture OutlineLecture Outline
• Today’s Reading– Sections 11.5 – 11.9, Russell and Norvig
– References: to be posted on class web board
• Next Week’s Reading: Chapter 12, Russell and Norvig
• Previously: Logical Representations and Theorem Proving
• Today: More Classical Planning– STRIPS axioms (review)
– Partial-order planning (NOAH, etc.)
– Limitations of POP
• Need for abstraction
• Hierarchical abstraction (ABSTRIPS)
• First Hour Exam: Wednesday 13 October 2004, in class– Remote students: have exam agreement faxed to DCE
– Exam will be faxed to proctors Wed
• Next Week: More Planning – Conditional and Reactive
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Kansas State University
Department of Computing and Information SciencesCIS 730: Introduction to Artificial Intelligence
POP Algorithm [1]:POP Algorithm [1]:SketchSketch
Adapted from slides by S. Russell, UC Berkeley
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Kansas State University
Department of Computing and Information SciencesCIS 730: Introduction to Artificial Intelligence
Adapted from slides by S. Russell, UC Berkeley
POP Algorithm [2]:POP Algorithm [2]:Subroutines and PropertiesSubroutines and Properties
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Kansas State University
Department of Computing and Information SciencesCIS 730: Introduction to Artificial Intelligence
Clobbering andClobbering andPromotion / DemotionPromotion / Demotion
Adapted from slides by S. Russell, UC Berkeley
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Kansas State University
Department of Computing and Information SciencesCIS 730: Introduction to Artificial Intelligence
Example: Blocks World [1]Example: Blocks World [1]SpecificationSpecification
Adapted from slides by S. Russell, UC Berkeley
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Kansas State University
Department of Computing and Information SciencesCIS 730: Introduction to Artificial Intelligence
Example: Blocks World [2]Example: Blocks World [2]POP TracePOP Trace
Adapted from slides by S. Russell, UC Berkeley
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Kansas State University
Department of Computing and Information SciencesCIS 730: Introduction to Artificial Intelligence
Adapted from slides by S. Russell, UC Berkeley
Example:Example:Preconditions for Remaining PlanPreconditions for Remaining Plan
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Kansas State University
Department of Computing and Information SciencesCIS 730: Introduction to Artificial Intelligence
Hierarchical Abstraction PlanningHierarchical Abstraction Planning
Adapted from Russell and Norvig
• Need for Abstraction– Question: What is wrong with uniform granularity?
– Answers (among many)
• Representational problems
• Inferential problems: inefficient plan synthesis
• Family of Solutions: Abstract Planning– But what to abstract in “problem environment”, “representation”?
• Objects, obstacles (quantification: later)
• Assumptions (closed world)
• Other entities
• Operators
• Situations
– Hierarchical abstraction
• See: Sections 12.2 – 12.3 R&N, pp. 371 – 380
• Figure 12.1, 12.6 (examples), 12.2 (algorithm), 12.3-5 (properties)
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Kansas State University
Department of Computing and Information SciencesCIS 730: Introduction to Artificial Intelligence
Universal Quantifiers in PlanningUniversal Quantifiers in Planning
• Quantification within Operators– Chapter 11, R&N 2e
– Examples
• Shakey’s World
• Blocks World (R&N; also in Winston, Rich and Knight)
• Grocery shopping
– Others (from projects?)
• Exercise for Next Tuesday: Blocks World
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Kansas State University
Department of Computing and Information SciencesCIS 730: Introduction to Artificial Intelligence
Practical PlanningPractical Planning
Adapted from Russell and Norvig
• The Real World– What can go wrong with classical planning?
– What are possible solution approaches?
• Conditional Planning
• Monitoring and Replanning (Next Time)
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Kansas State University
Department of Computing and Information SciencesCIS 730: Introduction to Artificial Intelligence
Review:Review:Clobbering and Promotion / Demotion in PlansClobbering and Promotion / Demotion in Plans
Adapted from slides by S. Russell, UC Berkeley
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Kansas State University
Department of Computing and Information SciencesCIS 730: Introduction to Artificial Intelligence
Things Go WrongThings Go Wrong
Adapted from slides by S. Russell, UC Berkeley
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Kansas State University
Department of Computing and Information SciencesCIS 730: Introduction to Artificial Intelligence
SolutionsSolutions
Adapted from slides by S. Russell, UC Berkeley
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Kansas State University
Department of Computing and Information SciencesCIS 730: Introduction to Artificial Intelligence
Summary PointsSummary Points
• Tuesday: Introduction to Classical Planning– Search vs. planning
– STRIPS axioms
• Operator representation
• Components: preconditions, postconditions (ADD, DELETE lists)
• Today: More Classical Planning– Partial-order planning (NOAH, etc.)
• Old terminology (deprecated): “linear” vs. “non-linear”
• Modern terminology (preferred): “partial-order (POP)” vs. “non-POP”
– Limitations of POP
• Haven’t considered conditionals yet (qualification problem revisited)
• Frame problems: representational, inferential; circumscription issues
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Kansas State University
Department of Computing and Information SciencesCIS 730: Introduction to Artificial Intelligence
Adapted from slides by S. Russell, UC Berkeley
TerminologyTerminology
• Classical Planning Framework– Planning versus search
– Representation: initial state, goal state / test, operators
• STRIPS Operators– Components: preconditions, postconditions (ADD, DELETE lists)
– STRIPS and interference
• Clobbering / threatening
• Promotion / demotion
– Partial-Order Planners (POP systems)
• Next Week– Hierarchical abstraction planning: ABSTRIPS
– Conditional plans
– Reactive plans and policies
– Markov decision processes