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Lecture 23 of 41 Planning and Reaction Friday, 15 October 2004 William H. Hsu Department of Computing and Information Sciences, KSU http://www.kddresearch.org http://www.cis.ksu.edu/~bhsu Reading: Chapter 12, Russell and Norvig CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences Lecture Outline • Today’s Reading – Chapter 13, Russell and Norvig – References: Readings in Planning – Allen, Hendler, and Tate • Next Week: Review Chapter 14, Russell and Norvig (Basic Probability) • Previously: Classical and Abstract Planning • Today, Monday: More Practical Planning – Conditional planning, concluded – Monitoring • Next Wednesday: Introduction to Uncertain Reasoning – Uncertainty in AI • Need for uncertain representation • Soft computing: probabilistic, neural, fuzzy, other representations – Probabilistic knowledge representation • Views of probability • Justification CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences Review: Hierarchical Abstraction Planning • 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) Adapted from Russell and Norvig CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences Review: How Things Go Wrong in Planning Adapted from slides by S. Russell, UC Berkeley CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences Review: Practical Planning Solutions Adapted from slides by S. Russell, UC Berkeley CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences Conditional Planning Adapted from slides by S. Russell, UC Berkeley CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences Conditional Planning Example Adapted from slides by S. Russell, UC Berkeley CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences Monitoring and Replanning CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences Preconditions for Remaining Plan Adapted from slides by S. Russell, UC Berkeley CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences Replanning Adapted from slides by S. Russell, UC Berkeley CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences Summary Points • Two Weeks Ago: Introduction to Classical Planning – Search vs. planning – STRIPS axioms • Last Week: More Classical Planning – Partial-order planning (NOAH, etc.)” – Limitations of POP – Abstraction in planning – Producing practical planners • Today: More Practical Planning – Conditional planning, concluded – Monitoring and replanning – Relation to reactive and universal planning CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences Terminology • Classical Planning Framework – Planning versus search – Representation: initial state, goal state / test, operators – STRIPS operators – Partial versus total-order: property of plans – Interleaved vs. noninterleaved: property of planners • Last Week – Hierarchical abstraction planning: ABSTRIPS – Conditional plans • This Week – Monitoring and replanning – Reactive plans and policies • Later – Decision theory – Markov decision processes CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences