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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Lecture 20 of 42 Wednesday, 15 October 2008 William H. Hsu Department of Computing and Information Sciences, KSU KSOL course page: http://snipurl.com/v9v3http://snipurl.com/v9v3 Course web site: http://www.kddresearch.org/Courses/Fall-2008/CIS730 Instructor home page: http://www.cis.ksu.edu/~bhsuhttp://www.cis.ksu.edu/~bhsu Reading for Next Class: Section 12.1 – 12.4, Russell & Norvig 2 nd edition HTN Planning and Robust Planning Discussion: CSP & Game Trees Review
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Constraint satisfaction problems (CSPs): Review Standard search problem: state is a "black box“ – any data structure that supports successor function, heuristic function, and goal test CSP: state is defined by variables X i with values from domain D i goal test is a set of constraints specifying allowable combinations of values for subsets of variables Simple example of a formal representation language Allows useful general-purpose algorithms with more power than standard search algorithms © 2004 S. J. Russell From: http://aima.eecs.berkeley.edu/slides-ppt/http://aima.eecs.berkeley.edu/slides-ppt/ Reused with permission.
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Constraint graph: Review Binary CSP: each constraint relates two variables Constraint graph: nodes are variables, arcs are constraints © 2004 S. J. Russell From: http://aima.eecs.berkeley.edu/slides-ppt/http://aima.eecs.berkeley.edu/slides-ppt/ Reused with permission.
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Standard search formulation: Review Let's start with the straightforward approach, then fix it States are defined by the values assigned so far Initial state: the empty assignment { } Successor function: assign a value to an unassigned variable that does not conflict with current assignment fail if no legal assignments Goal test: the current assignment is complete 1.This is the same for all CSPs 2.Every solution appears at depth n with n variables use depth-first search 3.Path is irrelevant, so can also use complete-state formulation 4.b = (n - l )d at depth l, hence n! · d n leaves © 2004 S. J. Russell From: http://aima.eecs.berkeley.edu/slides-ppt/http://aima.eecs.berkeley.edu/slides-ppt/ Reused with permission.
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Arc consistency algorithm AC-3: Review Time complexity: O(n 2 d 3 ) © 2004 S. J. Russell From: http://aima.eecs.berkeley.edu/slides-ppt/http://aima.eecs.berkeley.edu/slides-ppt/ Reused with permission.
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Local search for CSPs Hill-climbing, simulated annealing typically work with "complete" states, i.e., all variables assigned To apply to CSPs: allow states with unsatisfied constraints operators reassign variable values Variable selection: randomly select any conflicted variable Value selection by min-conflicts heuristic: choose value that violates the fewest constraints i.e., hill-climb with h(n) = total number of violated constraints © 2004 S. J. Russell From: http://aima.eecs.berkeley.edu/slides-ppt/http://aima.eecs.berkeley.edu/slides-ppt/ Reused with permission.
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Alpha-Beta ( - ) Pruning: Modified Minimax Algorithm Adapted from slides by S. Russell UC Berkeley
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Expectiminimax [1]
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Expectiminimax [2]
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Expectiminimax [3]
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Expectiminimax [4]
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Lecture Outline Today’s Reading: Sections 11.4 – 11.7, 12.1 – 12.4, R&N 2e Today and Wednesday: Practical Planning Conditional Planning Replanning Monitoring and Execution Continual Planning Wednesday: Hierarchical Planning Revisited Examples: Korf Real-World Example Friday: Robust Planning, Uncertainty, Planning-Like Problems Planning-like problems: design; scheduling; tutoring, critiquing Why probability? Planning and reaction Planning under Uncertainty
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Planning and Learning Roadmap Bounded Indeterminacy (12.3) Four Techniques for Dealing with Nondeterministic Domains 1. Sensorless / Conformant Planning: “Be Prepared” (12.3) Idea: be able to respond to any situation (universal planning) Coercion 2. Conditional / Contingency Planning: “Plan B” (12.4) Idea: be able to respond to many typical alternative situations Actions for sensing (“reviewing the situation”) 3. Execution Monitoring / Replanning: “Show Must Go On” (12.5) Idea: be able to resume momentarily failed plans Plan revision 4. Continuous Planning: “Always in Motion, The Future Is” (12.6) Lifetime planning (and learning!) Formulate new goals
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Review: Clobbering and Promotion / Demotion Adapted from slides by S. Russell, UC Berkeley
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Review: POP Example – Sussman Anomaly Adapted from slides by S. Russell, UC Berkeley
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Hierarchical 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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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Universal Quantifiers in Planning Quantification within Operators p. 383 R&N Examples Shakey’s World Blocks World Grocery shopping Others (from projects?) Exercise for Next Tuesday: Blocks World
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Practical 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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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Review: How Things Go Wrong in Planning
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Review: Practical Planning Solutions Adapted from slides by S. Russell, UC Berkeley
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Conditional Planning Adapted from slides by S. Russell, UC Berkeley
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Monitoring and Replanning Adapted from slides by S. Russell, UC Berkeley
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Preconditions for Remaining Plan Adapted from slides by S. Russell, UC Berkeley
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Replanning Adapted from slides by S. Russell, UC Berkeley
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Summary Points Previously: Logical Representations and Theorem Proving Propositional, predicate, and first-order logical languages Proof procedures: forward and backward chaining, resolution refutation Today: Introduction to Classical Planning Search vs. planning STRIPS axioms Operator representation Components: preconditions, postconditions (ADD, DELETE lists) Friday: Robust Planning, Uncertainty, Planning-Like Problems Planning-like problems: design; scheduling; tutoring, critiquing Why probability? Planning and reaction Planning under Uncertainty
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Computing & Information Sciences Kansas State University Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence Terminology Classical Planning Planning versus search Problematic approaches to planning Forward chaining Situation calculus Representation Initial state Goal state / test Operators Efficient Representations STRIPS axioms Components: preconditions, postconditions (ADD, DELETE lists) Clobbering / threatening Reactive plans and policies Markov decision processes
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