computing & information sciences kansas state university wednesday, 15 oct 2008cis 530 / 730:...
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Computing & Information SciencesKansas 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/v9v3
Course web site: http://www.kddresearch.org/Courses/Fall-2008/CIS730
Instructor home page: http://www.cis.ksu.edu/~bhsu
Reading for Next Class:
Section 12.1 – 12.4, Russell & Norvig 2nd edition
HTN Planning and Robust PlanningDiscussion: CSP & Game Trees Review
Computing & Information SciencesKansas State University
Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence
Constraint satisfaction problems (CSPs):Review
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 Xi with values from domain Di
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. RussellFrom: http://aima.eecs.berkeley.edu/slides-ppt/ Reused with permission.
Computing & Information SciencesKansas State University
Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence
Constraint graph:Review
Constraint graph:Review
Binary CSP: each constraint relates two variables Constraint graph: nodes are variables, arcs are constraints
© 2004 S. J. RussellFrom: http://aima.eecs.berkeley.edu/slides-ppt/ Reused with permission.
Computing & Information SciencesKansas State University
Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence
Standard search formulation:Review
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 CSPs2. Every solution appears at depth n with n variables
use depth-first search3. Path is irrelevant, so can also use complete-state formulation4. b = (n - l )d at depth l, hence n! · dn leaves
5.
© 2004 S. J. RussellFrom: http://aima.eecs.berkeley.edu/slides-ppt/ Reused with permission.
Computing & Information SciencesKansas State University
Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence
Arc consistency algorithm AC-3:Review
Arc consistency algorithm AC-3:Review
Time complexity: O(n2d3)
© 2004 S. J. RussellFrom: http://aima.eecs.berkeley.edu/slides-ppt/ Reused with permission.
Computing & Information SciencesKansas State University
Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence
Local search for CSPsLocal 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. RussellFrom: http://aima.eecs.berkeley.edu/slides-ppt/ Reused with permission.
Computing & Information SciencesKansas State University
Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence
Alpha-Beta (-) Pruning:Modified Minimax Algorithm
Adapted from slides by S. RussellUC Berkeley
Computing & Information SciencesKansas State University
Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence
Expectiminimax [1]
Computing & Information SciencesKansas State University
Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence
Expectiminimax [2]
Computing & Information SciencesKansas State University
Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence
Expectiminimax [3]
Computing & Information SciencesKansas State University
Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence
Expectiminimax [4]
Computing & Information SciencesKansas 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
Computing & Information SciencesKansas 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
Computing & Information SciencesKansas State University
Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence
Review: Clobbering andPromotion / Demotion
Adapted from slides by S. Russell, UC Berkeley
Computing & Information SciencesKansas State University
Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence
Review:POP Example – Sussman Anomaly
Adapted from slides by S. Russell, UC Berkeley
Computing & Information SciencesKansas 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 abstractionSee: Sections 12.2 – 12.3 R&N, pp. 371 – 380
Figure 12.1, 12.6 (examples), 12.2 (algorithm), 12.3-5 (properties)
Computing & Information SciencesKansas State University
Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence
Universal Quantifiers in Planning
Quantification within Operators p. 383 R&N
ExamplesShakey’s World
Blocks World
Grocery shopping
Others (from projects?)
Exercise for Next Tuesday: Blocks World
Computing & Information SciencesKansas 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)
Computing & Information SciencesKansas State University
Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence
Review:How Things Go Wrong in Planning
Computing & Information SciencesKansas State University
Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence
Review:Practical Planning Solutions
Adapted from slides by S. Russell, UC Berkeley
Computing & Information SciencesKansas State University
Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence
Conditional Planning
Adapted from slides by S. Russell, UC Berkeley
Computing & Information SciencesKansas State University
Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence
Monitoring and ReplanningMonitoring and Replanning
Adapted from slides by S. Russell, UC Berkeley
Computing & Information SciencesKansas State University
Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence
Preconditions for Remaining Plan
Adapted from slides by S. Russell, UC Berkeley
Computing & Information SciencesKansas State University
Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence
Replanning
Adapted from slides by S. Russell, UC Berkeley
Computing & Information SciencesKansas 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 axiomsOperator 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
Computing & Information SciencesKansas State University
Wednesday, 15 Oct 2008CIS 530 / 730: Artificial Intelligence
Terminology
Classical Planning Planning versus search
Problematic approaches to planningForward 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