Partial Order Planning - Artificial Intelligence - Lecture Slides, Slides of Artificial Intelligence

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Lecture 20 of 41

Classical Planning:

Partial Order Planning (POP) Basics

Lecture Outline

• Friday’s Reading
• Next Week: Review Chapters 8, 10
• 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
• Wednesday: More Classical Planning
  • Partial-order planning (NOAH, etc.)
  • Limitations
• First Hour Exam: Wednesday, 13 Oct 2004
  • Remote students: have exam agreement faxed to DCE
  • Exam will be faxed to proctors Wednesday or Friday

Midterm Review – KR, Logic, Proof Theory:

Unclear Points?

• Logical Frameworks
  • Knowledge Bases (KB)
  • Logic in general: representation languages, syntax, semantics
  • Propositional logic
  • First-order logic (FOL, FOPC)
  • Model theory, domain theory: possible worlds semantics, entailment
• Normal Forms
  • Conjunctive Normal Form (CNF)
  • Disjunctive Normal Form (DNF)
  • Horn Form
• Proof Theory and Inference Systems
  • Sequent calculi: rules of proof theory
  • Derivability or provability
  • Properties
    • Knowledge bases, WFFs : consistency, satisfiability, validity, entailment
    • Proof procedures : soundness, completeness; decidability (decision)

Midterm Review – Example Problems

Midterm Review – Game Trees:

Unclear Points?

• Games as Search Problems
  • Frameworks
  • Concepts: utility, reinforcements, game trees
  • Static evaluation under resource limitations
• Family of Algorithms for Game Trees: Minimax
  • Static evaluation algorithm
    • To arbitrary ply
    • To fixed ply
    • Sophistications: iterative deepening, alpha-beta pruning
  • Credit propagation
    • Intuitive concept
    • Basis for simple (delta-rule) learning algorithms
• State of The Field
• Uncertainty in Games: Expectiminimax and Other Algorithms

Adapted from slides by S. Russell, UC Berkeley

Planning in Situation Calculus

Making Plans:

A Better Way

Adapted from slides by S. Russell, UC Berkeley

Partially-Ordered Plans

Adapted from slides by S. Russell, UC Berkeley

Adapted from slides by S. Russell, UC Berkeley

POP Algorithm [2]:

Subroutines and Properties

Clobbering and

Promotion / Demotion

Adapted from slides by S. Russell, UC Berkeley

Adapted from slides by S. Russell, UC Berkeley

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