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

Some concept of Artificial Intelligence are Agents and Problem Solving, Autonomy, Programs, Classical and Modern Planning, First-Order Logic, Resolution Theorem Proving, Search Strategies, Structure Learning. Main points of this lecture are: Partial Order Planning, Logical Representations, Theorem Proving, Proof Procedures, Propositional, Predicate, First-Order Logical Languages, Resolution Refutation, Search, Planning

Typology: Slides

2012/2013

Uploaded on 04/29/2013

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Lecture 20 of 41
Classical Planning:
Partial Order Planning (POP) Basics
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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