Lecture 20 of 41
Classical Planning:
Partial Order Planning (POP) Basics
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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
1 / 16
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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
- Static evaluation algorithm
- 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
- STRIPS axioms