Ch. 11 – Planning Supplemental slides for CSE 327 Prof. Jeff Heflin.

Slides:



Advertisements
Similar presentations
Ch. 5 – Adversarial Search Supplemental slides for CSE 327 Prof. Jeff Heflin.
Advertisements

Graphplan Joe Souto CSE 497: AI Planning Sources: Ch. 6 “Fast Planning through Planning Graph Analysis”, A. Blum & M. Furst.
Ch. 2 – Intelligent Agents Supplemental slides for CSE 327 Prof. Jeff Heflin.
TWEAK solving the Sussman anomaly ON(A,B) ON(B,C) ON(C,A) ONTABLE(A) ONTABLE(B) CLEAR(C) CLEAR(B) ARMEMPTY STACK(A,B) STACK(B,C) Two step additions to.
Planning Planning is fundamental to “intelligent” behaviour. E.g.
All rights reserved ©L. Manevitz Lecture 61 Artificial Intelligence Planning System L. Manevitz.
Sussman anomaly - analysis The start state is given by: ON(C, A) ONTABLE(A) ONTABLE(B) ARMEMPTY The goal by: ON(A,B) ON(B,C) This immediately leads to.
Planning Russell and Norvig: Chapter 11 Slides adapted from: robotics.stanford.edu/~latombe/cs121/2003/ home.htm.
Artificial Intelligence II S. Russell and P. Norvig Artificial Intelligence: A Modern Approach Chapter 11: Planning.
Ch. 10 – Planning Supplemental slides for CSE 327 Prof. Jeff Heflin.
1 Classical STRIPS Planning Alan Fern * * Based in part on slides by Daniel Weld.
CPSC 322 Introduction to Artificial Intelligence November 19, 2004.
Planning Russell and Norvig: Chapter 11. Planning Agent environment agent ? sensors actuators A1A2A3.
Planning Problem Solving  Planning –Action Centred –More flexible decision strategies A Representation for Planning –Add & Delete Lists Planning Techniques.
1 Lecture 12 example (from slides prepared by Prof. J. Rosenchein)
Planning: Part 1 Representation and State-space Search COMP151 March 30, 2007.
Intro to AI Fall 2002 © L. Joskowicz 1 Introduction to Artificial Intelligence LECTURE 12: Planning Motivation Search, theorem proving, and planning Situation.
1 Pertemuan 17 Planning Matakuliah: T0264/Intelijensia Semu Tahun: Juli 2006 Versi: 2/1.
1 Action Planning (Where logic-based representation of knowledge makes search problems more interesting) R&N: Chap. 11, Sect. 11.1–4.
AI Principles, Lecture on Planning Planning Jeremy Wyatt.
Lirong Xia Friday, April 25, 2014 Introduction to Game Theory.
PLANNING Partial order regression planning Temporal representation 1 Deductive planning in Logic Temporal representation 2.
PLANNING Partial order regression planning Temporal representation 1 Deductive planning in Logic Temporal representation 2.
An Introduction to Artificial Intelligence CE Chapter 11 – Planning Ramin Halavati In which we see how an agent can take.
1 07. The planning problem 2  Inputs: 1. A description of the world state 2. The goal state description 3. A set of actions  Output: A sequence of actions.
Automated Planning and Decision Making Prof. Ronen Brafman Automated Planning and Decision Making Introduction.
Artificial Intelligence 1: Planning
April 3, 2006AI: Chapter 11: Planning1 Artificial Intelligence Chapter 11: Planning Michael Scherger Department of Computer Science Kent State University.
1 CSC 8520 Spring Paula MatuszekBased on
Planning Chapter Planning The Planning problem Planning with State-space search Partial-order planning Planning graphs Planning with propositional.
Action Planning (Where logic-based representation of knowledge makes search problems more interesting) R&N: Chap. 10.3, Chap. 11, Sect. 11.1–4 (2 nd edition.
M. Silaghi Robotics&AI Planning. M. Silaghi Robotics&AI Outline ● Search vs. planning ● STRIPS operators ● Partial-order planning.
CS.462 Artificial Intelligence SOMCHAI THANGSATHITYANGKUL Lecture 07 : Planning.
Automated Planning and Decision Making Prof. Ronen Brafman Automated Planning and Decision Making Introduction.
Artificial Intelligence Chapter 22 Planning Biointelligence Lab School of Computer Sci. & Eng. Seoul National University.
CPS 270: Artificial Intelligence Planning Instructor: Vincent Conitzer.
CPS 570: Artificial Intelligence Planning Instructor: Vincent Conitzer.
1 Chapter 16 Planning Methods. 2 Chapter 16 Contents (1) l STRIPS l STRIPS Implementation l Partial Order Planning l The Principle of Least Commitment.
AI Lecture 17 Planning Noémie Elhadad (substituting for Prof. McKeown)
1/16 Planning Chapter 11- Part1 Author: Vali Derhami.
Koen HindriksMulti-Agent Systems 2012 Introduction Agent Programming Koen Hindriks Delft University of Technology, The Netherlands Learning to program.
Ch. 7 – Logical Agents Supplemental slides for CSE 327 Prof. Jeff Heflin.
April 3, 2006AI: Chapter 11: Planning1 Artificial Intelligence Chapter 11: Planning.
Planning in FOL Systems sequences of actions to achieve goals.
Koen HindriksMulti-Agent Systems Introduction Agent Programming Koen Hindriks Delft University of Technology, The Netherlands Learning to program teaches.
Artificial Intelligence 2004 Planning: Situation Calculus Review STRIPS POP Hierarchical Planning Situation Calculus (John McCarthy) situations.
Ch. 7 – Logical Agents Supplemental slides for CSE 327 Prof. Jeff Heflin.
1 Planning Some material adopted from notes by Andreas Geyer-Schulz and Chuck Dyer.
An Introduction to Artificial Intelligence CE 40417
Ch. 2 – Intelligent Agents
Instructor: Vincent Conitzer
Introduction Contents Sungwook Yoon, Postdoctoral Research Associate
Planning (AIMA Ch. 10) Planning problem defined Simple planning agent
AI Planning.
Supplemental slides for CSE 327 Prof. Jeff Heflin
Artificial Intelligence: Planning
L11. Planning Agents and STRIPS
Planning Chapter
Planning Problems On(C, A)‏ On(A, Table)‏ On(B, Table)‏ Clear(C)‏
CPSC 322 Introduction to Artificial Intelligence
CPS 570: Artificial Intelligence Planning
Supplemental slides for CSE 327 Prof. Jeff Heflin
Ch. 5 – Adversarial Search
CS344 : Introduction to Artificial Intelligence
Russell and Norvig: Chapter 11 CS121 – Winter 2003
L9. STRIPS Examples Recursive STRIPS Block world
Artificial Intelligence Planning
Prof. Pushpak Bhattacharyya, IIT Bombay
Supplemental slides for CSE 327 Prof. Jeff Heflin
Ch. 2 – Intelligent Agents
Presentation transcript:

Ch. 11 – Planning Supplemental slides for CSE 327 Prof. Jeff Heflin

Goal-Based Agent sensors actuators Agent Environment What the world is like now What action I should do now Goals State How the world evolves What my actions do What it will be like if I do action A From Fig. 2.13, p. 50

Blocks World Example actions Action(Move(b,x,y), Precond: On(b,x)  Clear(b)  Clear(y)  Block(y), Effect: On(b,y)  Clear(x)   On(b,x)   Clear(y)) Action(MoveToTable(b,x), Precond: On(b,x)  Clear(b), Effect: On(b,Table)  Clear(x)   On(b,x)) initial state –On(A,Table)  On(B,Table)  Clear(A)  Clear(B)  Clear(Table)  Block(A)  Block(B) goal –On(A,B)

Applicable Actions Action(Move(b,x,y), Precond: On(b,x)  Clear(b)  Clear(y)  Block(y), Effect: On(b,y)  Clear(x)   On(b,x)   Clear(y)) Action(MoveToTable(b,x), Precond: On(b,x)  Clear(b), Effect: On(b,Table)  Clear(x)   On(b,x)) an action a is applicable in any state that satisfies the precondition e.g., Move(A,Table,B) is applicable in initial state –unify with action description –apply substitution  = {b/A, x/Table, y/B} to the action’s Precondition –initial state satisfies On(A,Table)  Clear(A)  Clear(B)  Block(B) Initial State: On(A,Table)  On(B,Table)  Clear(A)  Clear(B)  Clear(Table)  Block(A)  Block(B)

A Blocks World Problem initial state –On(A,Table)  On(B,Table)  On(C,Table)  Clear(A)  Clear(B)  Clear(C)  Clear(Table)  Block(A)  Block(B)  Block(C) goal state –On(C,Table)  On(B,C)  On(A,B) actions –Action(Move(b,x,y), Precond: On(b,x)  Clear(b)  Clear(y)  Block(y), Effect: On(b,y)  Clear(x)   On(b,x)   Clear(y)) –Action(MoveToTable(b,x), Precond: On(b,x)  Clear(b), Effect: On(b,Table)  Clear(x)   On(b,x))

Backward State-Space Search On(C,T) ^ On(B,C) ^ On(A,B) On(C,T) ^ On(A,B) ^ On(B,x) ^ Clear(B) ^ Clear(C) On(C,T) ^ On(B,C) ^ On(A,x) ^ Clear(A) ^ Clear(B) On(C,T) ^ On(A,x) ^ Clear(A) ^ Clear(B) ^ On(B,y) ^ Clear(C) Move(B,x,C) x  C Move(A,x,B) x  B Move(B,y,C) y  C GOAL: INITIAL STATE: (if x=T and y= T) Move(A,y,B)? Inconsistent: has effect delete Clear(B) ?? Move(C,x,T) MoveToTable(C,x) bold – literal that was added to the predecessor underline – goal is not satisfied by initial state

POP Example Problem Actions –Action(ActionA, PRECOND: Z, EFFECT: Y   Z) –Action(ActionB, PRECOND: Z, EFFECT: X) –Action(ActionC, PRECOND: X, EFFECT: V) –Action(ActionD, PRECOND: Y, EFFECT: W   Y) Initial State: Z Goal State: V  W

POP Solution X Z ActionB V X ActionC ZYZY Z ActionA WYWY Y ActionD Finish VWVW Start Z X V W Y Z Z