Planning as Satisfiability cse 473 sp 06. SATPLAN cnf formula satisfying model plan mapping length STRIPS problem description SAT engine encoder interpreter.

Slides:



Advertisements
Similar presentations
AAAI of 20 Deconstructing Planning as Satisfiability A Revised Report Henry Kautz UW University of Rochester in collaboration with Bart Selman and.
Advertisements

REVIEW : Planning To make your thinking more concrete, use a real problem to ground your discussion. –Develop a plan for a person who is getting out of.
Planning Chapter 11 Yet another popular formulation for AI – Logic-based language – One of the most structured formulations Can be translate into less.
State Transition Systems  linear planning  bounded model checking  conditional planning  model checking  state transition description languages: oPDDL.
Dana Nau: Lecture slides for Automated Planning Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike License:
Propositional and First Order Reasoning. Terminology Propositional variable: boolean variable (p) Literal: propositional variable or its negation p 
Models and Propositional Logic In propositional logic, a model in general simply fixes the truth value – true or false – for every proposition symbol.
Planning with Constraints Graphplan & SATplan Yongmei Shi.
SAT ∩ AI Henry Kautz University of Rochester. Outline Ancient History: Planning as Satisfiability The Future: Markov Logic.
Solving Partial Order Constraints for LPO termination.
CSE 5731 Lecture 19 Reasoning about Time and Change CSE 573 Artificial Intelligence I Henry Kautz Fall 2001 Salvador Dali, Persistence of Memory.
CSE 5731 Lecture 21 State-Space Search vs. Constraint- Based Planning CSE 573 Artificial Intelligence I Henry Kautz Fall 2001.
1 Planning. R. Dearden 2007/8 Exam Format  4 questions You must do all questions There is choice within some of the questions  Learning Outcomes: 1.Explain.
Search in the semantic domain. Some definitions atomic formula: smallest formula possible (no sub- formulas) literal: atomic formula or negation of an.
Last time Proof-system search ( ` ) Interpretation search ( ² ) Quantifiers Equality Decision procedures Induction Cross-cutting aspectsMain search strategy.
1 BLACKBOX: A New Paradigm for Planning Bart Selman Cornell University.
1 CSE 417: Algorithms and Computational Complexity Winter 2001 Lecture 24 Instructor: Paul Beame.
1 BLACKBOX: A New Approach to the Application of Theorem Proving to Problem Solving Bart Selman Cornell University Joint work with Henry Kautz AT&T Labs.
Reviving Integer Programming Approaches for AI Planning: A Branch-and-Cut Framework Thomas Vossen Leeds School of Business University of Colorado at Boulder.
CS444-Autumn of 20 Planning as Satisfiability Henry Kautz University of Rochester in collaboration with Bart Selman and Jöerg Hoffmann.
Logical Foundations of AI Planning as Satisfiability Clause Learning Backdoors to Hardness Henry Kautz.
Jonathon Doran. The Planning Domain A domain describes the objects, facts, and actions in the universe. We may have a box and a table in our universe.
Performing Bayesian Inference by Weighted Model Counting Tian Sang, Paul Beame, and Henry Kautz Department of Computer Science & Engineering University.
SAT and SMT solvers Ayrat Khalimov (based on Georg Hofferek‘s slides) AKDV 2014.
10/9/2015COSC , Lecture 51 Real-Time Systems, COSC , Lecture 5 Stefan Andrei.
1 Chapter 7 Propositional Satisfiability Techniques.
1 Planning as Satisfiability Alan Fern * * Based in part on slides by Stuart Russell and Dana Nau  Review of propositional logic (see chapter 7)  Planning.
Explorations in Artificial Intelligence Prof. Carla P. Gomes Module 3 Logic Representations (Part 2)
1 The LPSAT Engine and its Application to Metric Planning Steve Wolfman University of Washington CS&E Advisor: Dan Weld.
CS344: Introduction to Artificial Intelligence Lecture: Herbrand’s Theorem Proving satisfiability of logic formulae using semantic trees (from Symbolic.
CS621: Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 28– Interpretation; Herbrand Interpertation 30 th Sept, 2010.
HW #1. Due Mar 22 Midnight Verify the following program using SAT solver 1. Translate the program into a SSA form 2. Create a Boolean formula from.
Planning as Propositional Satisfiabililty Brian C. Williams Oct. 30 th, J/6.834J GSAT, Graphplan and WalkSAT Based on slides from Bart Selman.
Explorations in Artificial Intelligence Prof. Carla P. Gomes Module Logic Representations.
Cost-Optimal Planning with Constraints and Preferences in Large State Spaces Stefan Edelkamp, Shahid Jabbar, Mohammed Nazih University of Dortmund.
NP-Complete Problems. Running Time v.s. Input Size Concern with problems whose complexity may be described by exponential functions. Tractable problems.
CS 5411 Compilation Approaches to AI Planning 1 José Luis Ambite* Some slides are taken from presentations by Kautz and Selman. Please visit their.
IP addresses IPv4 and IPv6. IP addresses (IP=Internet Protocol) Each computer connected to the Internet must have a unique IP address.
Introduction to Artificial Intelligence Class 1 Planning & Search Henry Kautz Winter 2007.
Graphplan/ SATPlan Chapter Some material adapted from slides by Jean-Claude Latombe / Lise Getoor.
CSE 589 Part V One of the symptoms of an approaching nervous breakdown is the belief that one’s work is terribly important. Bertrand Russell.
Tommy Messelis * Stefaan Haspeslagh Patrick De Causmaecker *
Planning as Satisfiability (SAT-Plan). SAT-Plan Translate the planning problem into a satisfiability problem for length n of Plan garb 0 (proposition)present.
Semantics of Predicate Calculus For the propositional calculus, an interpretation was simply an assignment of truth values to the proposition letters of.
Planning 2 (SATPLAN) CSE 573. © Daniel S. Weld 2 Ways to make “plans” Generative Planning Reason from first principles (knowledge of actions) Requires.
AAAI of 20 Deconstructing Planning as Satisfiability Henry Kautz University of Rochester in collaboration with Bart Selman and Jöerg Hoffmann.
Local Search Methods for SAT Geoffrey Levine March 11, 2004.
Consider the task get milk, bananas, and a cordless drill.
PREDICATES AND QUANTIFIERS COSC-1321 Discrete Structures 1.
CSE 421 Algorithms Richard Anderson Lecture 27 NP-Completeness Proofs.
Dana Nau: Lecture slides for Automated Planning Licensed under the Creative Commons Attribution-NonCommercial-ShareAlike License:
CSE 332: NP Completeness, Part II Richard Anderson Spring 2016.
Satisfiability Modulo Theories and DPLL(T) Andrew Reynolds March 18, 2015.
© 2012 IBM Corporation Perfect Hashing and CNF Encodings of Cardinality Constraints Yael Ben-Haim Alexander Ivrii Oded Margalit Arie Matsliah SAT 2012.
Richard Anderson Lecture 26 NP-Completeness
Planning as Satisfiability
Richard Anderson Lecture 26 NP-Completeness
Local Search Strategies: From N-Queens to Walksat
Consider the task get milk, bananas, and a cordless drill
Watermarking of SAT Using Combinatorial Isolation Lemmas
CSE P573 Introduction to Artificial Intelligence Class 1 Planning & Search INTRODUCE MYSELF Relative new faculty member, but 20 years in AI research.
Planning as Satisfiability with Blackbox
Lecture 24 NP-Complete Problems
VoIP Final Report Proposal Spring 2004
Richard Anderson Lecture 25 NP-Completeness
NP-Completeness Proofs
Planning as Satisfiability
Introduction to the Boolean Satisfiability Problem
Introduction to the Boolean Satisfiability Problem
NP-Complete Problems.
Presentation transcript:

Planning as Satisfiability cse 473 sp 06

SATPLAN cnf formula satisfying model plan mapping length STRIPS problem description SAT engine encoder interpreter

Translating STRIPS Ground action = a STRIPS operator with constants assigned to all of its parameters Ground fluent = a precondition or effect of a ground action operator: Fly(a,b) precondition: At(a), Fueled effect: At(b), ~At(a), ~Fueled constants: NY, Boston, Seattle Ground actions: Fly(NY,Boston), Fly(NY,Seattle), Fly(Boston,NY), Fly(Boston,Seattle), Fly(Seattle,NY), Fly(Seattle,Boston) Ground fluents: Fueled, At(NY), At(Boston), At(Seattle)

Translating STRIPS Ground action = a STRIPS operator with constants assigned to all of its parameters Ground fluent = a precondition or effect of a ground action operator: Fly(a,b) precondition: At(a), Fueled effect: At(b), ~At(a), ~Fueled constants: NY, Boston, Seattle Ground actions: Fly(NY,Boston), Fly(NY,Seattle), Fly(Boston,NY), Fly(Boston,Seattle), Fly(Seattle,NY), Fly(Seattle,Boston) Ground fluents: Fueled, At(NY), At(Boston), At(Seattle)

Clause Schemas A large set of clauses can be represented by a schema

SAT Encoding Time is sequential and discrete –Represented by integers –Actions occur instantaneously at a time point –Each fluent is true or false at each time point If an action occurs at time i, then its preconditions must hold at time i If an action occurs at time i, then its effects must hold at time i+1 If a fluent changes its truth value from time i to time i+1, one of the actions with the new value as an effect must have occurred at time i Two conflicting actions cannot occur at the same time The initial state holds at time 0, and the goals hold at a given final state K

SAT Encoding If an action occurs at time i, then its preconditions must hold at time i If an action occurs at time i, then its effects must hold at time i+1

SAT Encoding If a fluent changes its truth value from time i to time i+1, one of the actions with the new value as an effect must have occurred at time i Like “for”, but connects propositions with OR

SAT Encoding If a fluent changes its truth value from time i to time i+1, one of the actions with the new value as an effect must have occurred at time i

The IPC-4 Domains Airport: control the ground traffic [Hoffmann & Trüg] Pipesworld: control oil product flow in a pipeline network [Liporace & Hoffmann] Promela: find deadlocks in communication protocols [Edelkamp] PSR: resupply lines in a faulty electricity network [Thiebaux & Hoffmann] Satellite & Settlers [Fox & Long], additional Satellite versions with time windows for sending data [Hoffmann] UMTS: set up applications for mobile terminals [Edelkamp & Englert]

The Competitors: Optimal planners

PSR

Dining Philosophers

Airport

Hosted at International Conference on Automated Planning and Scheduling Whistler, June 6, 2004 Stefan Edelkamp Jörg Hoffmann IPC-4 Co-Chairs Classical Part Performance Award: 1st Prize, Optimal Track Henry Kautz, David Roznyai, Farhad Teydaye-Saheli, Shane Neth and Michael Lindmark “SATPLAN04”