Situation Calculus. Dr. Neil T. Dantam. Spring CSCI-498/598 RPM, Colorado School of Mines

Size: px
Start display at page:

Download "Situation Calculus. Dr. Neil T. Dantam. Spring CSCI-498/598 RPM, Colorado School of Mines"

Transcription

1 Situation alculus Dr. Neil T. Dantam SI-498/598 RPM, olorado School of Mines Spring 2018 Dantam (Mines SI, RPM) Situation alculus Spring / 24

2 Logic and Planning Outline Logic and Planning locksworld Domain Planning Domain Definition Language (PDDL) Operators Facts Planning pproaches Heuristic Search onstraint-ased Planning Dantam (Mines SI, RPM) Situation alculus Spring / 24

3 Logic and Planning Logical alculi Propositional alculus: oolean variables (propositions) Logical Operators (,,, =,, ) Predicate alculus: Extends the propositional calculus with: Objects Predicates Functions Quantifiers Situation alculus: Extends the predicate calculus to model actions that change state: Fluents ctions Dantam (Mines SI, RPM) Situation alculus Spring / 24

4 Logic and Planning Situation alculus Predicate alculus + changing state: Fluents ctions Synonym for state variables of the system Example: closed (suitcase) contains (suitcase, laptop) From Latin fluere meaning to flow. Elements: Label: Name / arguments Precondition: States where the action is valid Effect: Result of the action Example: Label: open (suitcase) Precondition: closed (suitcase) Effect: closed (suitcase) Dantam (Mines SI, RPM) Situation alculus Spring / 24

5 Logic and Planning Illustration State/ction Sequence open (suitcase) insert (suitcase, banana) closed (suitcase) contains (suitcase, laptop) contains (suitcase, banana) remove (suitcase, laptop) close (suitcase) Dantam (Mines SI, RPM) Situation alculus Spring / 24

6 Logic and Planning Illustration utomaton open (suitcase) closed (suitcase) contains (suitcase, laptop) contains (suitcase, banana) closed (suitcase) contains (suitcase, laptop) contains (suitcase, banana) close (suitcase) remove (suitcase, laptop) closed (suitcase) contains (suitcase, laptop) contains (suitcase, banana)... Dantam (Mines SI, RPM) Situation alculus Spring / 24

7 Logic and Planning Exercise: State Space Size Objects: = {suitcase, backpack} = {laptop, banana, book} Predicate: contains : Fluents: Dantam (Mines SI, RPM) Situation alculus Spring / 24

8 Logic and Planning Transition System State Space: Q = f 0 f 1... f m, for each fluent f i ctions: U = {a 0,..., a n } Transitions: δ : Q U Q, where for δ(q 0, a) = q 1, q 0 satisfies the precondition of a q 1 is the effect of a applied to q 0 Start: q 0 Q is the initial state Goal: G Q is the set of goal states Dantam (Mines SI, RPM) Situation alculus Spring / 24

9 locksworld Domain Outline Logic and Planning locksworld Domain Planning Domain Definition Language (PDDL) Operators Facts Planning pproaches Heuristic Search onstraint-ased Planning Dantam (Mines SI, RPM) Situation alculus Spring / 24

10 locksworld Domain Planning Problem Start Goal Dantam (Mines SI, RPM) Situation alculus Spring / 24

11 locksworld Domain First-Order Logic Description onstants:,, Predicates: on (?x,?y) clear (?x) ontable (?x) handempty () Fluents: clear () clear () ontable () ontable () handempty () Dantam (Mines SI, RPM) Situation alculus Spring / 24

12 locksworld Domain Task Language pick-up () put-down () unstack (, ) pick-up () stack (, ) put-down () pick-up () put-down ()... Dantam (Mines SI, RPM) Situation alculus Spring / 24

13 locksworld Domain Example: pick-up (?x) Precondition: ontable (?x) clear (?x) handempty () Effect: ontable (?x) clear (?x) handempty () holding (?x) pick-up () ontable () ontable () on (, ) clear () clear () handempty () ontable () ontable () on (, ) clear () clear () handempty () holding () Dantam (Mines SI, RPM) Situation alculus Spring / 24

14 locksworld Domain Exercise: unstack (?x,?y) Precondition: on (?x,?y) clear (?x) handempty () Effect: on (?x,?y) clear (?x) handempty () holding (?x) clear (?y) unstack (, ) ontable () ontable () on (, ) clear () clear () handempty () Dantam (Mines SI, RPM) Situation alculus Spring / 24

15 Planning Domain Definition Language (PDDL) Outline Logic and Planning locksworld Domain Planning Domain Definition Language (PDDL) Operators Facts Planning pproaches Heuristic Search onstraint-ased Planning Dantam (Mines SI, RPM) Situation alculus Spring / 24

16 Planning Domain Definition Language (PDDL) Operators Example: pick-up (?x) pick-up (?x) PDDL Precondition: ontable (?x) clear (?x) handempty () Effect: ontable (?x) clear (?x) handempty () holding (?x) ( : a c t i o n pick up : parameters (? x ) : p r e c o n d i t i o n ( and ( o n t a b l e? x ) ( c l e a r? x ) ( handempty ) ) : e f f e c t ( and ( not ( o n t a b l e? x ) ) ( not ( c l e a r? x ) ) ( not ( handempty ) ) ( h o l d i n g? x ) ) ) Dantam (Mines SI, RPM) Situation alculus Spring / 24

17 Planning Domain Definition Language (PDDL) Operators Exercise: unstack (?x,?y) unstack (?x,?y) PDDL Precondition: on (?x,?y) clear (?x) handempty () Effect: on (?x,?y) clear (?x) handempty () holding (?x) clear (?y) ( : a c t i o n u n s t a c k : parameters (? x? y ) : p r e c o n d i t i o n ( and ( on? x? y ) ( c l e a r? x ) ( handempty ) ) : e f f e c t ( and ( not ( on? x? y ) ) ( not ( c l e a r? x ) ) ( not ( handempty ) ) ( h o l d i n g? x ) ( c l e a r? y ) ) ) Dantam (Mines SI, RPM) Situation alculus Spring / 24

18 Planning Domain Definition Language (PDDL) Operators Full Operators File Dantam (Mines SI, RPM) Situation alculus Spring / 24

19 Planning Domain Definition Language (PDDL) Facts Example: PDDL Facts Start PDDL Goal ( d e f i n e ( problem sussman anomaly ) ( : domain b l o c k s ) ( : o b j e c t s a b c ) ( : i n i t ( on c a ) ( o n t a b l e a ) ( o n t a b l e b ) ( c l e a r c ) ( c l e a r b ) ( handempty ) ) ( : g o a l ( and ( on b c ) ( on a b ) ) ) ) Dantam (Mines SI, RPM) Situation alculus Spring / 24

20 Planning Domain Definition Language (PDDL) Facts Exercise: PDDL Facts Start PDDL Goal Dantam (Mines SI, RPM) Situation alculus Spring / 24

21 Planning pproaches Outline Logic and Planning locksworld Domain Planning Domain Definition Language (PDDL) Operators Facts Planning pproaches Heuristic Search onstraint-ased Planning Dantam (Mines SI, RPM) Situation alculus Spring / 24

22 Planning pproaches Heuristic Search Heuristic Search pick-up () unstack (, ) stack (, ) put-down () Dantam (Mines SI, RPM) Situation alculus Spring / 24

23 Planning pproaches onstraint-ased Planning onstraint-ased Planning aka STPlan pick-up (, s 0 ) pick-up (, s 0 ) = precondition at step i {}}{ ontable (?x, s 0 ) clear (?x, s 0 ) handempty (s 0 ) ontable (?x, s 1 ) clear (?x, s 1 ) handempty (s 1 ) holding (?x, s 1 ) }{{} effect at step i+1 Dantam (Mines SI, RPM) Situation alculus Spring / 24

24 Planning pproaches onstraint-ased Planning Summary Logic and Planning locksworld Domain Planning Domain Definition Language (PDDL) Operators Facts Planning pproaches Heuristic Search onstraint-ased Planning Dantam (Mines SI, RPM) Situation alculus Spring / 24

Application: Planning as Satisfiability (SATPlan) (Pre Lecture)

Application: Planning as Satisfiability (SATPlan) (Pre Lecture) Application: Planning as Satisfiability (SATPlan) (Pre Lecture) Dr. Neil T. Dantam CSCI-561, Colorado School of Mines Fall 2018 Dantam (Mines CSCI-561) Application: Planning as Satisfiability (SATPlan)

More information

Introduction on Situational Calculus

Introduction on Situational Calculus Introduction on Situational Calculus Fangkai Yang Department of Computer Sciences The University of Texas at Austin September 24, 2010 Fangkai Yang (fkyang@cs.utexas.edu) September 24, 2010 1 / 27 Outline

More information

Propositional Calculus

Propositional Calculus Propositional Calculus Dr. Neil T. Dantam CSCI-498/598 RPM, Colorado School of Mines Spring 2018 Dantam (Mines CSCI, RPM) Propositional Calculus Spring 2018 1 / 64 Calculus? Definition: Calculus A well

More information

Lecture 10: Feb. 11, 2015

Lecture 10: Feb. 11, 2015 CS324: AI (Situation Calculus & Reasoning.) Spring 2015 Lecturer: K.R. Chowdhary Lecture 10: Feb. 11, 2015 : Professor of CS (VF) Disclaimer: These notes have not been subjected to the usual scrutiny reserved

More information

Example: Robot Tasks. What is planning? Background: Situation Calculus. Issues in Planning I

Example: Robot Tasks. What is planning? Background: Situation Calculus. Issues in Planning I What is planning? q A plan is any hierarchical process in the organism that can control the order in which a sequence of operations is to be performed. [Miller, Galanter and Pribram 1960] q Planning can

More information

Part of this work we present an extension for Language PP and we show how this

Part of this work we present an extension for Language PP and we show how this Chapter 5 Planning problems Currently, we can find different definitions for Planning. For instance, McCarthy defines planning in [25] as the restricted problem of finding a finite sequence of actions

More information

Teaching Computers To Make Plans

Teaching Computers To Make Plans November 14, 2018 Millersville University UNIV 103 Making lock-moving Plans What Is True Now? There are blocks and on a table and a block on top of block Making lock-moving Plans What Is True Now? There

More information

Compiling Causal Theories to Successor State Axioms and STRIPS-Like Systems

Compiling Causal Theories to Successor State Axioms and STRIPS-Like Systems Journal of Artificial Intelligence Research 19 (2003) 279-314 Submitted 09/02; published 10/03 Compiling Causal Theories to Successor State Axioms and STRIPS-Like Systems Fangzhen Lin Department of Computer

More information

Planning'&'Scheduling!

Planning'&'Scheduling! Planning'&'Scheduling! Roman Barták Department of Theoretical Computer Science and Mathematical Logic State Space Planning Just!to!recall! Planning'problem'P"is"a"triple"(Σ,s 0,g)" Σ"is"a"planning'domain'describing"possible"world"states"and"

More information

From Causal Theories to Successor State Axioms and STRIPS-Like Systems

From Causal Theories to Successor State Axioms and STRIPS-Like Systems From: AAAI-00 Proceedings. Copyright 2000, AAAI (www.aaai.org). All rights reserved. From Causal Theories to Successor State Axioms and STRIPS-Like Systems Fangzhen Lin (flin@cs.ust.hk) Department of Computer

More information

Today s Lecture. Lecture 4: Formal SE. Some Important Points. Formal Software Engineering. Introduction to Formal Software Engineering

Today s Lecture. Lecture 4: Formal SE. Some Important Points. Formal Software Engineering. Introduction to Formal Software Engineering Today s Lecture Lecture 4: Formal SE Introduction to Formal Software Engineering Discuss Models Discuss Formal Notations Kenneth M. Anderson Foundations of Software Engineering CSCI 5828 - Spring Semester,

More information

Context-Free Languages (Pre Lecture)

Context-Free Languages (Pre Lecture) Context-Free Languages (Pre Lecture) Dr. Neil T. Dantam CSCI-561, Colorado School of Mines Fall 2017 Dantam (Mines CSCI-561) Context-Free Languages (Pre Lecture) Fall 2017 1 / 34 Outline Pumping Lemma

More information

Pushdown Automata (Pre Lecture)

Pushdown Automata (Pre Lecture) Pushdown Automata (Pre Lecture) Dr. Neil T. Dantam CSCI-561, Colorado School of Mines Fall 2017 Dantam (Mines CSCI-561) Pushdown Automata (Pre Lecture) Fall 2017 1 / 41 Outline Pushdown Automata Pushdown

More information

Reasoning about action & change. Frame problem(s) Persistence problem. Situation calculus (McCarthy)

Reasoning about action & change. Frame problem(s) Persistence problem. Situation calculus (McCarthy) Reasoning about action & change Frame problem and related problems Situation calculus, event calculus, fluent calculus Planning Relation with non-monotonic reasoning 170 Frame problem(s) Persistence problem

More information

Classical Planning Chapter 10. Mausam (Based on slides of Dan Weld, Marie desjardins)

Classical Planning Chapter 10. Mausam (Based on slides of Dan Weld, Marie desjardins) Classical Planning Chapter 10 Mausam (ased on slides of Dan Weld, Marie desjardins) Given Planning a logical description of the world states, a logical description of a set of possible actions, a logical

More information

THE AUSTRALIAN NATIONAL UNIVERSITY Second Semester COMP2600/COMP6260 (Formal Methods for Software Engineering)

THE AUSTRALIAN NATIONAL UNIVERSITY Second Semester COMP2600/COMP6260 (Formal Methods for Software Engineering) THE AUSTRALIAN NATIONAL UNIVERSITY Second Semester 2016 COMP2600/COMP6260 (Formal Methods for Software Engineering) Writing Period: 3 hours duration Study Period: 15 minutes duration Permitted Materials:

More information

Action Planning (Where logic-based representation of knowledge makes search problems more interesting)

Action Planning (Where logic-based representation of knowledge makes search problems more interesting) ction Planning (Where logic-based representation of knowledge makes search problems more interesting) R&N: hap. 10.3, hap. 11, Sect. 11.1 4 (2 nd edition of the book a pdf of chapter 11 can be found on

More information

Logic & Logic Agents Chapter 7 (& background)

Logic & Logic Agents Chapter 7 (& background) Lecture Notes, Advanced Artificial Intelligence (SCOM7341) Sina Institute, University of Birzeit 2 nd Semester, 2012 Advanced Artificial Intelligence (SCOM7341) Logic & Logic Agents Chapter 7 (& background)

More information

Knowledge-based systems

Knowledge-based systems CS 750 Foundations of I Lecture 6 Knowledge-based systems Milos Hauskrecht milos@cs.pitt.edu 539 Sennott Square dministration announcements Midterm: Thursda October 6, 07 In-class Closed book What does

More information

Predicate Calculus lecture 1

Predicate Calculus lecture 1 Predicate Calculus lecture 1 Section 1.3 Limitation of Propositional Logic Consider the following reasoning All cats have tails Gouchi is a cat Therefore, Gouchi has tail. MSU/CSE 260 Fall 2009 1 MSU/CSE

More information

On the Complexity of Domain-Independent Planning

On the Complexity of Domain-Independent Planning On the Complexity of Domain-Independent Planning Kutluhan Erol Dana S. Nau V. S. Subrahmanian kutluhan@cs.umd.edu nau@cs.umd.edu vs@cs.umd.edu Computer Science Department University of Maryland College

More information

Complexity (Pre Lecture)

Complexity (Pre Lecture) Complexity (Pre Lecture) Dr. Neil T. Dantam CSCI-561, Colorado School of Mines Fall 2018 Dantam (Mines CSCI-561) Complexity (Pre Lecture) Fall 2018 1 / 70 Why? What can we always compute efficiently? What

More information

Finite Automata. Dr. Neil T. Dantam. Fall CSCI-561, Colorado School of Mines. Dantam (Mines CSCI-561) Finite Automata Fall / 43

Finite Automata. Dr. Neil T. Dantam. Fall CSCI-561, Colorado School of Mines. Dantam (Mines CSCI-561) Finite Automata Fall / 43 Finite Automata Dr. Neil T. Dantam CSCI-561, Colorado School of Mines Fall 2018 Dantam (Mines CSCI-561) Finite Automata Fall 2018 1 / 43 Outline Languages Review Traffic Light Example Deterministic Finite

More information

Planning: situation calculus

Planning: situation calculus CS 57 Introduction to I Lecture 8 Planning: situation calculus Milos Hauskrecht milos@cs.pitt.edu 5329 Sennott Square utomated reasoning systems Examples and main differences: Theorem provers Prove sentences

More information

Regular Expressions (Pre Lecture)

Regular Expressions (Pre Lecture) Regular Expressions (Pre Lecture) Dr. Neil T. Dantam CSCI-561, Colorado School of Mines Fall 2017 Dantam (Mines CSCI-561) Regular Expressions (Pre Lecture) Fall 2017 1 / 39 Regular Expressions Outline

More information

CSE 105 Homework 5 Due: Monday November 13, Instructions. should be on each page of the submission.

CSE 105 Homework 5 Due: Monday November 13, Instructions. should be on each page of the submission. CSE 05 Homework 5 Due: Monday November 3, 207 Instructions Upload a single file to Gradescope for each group. should be on each page of the submission. All group members names and PIDs Your assignments

More information

Spring 2015 Program Analysis and Verification. Lecture 4: Axiomatic Semantics I. Roman Manevich Ben-Gurion University

Spring 2015 Program Analysis and Verification. Lecture 4: Axiomatic Semantics I. Roman Manevich Ben-Gurion University Spring 2015 Program Analysis and Verification Lecture 4: Axiomatic Semantics I Roman Manevich Ben-Gurion University Agenda Basic concepts of correctness Axiomatic semantics (pages 175-183) Hoare Logic

More information

Structure and. Inference in. Classical Planning

Structure and. Inference in. Classical Planning Structure and Inference in Classical Planning i Structure and Inference in Classical Planning Nir Lipovetzky Computing and Information Systems The University of Melbourne Published by AI Access AI Access

More information

Propositional Logic. Logic. Propositional Logic Syntax. Propositional Logic

Propositional Logic. Logic. Propositional Logic Syntax. Propositional Logic Propositional Logic Reading: Chapter 7.1, 7.3 7.5 [ased on slides from Jerry Zhu, Louis Oliphant and ndrew Moore] Logic If the rules of the world are presented formally, then a decision maker can use logical

More information

Reasoning about State Constraints in the Situation Calculus

Reasoning about State Constraints in the Situation Calculus Reasoning about State Constraints in the Situation Calculus Joint work with Naiqi Li and Yi Fan Yongmei Liu Dept. of Computer Science Sun Yat-sen University Guangzhou, China Presented at IRIT June 26,

More information

2. Introduction to commutative rings (continued)

2. Introduction to commutative rings (continued) 2. Introduction to commutative rings (continued) 2.1. New examples of commutative rings. Recall that in the first lecture we defined the notions of commutative rings and field and gave some examples of

More information

Argument. whenever all the assumptions are true, then the conclusion is true. If today is Wednesday, then yesterday is Tuesday. Today is Wednesday.

Argument. whenever all the assumptions are true, then the conclusion is true. If today is Wednesday, then yesterday is Tuesday. Today is Wednesday. Logic and Proof Argument An argument is a sequence of statements. All statements but the first one are called assumptions or hypothesis. The final statement is called the conclusion. An argument is valid

More information

1. Propositions: Contrapositives and Converses

1. Propositions: Contrapositives and Converses Preliminaries 1 1. Propositions: Contrapositives and Converses Given two propositions P and Q, the statement If P, then Q is interpreted as the statement that if the proposition P is true, then the statement

More information

Lisp Introduction. Dr. Neil T. Dantam. Spring CSCI-498/598 RPM, Colorado School of Mines. Dantam (Mines CSCI, RPM) Lisp Spring / 88

Lisp Introduction. Dr. Neil T. Dantam. Spring CSCI-498/598 RPM, Colorado School of Mines. Dantam (Mines CSCI, RPM) Lisp Spring / 88 Lisp Introduction Dr. Neil T. Dantam CSCI-498/598 RPM, Colorado School of Mines Spring 28 Dantam (Mines CSCI, RPM) Lisp Spring 28 / 88 Outline Lisp Common Lisp by Example Implementation Details Typing

More information

3.4 Set Operations Given a set A, the complement (in the Universal set U) A c is the set of all elements of U that are not in A. So A c = {x x / A}.

3.4 Set Operations Given a set A, the complement (in the Universal set U) A c is the set of all elements of U that are not in A. So A c = {x x / A}. 3.4 Set Operations Given a set, the complement (in the niversal set ) c is the set of all elements of that are not in. So c = {x x /. (This type of picture is called a Venn diagram.) Example 39 Let = {1,

More information

On Moving Objects in Dynamic Domains

On Moving Objects in Dynamic Domains On Moving Objects in Dynamic Domains Fangzhen Lin Department of Computer Science Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong Kong Abstract In the physical world, an object

More information

Having read this workbook you should be able to: design a logic circuit from its Boolean equation or truth table.

Having read this workbook you should be able to: design a logic circuit from its Boolean equation or truth table. Objectives Having read this workbook you should be able to: analyse a given logic circuit by deriving its oolean equation and completing its truth table. design a logic circuit from its oolean equation

More information

Compiling and Executing PDDL in Picat

Compiling and Executing PDDL in Picat Compiling and Executing PDDL in Picat Marco De Bortoli, Roman Bartak, Agostino Dovier, Neng-Fa Zhou Università degli Studi di Udine CILC 2016 Picat CILC 2016 1 / 21 Outline Introduction to classical planning

More information

AI Planning. goal states B Definition Example Matrices Reachability Algorithm. Transition systems. Succinct TS. initial state

AI Planning. goal states B Definition Example Matrices Reachability Algorithm. Transition systems. Succinct TS. initial state C goal states B Matrices Reachability Algorithm D A E F initial state Formalization of the dynamics of the world/application A transition system is S, I, {a 1,..., a n }, G where S is a finite set of states

More information

Answers to the CSCE 551 Final Exam, April 30, 2008

Answers to the CSCE 551 Final Exam, April 30, 2008 Answers to the CSCE 55 Final Exam, April 3, 28. (5 points) Use the Pumping Lemma to show that the language L = {x {, } the number of s and s in x differ (in either direction) by at most 28} is not regular.

More information

Example. Logic. Logical Statements. Outline of logic topics. Logical Connectives. Logical Connectives

Example. Logic. Logical Statements. Outline of logic topics. Logical Connectives. Logical Connectives Logic Logic is study of abstract reasoning, specifically, concerned with whether reasoning is correct. Logic focuses on relationship among statements as opposed to the content of any particular statement.

More information

Finite Automata. Dr. Neil T. Dantam. Fall CSCI-561, Colorado School of Mines. Dantam (Mines CSCI-561) Finite Automata Fall / 35

Finite Automata. Dr. Neil T. Dantam. Fall CSCI-561, Colorado School of Mines. Dantam (Mines CSCI-561) Finite Automata Fall / 35 Finite Automata Dr. Neil T. Dantam CSCI-561, Colorado School of Mines Fall 2017 Dantam (Mines CSCI-561) Finite Automata Fall 2017 1 / 35 Outline Dantam (Mines CSCI-561) Finite Automata Fall 2017 2 / 35

More information

King s College London

King s College London King s College London This paper is part of an examination of the College counting towards the award of a degree. Examinations are governed by the College Regulations under the authority of the Academic

More information

Formal Logic Lecture 11

Formal Logic Lecture 11 Faculty of Philosophy Formal Logic Lecture 11 Peter Smith Peter Smith: Formal Logic, Lecture 11 1 Outline Where next? Introducing PL trees Branching trees Peter Smith: Formal Logic, Lecture 11 2 Where

More information

Basic Logic and Proof Techniques

Basic Logic and Proof Techniques Chapter 3 Basic Logic and Proof Techniques Now that we have introduced a number of mathematical objects to study and have a few proof techniques at our disposal, we pause to look a little more closely

More information

Section 2.2 Set Operations. Propositional calculus and set theory are both instances of an algebraic system called a. Boolean Algebra.

Section 2.2 Set Operations. Propositional calculus and set theory are both instances of an algebraic system called a. Boolean Algebra. Section 2.2 Set Operations Propositional calculus and set theory are both instances of an algebraic system called a Boolean Algebra. The operators in set theory are defined in terms of the corresponding

More information

PROOFS IN PREDICATE LOGIC AND COMPLETENESS; WHAT DECIDABILITY MEANS HUTH AND RYAN 2.3, SUPPLEMENTARY NOTES 2

PROOFS IN PREDICATE LOGIC AND COMPLETENESS; WHAT DECIDABILITY MEANS HUTH AND RYAN 2.3, SUPPLEMENTARY NOTES 2 PROOFS IN PREDICATE LOGIC AND COMPLETENESS; WHAT DECIDABILITY MEANS HUTH AND RYAN 2.3, SUPPLEMENTARY NOTES 2 Neil D. Jones DIKU 2005 12 September, 2005 Some slides today new, some based on logic 2004 (Nils

More information

Discrete Mathematics

Discrete Mathematics Discrete Mathematics Jeremy Siek Spring 2010 Jeremy Siek Discrete Mathematics 1 / 24 Outline of Lecture 3 1. Proofs and Isabelle 2. Proof Strategy, Forward and Backwards Reasoning 3. Making Mistakes Jeremy

More information

Define logic [fuc, ] Natural Deduction. Natural Deduction. Preamble. Akim D le June 14, 2016

Define logic [fuc, ] Natural Deduction. Natural Deduction. Preamble. Akim D le June 14, 2016 Define logic [fuc, ] Natural Deduction kim Demaille akim@lrde.epita.fr EPIT École Pour l Informatique et les Techniques vancées June 14, 2016. Demaille Natural Deduction 2 / 49 Natural Deduction Preamble

More information

2 Truth Tables, Equivalences and the Contrapositive

2 Truth Tables, Equivalences and the Contrapositive 2 Truth Tables, Equivalences and the Contrapositive 12 2 Truth Tables, Equivalences and the Contrapositive 2.1 Truth Tables In a mathematical system, true and false statements are the statements of the

More information

Recall that the expression x > 3 is not a proposition. Why?

Recall that the expression x > 3 is not a proposition. Why? Predicates and Quantifiers Predicates and Quantifiers 1 Recall that the expression x > 3 is not a proposition. Why? Notation: We will use the propositional function notation to denote the expression "

More information

Spring 2016 Program Analysis and Verification. Lecture 3: Axiomatic Semantics I. Roman Manevich Ben-Gurion University

Spring 2016 Program Analysis and Verification. Lecture 3: Axiomatic Semantics I. Roman Manevich Ben-Gurion University Spring 2016 Program Analysis and Verification Lecture 3: Axiomatic Semantics I Roman Manevich Ben-Gurion University Warm-up exercises 1. Define program state: 2. Define structural semantics configurations:

More information

Today. Proof using contrapositive. Compound Propositions. Manipulating Propositions. Tautology

Today. Proof using contrapositive. Compound Propositions. Manipulating Propositions. Tautology 1 Math/CSE 1019N: Discrete Mathematics for Computer Science Winter 2007 Suprakash Datta datta@cs.yorku.ca Office: CSEB 3043 Phone: 416-736-2100 ext 77875 Course page: http://www.cs.yorku.ca/course/1019

More information

Writing proofs for MATH 61CM, 61DM Week 1: basic logic, proof by contradiction, proof by induction

Writing proofs for MATH 61CM, 61DM Week 1: basic logic, proof by contradiction, proof by induction Writing proofs for MATH 61CM, 61DM Week 1: basic logic, proof by contradiction, proof by induction written by Sarah Peluse, revised by Evangelie Zachos and Lisa Sauermann September 27, 2016 1 Introduction

More information

Section 2.1: Introduction to the Logic of Quantified Statements

Section 2.1: Introduction to the Logic of Quantified Statements Section 2.1: Introduction to the Logic of Quantified Statements In the previous chapter, we studied a branch of logic called propositional logic or propositional calculus. Loosely speaking, propositional

More information

Problem Points Score Total 100

Problem Points Score Total 100 Exam 1 A. Miller Spring 2005 Math 240 1 Show all work. No notes, no books, no calculators, no cell phones, no pagers, no electronic devices of any kind. Name Circle your Discussion Section: DIS 303 12:05p

More information

Predicate Logic: Sematics Part 1

Predicate Logic: Sematics Part 1 Predicate Logic: Sematics Part 1 CS402, Spring 2018 Shin Yoo Predicate Calculus Propositional logic is also called sentential logic, i.e. a logical system that deals with whole sentences connected with

More information

Discrete Mathematics Exam File Spring Exam #1

Discrete Mathematics Exam File Spring Exam #1 Discrete Mathematics Exam File Spring 2008 Exam #1 1.) Consider the sequence a n = 2n + 3. a.) Write out the first five terms of the sequence. b.) Determine a recursive formula for the sequence. 2.) Consider

More information

Section Summary. Section 1.5 9/9/2014

Section Summary. Section 1.5 9/9/2014 Section 1.5 Section Summary Nested Quantifiers Order of Quantifiers Translating from Nested Quantifiers into English Translating Mathematical Statements into Statements involving Nested Quantifiers Translated

More information

Floyd-Hoare Style Program Verification

Floyd-Hoare Style Program Verification Floyd-Hoare Style Program Verification Deepak D Souza Department of Computer Science and Automation Indian Institute of Science, Bangalore. 9 Feb 2017 Outline of this talk 1 Overview 2 Hoare Triples 3

More information

Tractable Reasoning with Incomplete First-Order Knowledge in Dynamic Systems with Context-Dependent Actions

Tractable Reasoning with Incomplete First-Order Knowledge in Dynamic Systems with Context-Dependent Actions Tractable Reasoning with Incomplete First-Order Knowledge in Dynamic Systems with Context-Dependent Actions Yongmei Liu and Hector J. Levesque Department of Computer Science University of Toronto Toronto,

More information

CMU-Q Lecture 5: Classical Planning Factored Representations STRIPS. Teacher: Gianni A. Di Caro

CMU-Q Lecture 5: Classical Planning Factored Representations STRIPS. Teacher: Gianni A. Di Caro CMU-Q 15-381 Lecture 5: Classical Planning Factored Representations STRIPS Teacher: Gianni A. Di Caro AUTOMATED PLANNING: FACTORED STATE REPRESENTATIONS 2 PLANNING, FOR (MORE) COMPLEX WORLDS Searching

More information

Fuzzy Sets and Systems. Lecture 4 (Fuzzy Logic) Bu- Ali Sina University Computer Engineering Dep. Spring 2010

Fuzzy Sets and Systems. Lecture 4 (Fuzzy Logic) Bu- Ali Sina University Computer Engineering Dep. Spring 2010 Fuzzy Sets and Systems Lecture 4 (Fuzzy Logic) Bu- Ali Sina University Computer Engineering Dep. Spring 2010 Outline Fuzzy Logic Classical logic- an overview Multi-valued logic Fuzzy logic Fuzzy proposition

More information

CPSC 121: Models of Computation

CPSC 121: Models of Computation CPSC 121: Models of Computation Unit 6 Rewriting Predicate Logic Statements Based on slides by Patrice Belleville and Steve Wolfman Coming Up Pre-class quiz #7 is due Wednesday October 25th at 9:00 pm.

More information

THE AUSTRALIAN NATIONAL UNIVERSITY Second Semester COMP2600 (Formal Methods in Software Engineering)

THE AUSTRALIAN NATIONAL UNIVERSITY Second Semester COMP2600 (Formal Methods in Software Engineering) THE AUSTRALIAN NATIONAL UNIVERSITY Second Semester 2007 COMP2600 (Formal Methods in Software Engineering) Writing Period: 3 hours duration Study Period: 15 minutes duration Permitted Materials: None Answer

More information

Introduction to Predicate Logic Part 1. Professor Anita Wasilewska Lecture Notes (1)

Introduction to Predicate Logic Part 1. Professor Anita Wasilewska Lecture Notes (1) Introduction to Predicate Logic Part 1 Professor Anita Wasilewska Lecture Notes (1) Introduction Lecture Notes (1) and (2) provide an OVERVIEW of a standard intuitive formalization and introduction to

More information

THE LOGIC OF QUANTIFIED STATEMENTS. Predicates and Quantified Statements I. Predicates and Quantified Statements I CHAPTER 3 SECTION 3.

THE LOGIC OF QUANTIFIED STATEMENTS. Predicates and Quantified Statements I. Predicates and Quantified Statements I CHAPTER 3 SECTION 3. CHAPTER 3 THE LOGIC OF QUANTIFIED STATEMENTS SECTION 3.1 Predicates and Quantified Statements I Copyright Cengage Learning. All rights reserved. Copyright Cengage Learning. All rights reserved. Predicates

More information

Exercises for the Logic Course

Exercises for the Logic Course Exercises for the Logic Course First Order Logic Course Web Page http://www.inf.unibz.it/~artale/dml/dml.htm Computer Science Free University of Bozen-Bolzano December 22, 2017 1 Exercises 1.1 Formalisation

More information

Classical Planning. Content. 16. dubna Definition Conceptual Model Methodology of Planners STRIPS PDDL

Classical Planning. Content. 16. dubna Definition Conceptual Model Methodology of Planners STRIPS PDDL Classical Planning Radek Mařík CVUT FEL, K13132 16. dubna 24 Radek Mařík (marikr@fel.cvut.cz) Classical Planning 16. dubna 24 1 / 77 Content 1 Concept of AI Planning Definition Conceptual Model Methodology

More information

In Defense of PDDL Axioms

In Defense of PDDL Axioms Sylvie Thiébaux Computer Sciences Laboratory The Australian National University Canberra, ACT 0200, Australia Sylvie.Thiebaux@anu.edu.au In Defense of PDDL Axioms Jörg Hoffmann and Bernhard Nebel Institut

More information

A Logic Primer. Stavros Tripakis University of California, Berkeley. Stavros Tripakis (UC Berkeley) EE 144/244, Fall 2014 A Logic Primer 1 / 35

A Logic Primer. Stavros Tripakis University of California, Berkeley. Stavros Tripakis (UC Berkeley) EE 144/244, Fall 2014 A Logic Primer 1 / 35 EE 144/244: Fundamental Algorithms for System Modeling, Analysis, and Optimization Fall 2014 A Logic Primer Stavros Tripakis University of California, Berkeley Stavros Tripakis (UC Berkeley) EE 144/244,

More information

Homework 5 - Solution

Homework 5 - Solution DCP3122 Introduction to Formal Languages, Spring 2015 Instructor: Prof. Wen-Guey Tzeng Homework 5 - Solution 5-May-2015 Due: 18-May-2015 1. Given Σ = {a, b, c}, find an NPDA that accepts L = {a n b n+m

More information

3. The Logic of Quantified Statements Summary. Aaron Tan August 2017

3. The Logic of Quantified Statements Summary. Aaron Tan August 2017 3. The Logic of Quantified Statements Summary Aaron Tan 28 31 August 2017 1 3. The Logic of Quantified Statements 3.1 Predicates and Quantified Statements I Predicate; domain; truth set Universal quantifier,

More information

TECHNISCHE UNIVERSITEIT EINDHOVEN Faculteit Wiskunde en Informatica. Final examination Logic & Set Theory (2IT61/2IT07/2IHT10) (correction model)

TECHNISCHE UNIVERSITEIT EINDHOVEN Faculteit Wiskunde en Informatica. Final examination Logic & Set Theory (2IT61/2IT07/2IHT10) (correction model) TECHNISCHE UNIVERSITEIT EINDHOVEN Faculteit Wiskunde en Informatica Final examination Logic & Set Theory (2IT61/2IT07/2IHT10) (correction model) Thursday October 29, 2015, 9:00 12:00 hrs. (2) 1. Determine

More information

Predicate Calculus - Syntax

Predicate Calculus - Syntax Predicate Calculus - Syntax Lila Kari University of Waterloo Predicate Calculus - Syntax CS245, Logic and Computation 1 / 26 The language L pred of Predicate Calculus - Syntax L pred, the formal language

More information

Logic. Knowledge Representation & Reasoning Mechanisms. Logic. Propositional Logic Predicate Logic (predicate Calculus) Automated Reasoning

Logic. Knowledge Representation & Reasoning Mechanisms. Logic. Propositional Logic Predicate Logic (predicate Calculus) Automated Reasoning Logic Knowledge Representation & Reasoning Mechanisms Logic Logic as KR Propositional Logic Predicate Logic (predicate Calculus) Automated Reasoning Logical inferences Resolution and Theorem-proving Logic

More information

Lecture 13: Soundness, Completeness and Compactness

Lecture 13: Soundness, Completeness and Compactness Discrete Mathematics (II) Spring 2017 Lecture 13: Soundness, Completeness and Compactness Lecturer: Yi Li 1 Overview In this lecture, we will prvoe the soundness and completeness of tableau proof system,

More information

Handout: Proof of the completeness theorem

Handout: Proof of the completeness theorem MATH 457 Introduction to Mathematical Logic Spring 2016 Dr. Jason Rute Handout: Proof of the completeness theorem Gödel s Compactness Theorem 1930. For a set Γ of wffs and a wff ϕ, we have the following.

More information

In Defense of PDDL Axioms

In Defense of PDDL Axioms In Defense of PDDL Axioms Sylvie Thiébaux Computer Sciences Laboratory, The Australian National University, Knowledge Representation & Reasoning Program, National ICT Australia, Canberra, ACT 0200, Australia

More information

Continuing the KRR Journey

Continuing the KRR Journey 1 Weeks 1 3 Week 4 Week 5 Week 6 Continuing the KRR Journey Propositional and first-order logic (FOL) Expressiveness and Decidability: Propositional logic is decidable, FOL is not. Classical planning problems:

More information

THE PREDICATE CALCULUS

THE PREDICATE CALCULUS 2 THE PREDICATE CALCULUS Slide 2.1 2.0 Introduction 2.1 The Propositional Calculus 2.2 The Predicate Calculus 2.3 Using Inference Rules to Produce Predicate Calculus Expressions 2.4 Application: A Logic-Based

More information

Introduction to Logic in Computer Science: Autumn 2006

Introduction to Logic in Computer Science: Autumn 2006 Introduction to Logic in Computer Science: Autumn 2006 Ulle Endriss Institute for Logic, Language and Computation University of Amsterdam Ulle Endriss 1 Plan for Today Today s class will be an introduction

More information

Collaborative Planning with Privacy

Collaborative Planning with Privacy Collaborative Planning with Privacy Protocol exchange May 7, 2007 Max Kanovich 1, Paul Rowe 2, Andre Scedrov 2 1 Quenn Mary, University of London 2 University of Pennsylvania Context Many examples of collaboration

More information

Grundlagen der Künstlichen Intelligenz

Grundlagen der Künstlichen Intelligenz Grundlagen der Künstlichen Intelligenz Formal models of interaction Daniel Hennes 27.11.2017 (WS 2017/18) University Stuttgart - IPVS - Machine Learning & Robotics 1 Today Taxonomy of domains Models of

More information

A Logic Primer. Stavros Tripakis University of California, Berkeley

A Logic Primer. Stavros Tripakis University of California, Berkeley EE 144/244: Fundamental Algorithms for System Modeling, Analysis, and Optimization Fall 2015 A Logic Primer Stavros Tripakis University of California, Berkeley Stavros Tripakis (UC Berkeley) EE 144/244,

More information

MATH202 Introduction to Analysis (2007 Fall and 2008 Spring) Tutorial Note #12

MATH202 Introduction to Analysis (2007 Fall and 2008 Spring) Tutorial Note #12 MATH0 Introduction to Analysis (007 Fall and 008 Spring) Tutorial Note #1 Limit (Part ) Recurrence Relation: Type 1: Monotone Sequence (Increasing/ Decreasing sequence) Theorem 1: Monotone Sequence Theorem

More information

Lecture 13: Turing Machine

Lecture 13: Turing Machine Lecture 13: Turing Machine Instructor: Ketan Mulmuley Scriber: Yuan Li February 19, 2015 Turing machine is an abstract machine which in principle can simulate any computation in nature. Church-Turing Thesis:

More information

Equivalence of Propositions

Equivalence of Propositions Equivalence of Propositions 1. Truth tables: two same columns 2. Sequence of logical equivalences: from one to the other using equivalence laws 1 Equivalence laws Table 6 & 7 in 1.2, some often used: Associative:

More information

INTRODUCTION TO PREDICATE LOGIC HUTH AND RYAN 2.1, 2.2, 2.4

INTRODUCTION TO PREDICATE LOGIC HUTH AND RYAN 2.1, 2.2, 2.4 INTRODUCTION TO PREDICATE LOGIC HUTH AND RYAN 2.1, 2.2, 2.4 Neil D. Jones DIKU 2005 Some slides today new, some based on logic 2004 (Nils Andersen), some based on kernebegreber (NJ 2005) PREDICATE LOGIC:

More information

spaghetti fish pie cake Ann X X Tom X X X Paul X X X

spaghetti fish pie cake Ann X X Tom X X X Paul X X X CmSc175 Discrete Mathematics Lesson 14: Set Relations 1. Introduction A college cafeteria line has two stations: main courses and desserts. The main course station offers spaghetti or fish; the dessert

More information

ES201 - Examination III Richards, North, Berry Fall November 2000 NAME BOX NUMBER

ES201 - Examination III Richards, North, Berry Fall November 2000 NAME BOX NUMBER ES201 - Examination III Richards, North, Berry Fall 2000-2001 2 November 2000 NAME BOX NUMBER Problem 1 Problem 2 ( 30 ) ( 30 ) Problem 3 ( 40 ) Total ( 100 ) INSTRUCTIONS Closed book/notes exam. (Unit

More information

Predicate Logic: Semantics

Predicate Logic: Semantics Predicate Logic: Semantics Alice Gao Lecture 13 Based on work by J. Buss, L. Kari, A. Lubiw, B. Bonakdarpour, D. Maftuleac, C. Roberts, R. Trefler, and P. Van Beek 1/34 Outline Semantics of Predicate Logic

More information

Theory of Computation

Theory of Computation Theory of Computation Prof. Michael Mascagni Florida State University Department of Computer Science 1 / 33 This course aims to cover... the development of computability theory using an extremely simple

More information

From Binary to Multiclass Classification. CS 6961: Structured Prediction Spring 2018

From Binary to Multiclass Classification. CS 6961: Structured Prediction Spring 2018 From Binary to Multiclass Classification CS 6961: Structured Prediction Spring 2018 1 So far: Binary Classification We have seen linear models Learning algorithms Perceptron SVM Logistic Regression Prediction

More information

2/18/14. What is logic? Proposi0onal Logic. Logic? Propositional Logic, Truth Tables, and Predicate Logic (Rosen, Sections 1.1, 1.2, 1.

2/18/14. What is logic? Proposi0onal Logic. Logic? Propositional Logic, Truth Tables, and Predicate Logic (Rosen, Sections 1.1, 1.2, 1. Logic? Propositional Logic, Truth Tables, and Predicate Logic (Rosen, Sections 1.1, 1.2, 1.3) TOPICS Propositional Logic Logical Operations Equivalences Predicate Logic CS160 - Spring Semester 2014 2 What

More information

Predicate Logic. CSE 191, Class Note 02: Predicate Logic Computer Sci & Eng Dept SUNY Buffalo

Predicate Logic. CSE 191, Class Note 02: Predicate Logic Computer Sci & Eng Dept SUNY Buffalo Predicate Logic CSE 191, Class Note 02: Predicate Logic Computer Sci & Eng Dept SUNY Buffalo c Xin He (University at Buffalo) CSE 191 Discrete Structures 1 / 22 Outline 1 From Proposition to Predicate

More information

What happens to the value of the expression x + y every time we execute this loop? while x>0 do ( y := y+z ; x := x:= x z )

What happens to the value of the expression x + y every time we execute this loop? while x>0 do ( y := y+z ; x := x:= x z ) Starter Questions Feel free to discuss these with your neighbour: Consider two states s 1 and s 2 such that s 1, x := x + 1 s 2 If predicate P (x = y + 1) is true for s 2 then what does that tell us about

More information

Schema Refinement: Other Dependencies and Higher Normal Forms

Schema Refinement: Other Dependencies and Higher Normal Forms Schema Refinement: Other Dependencies and Higher Normal Forms Spring 2018 School of Computer Science University of Waterloo Databases CS348 (University of Waterloo) Higher Normal Forms 1 / 14 Outline 1

More information

Propositional Logic: Deductive Proof & Natural Deduction Part 1

Propositional Logic: Deductive Proof & Natural Deduction Part 1 Propositional Logic: Deductive Proof & Natural Deduction Part 1 CS402, Spring 2016 Shin Yoo Deductive Proof In propositional logic, a valid formula is a tautology. So far, we could show the validity of

More information

Direct Proof Universal Statements

Direct Proof Universal Statements Direct Proof Universal Statements Lecture 13 Section 4.1 Robb T. Koether Hampden-Sydney College Wed, Feb 6, 2013 Robb T. Koether (Hampden-Sydney College) Direct Proof Universal Statements Wed, Feb 6, 2013

More information