CS344 : Introduction to Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 4- Logic.

Slides:



Advertisements
Similar presentations
Artificial Intelligence Chapter 13 The Propositional Calculus Biointelligence Lab School of Computer Sci. & Eng. Seoul National University.
Advertisements

CS344: Introduction to Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 10– Club and Circuit Examples.
1 Logic Logic in general is a subfield of philosophy and its development is credited to ancient Greeks. Symbolic or mathematical logic is used in AI. In.
CS344 : Introduction to Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 9,10,11- Logic; Deduction Theorem 23/1/09 to 30/1/09.
Deduction In addition to being able to represent facts, or real- world statements, as formulas, we want to be able to manipulate facts, e.g., derive new.
CS344: Artificial Intelligence
CS344: Introduction to Artificial Intelligence (associated lab: CS386) Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture–5 and 6: Propositional Calculus.
Logic Concepts Lecture Module 11.
CS344 : Introduction to Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 15, 16, 17- Completeness Proof; Self References and.
CS621 : Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 12- Completeness Proof; Self References and Paradoxes 16 th August,
CS344: Introduction to Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 7– Predicate Calculus and Knowledge Representation.
Syllabus Every Week: 2 Hourly Exams +Final - as noted on Syllabus
TR1413: Discrete Mathematics For Computer Science Lecture 3: Formal approach to propositional logic.
1 Chapter 7 Propositional and Predicate Logic. 2 Chapter 7 Contents (1) l What is Logic? l Logical Operators l Translating between English and Logic l.
From Chapter 4 Formal Specification using Z David Lightfoot
Knoweldge Representation & Reasoning
CS621: Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 21– Predicate Calculus and Knowledge Representation 7 th September,
TR1413: Discrete Mathematics For Computer Science Lecture 4: System L.
Logical and Rule-Based Reasoning Part I. Logical Models and Reasoning Big Question: Do people think logically?
Fall 2002CMSC Discrete Structures1 Let’s proceed to… Mathematical Reasoning.
CS621: Introduction to Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture–4: Fuzzy Inferencing 29 th July 2010.
Methods of Proof & Proof Strategies
CS621: Artificial Intelligence Lecture 27: Backpropagation applied to recognition problems; start of logic Pushpak Bhattacharyya Computer Science and Engineering.
1 Chapter 7 Propositional and Predicate Logic. 2 Chapter 7 Contents (1) l What is Logic? l Logical Operators l Translating between English and Logic l.
CS344: Introduction to Artificial Intelligence (associated lab: CS386) Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 17, 18: Predicate Calculus 15.
March 3, 2015Applied Discrete Mathematics Week 5: Mathematical Reasoning 1Arguments Just like a rule of inference, an argument consists of one or more.
1 Sections 1.5 & 3.1 Methods of Proof / Proof Strategy.
Of 33 lecture 12: propositional logic – part I. of 33 propositions and connectives … two-valued logic – every sentence is either true or false some sentences.
Propositional Logic Dr. Rogelio Dávila Pérez Profesor-Investigador División de Posgrado Universidad Autónoma Guadalajara
CS621 : Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 12- Completeness Proof; Self References and Paradoxes 16 th August,
ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information.
CS344: Introduction to Artificial Intelligence (associated lab: CS386) Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture–4: Fuzzy Control of Inverted.
CS621: Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture–8: (a) Some Proofs in Formal System;(b) How to read research papers.
1 Introduction to Abstract Mathematics Chapter 2: The Logic of Quantified Statements. Predicate Calculus Instructor: Hayk Melikya 2.3.
CS6133 Software Specification and Verification
CS621: Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture–7: (a) Formal System;(b) How to read research papers 3 rd August, 2010.
Artificial Intelligence 7. Making Deductive Inferences Course V231 Department of Computing Imperial College, London Jeremy Gow.
CS621: Artificial Intelligence Lecture 28: Propositional calculus; Puzzle Solving Pushpak Bhattacharyya Computer Science and Engineering Department IIT.
CS344 : Introduction to Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 9- Completeness proof; introducing knowledge representation.
Of 38 lecture 13: propositional logic – part II. of 38 propositional logic Gentzen system PROP_G design to be simple syntax and vocabulary the same as.
CS344: Introduction to Artificial Intelligence (associated lab: CS386) Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 14: AI, Logic, and Puzzle Solving.
Chapter 7. Propositional and Predicate Logic Fall 2013 Comp3710 Artificial Intelligence Computing Science Thompson Rivers University.
ARTIFICIAL INTELLIGENCE Lecture 2 Propositional Calculus.
Outline Logic Propositional Logic Well formed formula Truth table
Foundations of Discrete Mathematics Chapter 1 By Dr. Dalia M. Gil, Ph.D.
Prof. Pushpak Bhattacharyya, IIT Bombay 1 CS 621 Artificial Intelligence Lecture 16 – 09/09/05 Prof. Pushpak Bhattacharyya Soundness, Completeness,
CT214 – Logical Foundations of Computing Darren Doherty Rm. 311 Dept. of Information Technology NUI Galway
Discrete Mathematical Structures: Theory and Applications 1 Logic: Learning Objectives  Learn about statements (propositions)  Learn how to use logical.
CS621: Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture–10: Soundness of Propositional Calculus 12 th August, 2010.
CS 344 Artificial Intelligence By Prof: Pushpak Bhattacharya Class on 12/Feb/2007.
March 3, 2016Introduction to Artificial Intelligence Lecture 12: Knowledge Representation & Reasoning I 1 Back to “Serious” Topics… Knowledge Representation.
CS621: Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture–6: Propositional calculus, Semantic Tableau, formal System 2 nd August,
CS344 : Introduction to Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 5- Deduction Theorem.
Chapter 7. Propositional and Predicate Logic
CS344: Introduction to Artificial Intelligence (associated lab: CS386)
Knowledge Representation and Reasoning
CS344 : Introduction to Artificial Intelligence
CS621: Artificial Intelligence
7.1 Rules of Implication I Natural Deduction is a method for deriving the conclusion of valid arguments expressed in the symbolism of propositional logic.
Applied Discrete Mathematics Week 1: Logic
Back to “Serious” Topics…
CS621: Artificial Intelligence
Chapter 7. Propositional and Predicate Logic
CSNB234 ARTIFICIAL INTELLIGENCE
ece 720 intelligent web: ontology and beyond
CS621: Artificial Intelligence
CS344 : Introduction to Artificial Intelligence
CS621: Artificial Intelligence
Artificial Intelligence
CS 621 Artificial Intelligence Lecture /09/05 Prof
Presentation transcript:

CS344 : Introduction to Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 4- Logic

Logic and inferencing Vision NLP Expert Systems Planning Robotics  Search  Reasoning  Learning  Knowledge Obtaining implication of given facts and rules -- Hallmark of intelligence

Inferencing through − Deduction (General to specific) − Induction (Specific to General) − Abduction (Conclusion to hypothesis in absence of any other evidence to contrary) Deduction Given:All men are mortal (rule) Shakespeare is a man (fact) To prove:Shakespeare is mortal (inference) Induction Given:Shakespeare is mortal Newton is mortal (Observation) Dijkstra is mortal To prove:All men are mortal (Generalization)

If there is rain, then there will be no picnic Fact1: There was rain Conclude: There was no picnic Deduction Fact2: There was no picnic Conclude: There was no rain (?) Induction and abduction are fallible forms of reasoning. Their conclusions are susceptible to retraction Two systems of logic 1) Propositional calculus 2) Predicate calculus

Propositions − Stand for facts/assertions − Declarative statements − As opposed to interrogative statements (questions) or imperative statements (request, order) Operators => and ¬ form a minimal set (can express other operations) - Prove it. Tautologies are formulae whose truth value is always T, whatever the assignment is

Model In propositional calculus any formula with n propositions has 2 n models (assignments) - Tautologies evaluate to T in all models. Examples: 1) 2) - e Morgan with AND

Semantic Tree/Tableau method of proving tautology Start with the negation of the formula α-formula β-formula α-formula - α - formula - β - formula - α - formula

Example 2: BC BC Contradictions in all paths X α-formula (α - formulae) (β - formulae) (α - formula)

Exercise: Prove the backward implication in the previous example

Formal Systems Rule governed Strict description of structure and rule application Constituents Symbols Well formed formulae Inference rules Assignment of semantics Notion of proof Notion of soundness, completeness, consistency, decidability etc.

Hilbert's formalization of propositional calculus 1. Elements are propositions : Capital letters 2. Operator is only one :  (called implies) 3. Special symbol F (called 'false') 4. Two other symbols : '(' and ')' 5. Well formed formula is constructed according to the grammar WFF  P|F|WFF  WFF 6. Inference rule : only one Given A  B and A write B known asMODUS PONENS

7. Axioms : Starting structures A1: A2: A3 This formal system defines the propositional calculus

Notion of proof 1. Sequence of well formed formulae 2. Start with a set of hypotheses 3. The expression to be proved should be the last line in the sequence 4. Each intermediate expression is either one of the hypotheses or one of the axioms or the result of modus ponens 5. An expression which is proved only from the axioms and inference rules is called a THEOREM within the system

Example of proof From P and and prove R H1: P H2: H3: i) PH1 ii) H2 iii) QMP, (i), (ii) iv) H3 v) RMP, (iii), (iv)

Prove that is a THEOREM i) A1 : P for A and B ii) A1: P for A and for B iii) A2: with P for A, for B and P for C iv) MP, (ii), (iii) v) MP, (i), (iv)

Formalization of propositional logic (review) Axioms : A1 A2 A3 Inference rule: Given and A, write B A Proof is: A sequence of i) Hypotheses ii) Axioms iii) Results of MP A Theorem is an Expression proved from axioms and inference rules

Example: To prove i) A1 : P for A and B ii) A1: P for A and for B iii) A2: with P for A, for B and P for C iv) MP, (ii), (iii) v) MP, (i), (iv)

Shorthand 1. is written as and called 'NOT P' 2. is written as and called 'P OR Q’ 3. is written as and called 'P AND Q' Exercise: (Challenge) - Prove that

A very useful theorem (Actually a meta theorem, called deduction theorem) Statement If A 1, A 2, A A n ├ B then A 1, A 2, A 3, A n-1 ├ ├ is read as 'derives' Given A1A2A3....AnBA1A2A3....AnB Picture 1 A 1 A 2 A 3. A n-1 Picture 2

Use of Deduction Theorem Prove i.e., ├ F (M.P) A├ (D.T) ├ (D.T) Very difficult to prove from first principles, i.e., using axioms and inference rules only

Prove i.e. ├ F ├ (D.T) ├ Q (M.P with A3) P ├ ├