9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria. 1 CSC 427:ARTIFICIAL INTELLIGENCE BY DR. A. F. ADEKOYA Room No: COLNAS B321 Department of Computer Science College of Natural Sciences University of Agriculture, Abeokuta

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria. 2 CSC 427:ARTIFICIAL INTELLIGENCE Course Contents: 1.Introduction to Artificial Intelligence 2.Overview of Artificial Intelligence Techniques/Tools 3.Descriptive Logic 4.Artificial Neural Network 5.Fuzzy Logic 6.Genetic Algorithm 7.Knowledge Representation, Expert Systems and Pattern Recognition 8.Natural Language Processing

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria. 3 CSC 427:ARTIFICIAL INTELLIGENCE References: 1.Data Mining: Concepts, Models, Methods, and Algorithms by Mehmed Kantardzic John Wiley & Sons ISBN: © Artificial Intelligence and Expert Systems for Engineers by C.S. Krishnamoorthy; S. Rajeev CRC Press, CRC Press LLC ISBN: C++ Neural Networks and Fuzzy Logic by Valluru B. Rao M&T Books, IDG Books Worldwide, Inc. ISBN: Mathematics for Computing by R. Callan, Letts Educational, London. ISBN Artificial Intelligence: A Guide to Intelligent Systems by Michael Negnevitsky, Addison Wesley Pearson Education ISBN

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria. 4 Assessments 1. Attendance 5% 2. Assignments10% 3. Mid-Semester Exams15% 4. Semester Examination70% Note: Deadlines for submission of assignments are not negotiable and must be strictly complied with. Copying of assignments is forbidden and would attract zero mark. There would be one or two impromptu CAT(s). 75% Attendance is required before a student would be allowed to sit for the semester examination. CSC 427:ARTIFICIAL INTELLIGENCE

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria. 5 CSC 427:ARTIFICIAL INTELLIGENCE Overview: Computers have been extensively used to expedite or automate many complex and sometimes dangerous tasks. The history of the use of computers in problems solving parallels the developments in computer hardware and software technology. The emergence of improved paradigms such as evolutionary, soft, parallel and distributed computing, backed up by appropriate software environments, has virtually transformed the direction of research in computer usage. This has resulted in the transformation of computers from large numerical computing machines to aids to engineers at every stage of problem solving. Introduction to Artificial Intelligence

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria. 6 CSC 427:ARTIFICIAL INTELLIGENCE A key task in problem solving especially with computers at different stages of execution is decision making. Decision making requires processing of symbolic information in handling of facts and inference using domain knowledge. Inference is nothing but search through the knowledge base using the facts. The intensive research carried out in the area of AI in the last six decades resulted in the emergence of a number of useful techniques which can be used for solving many complex problems. Introduction to Artificial Intelligence

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria. 7 CSC 427:ARTIFICIAL INTELLIGENCE Intelligence is synonymous with human (animal) ability to store and recall fact (cognitive), solve a given problem based on known fact and relevant theorem (psychomotor). This ability is inherent and innate, trainable and can be developed. Artificial Intelligence (AI) is the ability of an electronic device (computer) to accomplish any tasks that ordinary would have been handled by human. Introduction to Artificial Intelligence

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria. 8 CSC 427:ARTIFICIAL INTELLIGENCE Introduction to Artificial Intelligence AI models the richness and dynamisms of human brain and its analytic and memory capability. Artificial Intelligence focus on (i) the use of computers to process symbols, (ii) the need for new languages, and (iii) the role of computers for theorem proving instead of focusing on hardware that simulated intelligence. (J. McCarthy, M. Minsky, N. Rochester and C. Shanon 1956)

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria. 9 CSC 427:ARTIFICIAL INTELLIGENCE Introduction to Artificial Intelligence Major Categories of AI 1.Symbolic Based on logic and uses of sequences of rules. Symbolic programs are good in modeling how human think, act and accomplish tasks. 2.Connectionist Based on network of neurons in the brain. Brittle and good for machine learning and pattern recognition. 3.Evolutionary Based on genetics evolution theory in biology.

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria CSC 427:ARTIFICIAL INTELLIGENCE Introduction to Artificial Intelligence Purpose of AI 1.Technological 2.Psychological 3.Economic Home Task Do a critic of Alan Turing and John Searle views of AI

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria CSC 427:ARTIFICIAL INTELLIGENCE Logic Proposition Logic Predicate Logic

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria CSC 427:ARTIFICIAL INTELLIGENCE Logic Proposition Logic A propositional logic is a logical statement whose truth value can be evaluated as either TRUE or FALSE. Where T denotes TRUE AndFdenotes FALSE Propositional logic is of two types, namely Simple Propositional Logic Complex or Compound Propositional Logic.

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria CSC 427:ARTIFICIAL INTELLIGENCE Logic Simple Propositional Logic A simple propositional logic refers to single logical statement whose truth value can be verified or evaluated. e.g.UNAAB is a university. Complex or Compound Propositional Logic A complex propositional logic refers to logical statements which are combinations of two or more simple propositional logic statements with the use of connectors (connectives) such as disjunction, conjunction etc. e.g. UNAAB is a university and it is located in Abeokuta.

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria CSC 427:ARTIFICIAL INTELLIGENCE Logic Truth Tables Truth tables are used for stating precise logic values for logic statements. The number of rows in a truth table is 2 n, where n is the number of simple propositions in the logical statements. Each of the propositions are given labels such as A, B, C etc. e.g. UNAAB is a university A e.g. UNAAB is a university and it is located in Abeokuta. comprises UNAAB is a university A UNAAB is located in AbeokutaB

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria CSC 427:ARTIFICIAL INTELLIGENCE Logic Logic Connectives Logic uses names and symbols to represent the connectives as illustrated in this table. SymbolConnectivesLogic Name.,., ConjunctionAND +,+, DisjunctionOR NegationNOT ImplicationImplies EquivalenceDouble implication Exclusive OR EX-OR

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria CSC 427:ARTIFICIAL INTELLIGENCE AND Truth Table e.g.UNAAB is a university and it is located in Abeokuta. comprises UNAAB is a university A UNAAB is located in AbeokutaB Connective: AND,., ABA AND BA BA. B TTTTT TFFFF FTFFF FFFFF Rules of AND 1.The output is TRUE when all the inputs are TRUE. 2.The output is FALSE if any or all the inputs are FALSE

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria CSC 427:ARTIFICIAL INTELLIGENCE OR Truth Table e.g.Either UNAAB is a university or is a polytechnic. comprises UNAAB is a university A UNAAB is a polytechnic B Connective: OR, +, ABA OR BA BA + B TTTTT TFTTT FTTTT FFFFF Rules of OR 1.The output is TRUE when at least one or all the inputs are TRUE. 2.The output is FALSE if all the inputs are FALSE

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria CSC 427:ARTIFICIAL INTELLIGENCE NOT Truth Table e.g. UNAAB is a university A UNAAB is not a university A Connective: NOT, -, ANOT A A - A TFFF FTTT Rules of OR The output is a negation of the input. i.e. when the Input is TRUE, the output is FALSE. Input is FALSE, the output is TRUE.

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria CSC 427:ARTIFICIAL INTELLIGENCE IMPLICATION Truth Table e.g.IF UNAAB is a university THEN it is an higher institution. comprises UNAAB is a university A UNAAB is an higher institution B Connective: Implies, -, ABA Implies BA B TTTT TFFF FTTT FFTT Rules of Implication 1.The output is TRUE when at least one or all the inputs are TRUE. 2.The output is FALSE if all the input are FALSE

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria CSC 427:ARTIFICIAL INTELLIGENCE EQUIVALENCE Truth Table e.g.A university degree is equivalent to a polytechnic diploma Connective: Equivalence, ↔ ABA ↔ B TTT TFF FTF FFT Rules of Equivalence 1.The output is TRUE when all the inputs are TRUE. 2.The output is TRUE when all the inputs are FALSE. 3.The output is FALSE if any of the input is FALSE Exercise: Using a truth table, prove that (A ↔B) ↔((A ↔B)^(B→A))

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria CSC 427:ARTIFICIAL INTELLIGENCE EXCLUSIVE-OR Truth Table e.g.A university degree is equivalent to a polytechnic diploma Connective: EX-OR, ABA B TTF TFT FTT FFF Rules of Equivalence 1.The output is TRUE when either at least an input is TRUE or FALSE. 2. The output is FALSE if either all the inputs are TRUE or all the inputs are FALSE

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria CSC 427:ARTIFICIAL INTELLIGENCE TAUTOLOGY An expression with a truth value T irrespective of the truth values of the constituent atoms. ABA ^ B→A TTT TFT FTT FFT CONTRADICTION An expression with a truth value F irrespective of the truth values of the constituent atoms. TAUTOLOGY AND CONTRADICTION ABA ^ B→A TTF TFF FTF FFF

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria CSC 427:ARTIFICIAL INTELLIGENCE ARGUMENT AND VALIDITY Argument: An argument present a conclusion as following logically from a set of assumptions. e.g. If we say “John’s keys are in the car or hung up in the office. John’s keys are not in the car. Then John’s keys are hung up in the office.” We can always write this argument in a clear and precise formal expression, such as: John’s keys are in the car or hung up in the officeP John’s keys are in the car ¬ P Therefore, John’s keys are hung up in the officeQ We can express the argument in a more formal form i.P νQAssumptions ii. ¬ P iii,QConclusion } Logically, it is written as PνQ ¬P Q

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria CSC 427:ARTIFICIAL INTELLIGENCE ARGUMENT AND VALIDITY Validity of an argument: If we express the argument in the form [ A

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria CSC 427:ARTIFICIAL INTELLIGENCE

9/27/2016 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria CSC 427:ARTIFICIAL INTELLIGENCE