CSC 427: ARTIFICIAL INTELLIGENCE

CSC 427: ARTIFICIAL INTELLIGENCE
7/5/2019 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 CSC 427 Dr. A. F. Adekoya 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 CSC 427: ARTIFICIAL INTELLIGENCE Course Contents: Introduction to Artificial Intelligence Overview of Artificial Intelligence Techniques/Tools Descriptive Logic Artificial Neural Network Fuzzy Logic Genetic Algorithm Knowledge Representation, Expert Systems and Pattern Recognition Natural Language Processing CSC 427 Dr. A. F. Adekoya 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 CSC 427: ARTIFICIAL INTELLIGENCE References: Data Mining: Concepts, Models, Methods, and Algorithms by Mehmed Kantardzic John Wiley & Sons ISBN: © 2003 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: 1995 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 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

Assessments CSC 427: ARTIFICIAL INTELLIGENCE Attendance 5%
7/5/2019 CSC 427: ARTIFICIAL INTELLIGENCE Assessments Attendance 5% Assignments 10% Mid-Semester Exams 15% Semester Examination 70% 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. 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 CSC 427: ARTIFICIAL INTELLIGENCE Introduction to 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. 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 CSC 427: ARTIFICIAL INTELLIGENCE Introduction to 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. 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 CSC 427: ARTIFICIAL INTELLIGENCE Introduction to 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. 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 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 the use of computers to process symbols, the need for new languages, and 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) 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 CSC 427: ARTIFICIAL INTELLIGENCE Introduction to Artificial Intelligence Major Categories of AI Symbolic Based on logic and uses of sequences of rules. Symbolic programs are good in modeling how human think, act and accomplish tasks. Connectionist Based on network of neurons in the brain. Brittle and good for machine learning and pattern recognition. Evolutionary Based on genetics evolution theory in biology. 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 CSC 427: ARTIFICIAL INTELLIGENCE Introduction to Artificial Intelligence Purpose of AI Technological Psychological Economic Home Task Do a critic of Alan Turing and John Searle views of AI 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 CSC 427: ARTIFICIAL INTELLIGENCE Logic Proposition Logic Predicate Logic 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 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 And F denotes FALSE Propositional logic is of two types, namely Simple Propositional Logic Complex or Compound Propositional Logic. 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 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. 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 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 2n, 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 Abeokuta B 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 CSC 427: ARTIFICIAL INTELLIGENCE Logic Logic Connectives Logic uses names and symbols to represent the connectives as illustrated in this table. Symbol Connectives Logic Name ., Conjunction AND +, Disjunction OR Negation NOT Implication Implies Equivalence Double implication Exclusive OR EX-OR 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 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 Abeokuta B Connective: AND, ., A B A AND B A B A . B T F Rules of AND The output is TRUE when all the inputs are TRUE. The output is FALSE if any or all the inputs are FALSE 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 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, +, A B A OR B A B A + B T F Rules of OR The output is TRUE when at least one or all the inputs are TRUE. The output is FALSE if all the inputs are FALSE 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 CSC 427: ARTIFICIAL INTELLIGENCE NOT Truth Table e.g. UNAAB is a university A UNAAB is not a university A Connective: NOT, -, 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. A NOT A - A T F 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 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, -, A B A Implies B A B T F Rules of Implication The output is FALSE when the condition is TRUE and the conclusion is FALSE Otherwise, the output is TRUE 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 CSC 427: ARTIFICIAL INTELLIGENCE EQUIVALENCE Truth Table e.g. A university degree is equivalent to a polytechnic diploma Connective: Equivalence, ↔ Rules of Equivalence The output is TRUE when all the inputs are TRUE. The output is TRUE when all the inputs are FALSE. The output is FALSE if any of the input is FALSE A B A ↔ B T F Exercise: Using a truth table, prove that (A ↔B) ↔((A ↔B)^(B→A)) 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 CSC 427: ARTIFICIAL INTELLIGENCE EXCLUSIVE-OR Truth Table e.g. A university degree is equivalent to a polytechnic diploma Connective: EX-OR, A B A B T F Rules of Equivalence 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 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 CSC 427: ARTIFICIAL INTELLIGENCE TAUTOLOGY AND CONTRADICTION TAUTOLOGY An expression with a truth value T irrespective of the truth values of the constituent atoms. CONTRADICTION An expression with a truth value F irrespective of the truth values of the constituent atoms. A B A ^ B→A T F A B A ^ B→A T F 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 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 office P John’s keys are in the car ¬ P Therefore, John’s keys are hung up in the office Q We can express the argument in a more formal form i. P νQ Assumptions ii. ¬ P iii, Q Conclusion Logically, it is written as PνQ ¬P Q } 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 CSC 427: ARTIFICIAL INTELLIGENCE ARGUMENT AND VALIDITY Validity of an argument: If we express the argument in the form [ A 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

7/5/2019 CSC 427: ARTIFICIAL INTELLIGENCE 7/5/2019 Department of Computer Science, University of Agriculture, Abeokuta, Nigeria.

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