Course Title Fundamentals of Artificial Intelligence Course Code: InSy3102 Cr. Hr. /ECTS 3 Cr.hr (5 ECTS) Study Hours Lecture: 2 Laboratory: 3 Tutorial: 0 Instructor Name: Abdulmalik Johar Email: nesratm@gmail.com Course Description The course explores basic principles, methodologies, techniques, tools and current research topics of Artificial Intelligence. The content includes history and perspectives of AI, the different types of intelligent agents, goal based agents, search problems, constraint satisfaction problems, adversarial search problems, knowledge - based agents, knowledge representation, inference techniques, propositional logic, first order logic, learning agents, inductive learning, neural networks, fuzzy logic, communicati on and perception, natural language processing, machine learning, computer vision and robotics. Application of these methods to important areas of Artificial Intelligence including development of knowledge - based systems. Learning Outcomes On successful completion of the course students will be able to: ➢ Explain the different perspectives and historical background of Artificial Intelligence ➢ Describe different types and characteristics of intelligent agents ➢ Differentiate the different types of searching strategies employed in goal - based agents ➢ Represent knowledge and implement inference techniques to provide solutions to partially observable environments using propositional and first order logic ➢ Use learning algorithms to create decision tree from examples and demonstrate the use of neural network in implementing learning agents ➢ Elaborate issues in word and sentence tokenization, text classification and sentiment analysis Course Outline Topics Duration in week Chapter 1: Introduction to Artificial Intelligence (AI) 1.1 The Foundations of AI 1.2 Why AI 1.3 Definition of AI. 1.4 History of AI 1.5 Approaches to AI 1.6 State of the Art 1 – 2 Chapter 2: Intelligent Agents 2.1 Agents and Environments 2.2 Rationality Vs Omniscience 2.3 Structure of Intelligent Agents 2.4 Autonomy 2.5 Task Environments 2.6 Properties of Task Environment 2.7 PEAS examples 2.8 Agent Types 3 - 5 Chapter 3: Problem Solving (Goal Based) Agents 3.1 Problem Solving by Searching 3.2 What is search? 3.3 Problem Formulation 3.4 State and state space. 3.5 Search Strategies 3.5.1 Informed Search Strategies 3.5.2 Uninformed Search Strategies 3.5.3 Local Search Strategies 6 - 8 Chapter 4: Knowledge Representation and Reasoning 4.1 Logical Agents 4.2 Propositional Logic 4.2.1 Inference in Propositional Logic 9 - 10 4.3 Predicate (First - Order)Logic 4.3.1 Inference in First - Order Logic 4.3.2 Knowledge Representation 4.3.3 Knowledge - based Systems 4.4 Reasoning under uncertainty Chapter 5. Expert System 5.1 Introduction 5.2 Applications of Expert Systems 5.3 Expert Systems Technologies 5.4 Benefits of Expert Systems 5.5 Expert System Limitations 5.6 The Architecture of Expert Systems 5.7 Components of Expert Systems 5.7.1 The Knowledge bases 5.7.2 The Inference Engine 5.7.3 The User Interface 10 - 12 Chapter 6: Learning Agents 6.1 Factors for designing learning agents 6.2 Learning from Examples/Observation 6.3 Knowledge in Learning 6.4 Neural Networks 13 - 14 Chapter 7: Communicating, Perceiving, and Acting 7.1 Natural Language Processing 7.2 Natural Language for Communication 7.3 Perception 7.4 Introduction to Robotics 15 - 16 Blended Mode Instructional Contents Chapter Mode of Delivery Chapter 1 In - person Chapter 2 In - person Chapter 3 In - person Chapter 4 Online Chapter 5 Online Chapter 6 In - person Chapter 7 In - person Teaching Strategy Teaching Strategy The course will be delivered through lectures, demonstrations, presentations, group discussions, and individual and group project work. In addition, online learning will be supported through the Learning Management System (LMS) using Open edX, including di gital content delivery, online discussions, assignments, and assessments. Assessment Criteria Assessment Forms Weight • Laboratory Exam/project work • Mid • Final Exam 25% 25% 50% Reading materials Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. – Comprehensive reference covering agents, search, knowledge representation, reasoning, learning, NLP, and robotics.