Artificial Intelligence
Course Objectives
Introduce machine intelligence, problem-solving, heuristic search
Introduce game playing.
Provide and introduction to various knowledge representation techniques, reasoning, and expert systems.
Introduce planning and learning in AI.
Course Outcomes:
Upon successful completion of this course, students should be able to:
CO1. Understand and analyze different AI-related state space search techniques.
CO2. Outline and model simple knowledge-based systems.
CO3. Apply knowledge representation techniques and identify algorithms for reasoning with knowledge.
CO4. Identify appropriate planning and learning algorithms to enhance AI problem-solving.
CO5. Explore the scope of AI in various application domains.
Syllabus
UNIT:1 (10 Hours)
What is Artificial Intelligence? AI Technique, Level of the Model, Problem Spaces, and Search: Defining the Problem as a State Space Search, Production Systems, Problem Characteristics, Production System Characteristics, and Issues in the Design of Search Programs. Heuristic Search Techniques: Generate-and-Test, Hill Climbing, Best-first Search, Problem Reduction, Constraint Satisfaction, Means-ends Analysis
UNIT:2 (10 Hours)
Knowledge Representation: Representations and Mappings, Approaches to Knowledge Representation, Using Predicate Logic: Representing Simple Facts in Logic, Representing Instance and ISA Relationships, Computable Functions and Predicates, Resolution, Natural Deduction. Using Rules: Procedural Versus Declarative Knowledge, Logic Programming, Forward Versus Backward Reasoning, Matching, Control Knowledge. Symbolic Reasoning
Under Uncertainty: Introduction to Non monotonic Reasoning, Logics for Non monotonic Reasoning, Implementation Issues, Augmenting a Problem-solver, Depth-first Search, and Breadth-first Search. Weak and Strong Slot-and-Filler Structures: Semantic Nets, Frames, Conceptual Dependency Scripts, CYC.
UNIT:3 (10 Hours)
Game Playing: The Mini-max Search Procedure, Adding Alpha-beta Cutoffs, Iterative Deepening. Planning: The Blocks World, Components of a Planning System, Goal Stack Planning, Nonlinear Planning Using Constraint Posting, Hierarchical Planning, Other Planning Techniques. Understanding: What is Understanding, What Makes Understanding Hard?, Understanding as Constraint Satisfaction.
Natural Language Processing: Introduction, Syntactic Processing, Semantic Analysis, Discourse and Pragmatic Processing, Statistical Natural Language Processing, Spell Checking.
UNIT:4 (10 Hours)
Learning: Rote Learning, Learning by Taking Advice, Learning in Problem-solving, Learning from Examples: Induction, Explanation-based Learning, Discovery, Analogy, Formal Learning Theory, Neural Net Learning and Genetic Learning. Expert Systems: Representing and Using Domain Knowledge, Expert System Shells, Explanation, Knowledge Acquisition.
Textbooks:
Russel, Stuart, and Peter Norvig. "Artificial Intelligence: A Modern Approach, Global Edition." Foundations 19 (2021): 1-1166.
Elaine Rich, Kevin Knight, & Shivashankar B Nair, Artificial Intelligence, McGraw Hill, 3rd ed.,2009
Reference Books:
1. Introduction to Artificial Intelligence & Expert Systems, Dan W Patterson, PHI.,2010
2. S Kaushik, Artificial Intelligence, Cengage Learning, 1st ed.2011