Unit 1 Introduction

• The AI Problems
• The Underlying Assumption
• AI techniques
• The Level of The Model
• Criteria for Success 2 Program Name: Bachelor of Engineering Level: UG Subject Code: BE05000151 Subject Name: Artificial Intelligence

Unit 2 Problems, State Space Search & Heuristic Search Techniques

6 lecture hours

• Defining The Problems as a State Space Search
• Production Systems
• Production Characteristics
• Production System Characteristics and Issues in the Design of Search Programs
• Hill Climbing
• Best-First Search
• Problem Reduction
• Constraint Satisfaction
• Means-Ends Analysis

Unit 3 Knowledge Representation

6 lecture hours

• Representations And Mappings
• Approaches To Knowledge Representation
• Representation Simple Facts In Logic
• Representing Instance and Isa Relationships
• Computable Functions and Predicates
• Procedural versus Declarative Knowledge
• Logic Programming
• Forward versus Backward Reasoning

Unit 4 Symbolic Reasoning Under Uncertainty

6 lecture hours

• Introduction to Nonmonotonic Reasoning
• Logics for Non-monotonic Reasoning

Unit 5 Probabilistic Reasoning

3 lecture hours

• Probability And Bays’ Theorem
• Certainty Factors and Rule Base Systems
• Bayesian Networks
• Dempster- Shafer Theory
• Fuzzy Logic

Unit 6 Game Playing

3 lecture hours

• Min-Max Search Procedure
• Alpha-Beta Cut-offs
• Iterative deepening

Unit 7 Planning

3 lecture hours

• The Blocks World
• Components of a Planning System
• Goal Stack Planning
• Nonlinear Planning Using Constraint Posting
• Hierarchical Planning
• Reactive Systems

Unit 8 Natural Language & Language Models

11 lecture hours

• Introduction to NLP: Basics of Natural Language Processing
• Word embeddings
• Evolution of Language Models: Statistical language models (n-grams)
• LSTMs, and their limitations Transition to attention-based models
• Transformer Architecture: Encoder-decoder structure
• Self-attention mechanism
• Positional encoding
• multi-head attention and feed-forward layers Large Language Models: Pre-training
• Fine-tuning and transfer learning
• Prompt engineering basics
• Applications: chatbots
• translation Challenges & Ethics: Bias
• fairness, and hallucination in LLMs
• Energy consumption and scalability
• Alignment and safety concerns

Unit 9 Introduction to Prolog

5 lecture hours

• Converting English to Prolog Facts and Rules
• Prolog Terminology
• Control Structures
• Arithmetic Operators
• Matching in Prolog