Artificial Intelligence

Artificial Intelligence

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Unit I

Introduction to Artificial Intelligence & Intelligent Agents : 

Definition and scope of AI, History and applications of AI, 

Characteristics of Intelligent Agents, 

Types of agents and environments, 

Agent architecture, Problem Solving Approach to Typical AI problems, 

Problem-solving agents. 

Example problems and approaches

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Unit II

Problem Solving & Search Strategies: 

Uninformed Search Strategies: 

BFS, 

DFS, 

Iterative Deepening, 

Informed Search Strategies: 

Greedy Best-First Search, 

A* Search, Heuristics and Optimization, 

Hill Climbing, Simulated Annealing, 

Constraint Satisfaction Problems, 

Game Playing: Min-max, 

Alpha-Beta Pruning, 

Stochastic & 

Partially Observable Games

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Unit III

Knowledge Representation & Reasoning: 

Propositional and First Order Logic, 

Syntax, 

Semantics, 

and Inference, 

Knowledge-based agents: 

Wumpus world, 

Logic Programming using Prolog, 

Forward and Backward Chaining, 

Resolution, 

Ontological Engineering and Reasoning

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Unit IV

Uncertainty & Learning Techniques: 

Introduction to uncertainty and probabilistic reasoning, 

Bayes' Rule, 

Bayesian Networks, 

Fuzzy logic and handling imprecision, 

Neural Networks (basics only): 

Perceptron, 

Backpropagation (intro level), 

Fundamentals of Machine Learning in AI context, 

Introduction to supervised and 

unsupervised learning

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Unit V

Applications of AI & Multi-Agent Systems: 

Natural Language Processing, 

Machine Translation, 

Information Retrieval and Extraction, 

Robotics: Perception, 

Planning, and Motion, 

Speech Recognition, 

Software Agents: Architecture, 

Communication, 

Trust, 

Multi-agent Negotiation and Reputation. 

Explainable AI (XAI) – Importance of interpretability, 

techniques for explaining black-box models, 

trust in AI, 

case studies in NLP and vision.

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