> For the complete documentation index, see [llms.txt](https://nec-license.gitbook.io/books/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://nec-license.gitbook.io/books/9.-artificial-intelligence-and-neural-networks.md).

# 9. Artificial Intelligence and Neural Networks

### **9.1 Introduction to AI and Intelligent Agent**

* **Concept of Artificial Intelligence (AI)**
* **AI Perspectives**
* **History of AI and Applications**
* **Foundations of AI**
* **Introduction to Agents**
  * Structure of Intelligent Agent
  * Properties of Intelligent Agents
  * PEAS Description of Agents
* **Types of Agents**:
  * Simple Reflexive, Model-Based, Goal-Based, Utility-Based
* **Environment Types**:
  * Deterministic, Stochastic, Static, Dynamic, Observable, Semi-observable, Single Agent, Multi-Agent

### **9.2 Problem Solving and Searching Techniques**

* **Problem Formulation and State Space Search**
  * Well-defined Problems, Constraint Satisfaction Problem
* **Uninformed Search Techniques**:
  * Depth First Search, Breadth First Search, Depth Limited Search, Iterative Deepening Search, Bidirectional Search
* **Informed Search**:
  * Greedy Best First Search, A\* Search, Hill Climbing, Simulated Annealing
* **Game Playing and Adversarial Search**:
  * Mini-max Search, Alpha-Beta Pruning

### **9.3 Knowledge Representation**

* **Knowledge Representation and Mappings**
  * Approaches to Knowledge Representation
  * Issues in Knowledge Representation
* **Semantic Nets and Frames**
* **Propositional Logic (PL)**:
  * Syntax, Semantics, Formal Logic-Connectives, Tautology, Validity, Well-Formed Formula, Inference Using Resolution
* **Predicate Logic (FOPL)**:
  * Syntax, Semantics, Quantification, Rules of Inference, Unification, Resolution Refutation System
* **Bayesian Networks**:
  * Bayes' Rule, Reasoning in Belief Networks

### **9.4 Expert Systems and Natural Language Processing**

* **Expert Systems**:
  * Architecture of Expert Systems, Knowledge Acquisition, Declarative vs Procedural Knowledge, Development of Expert Systems
* **Natural Language Processing (NLP)**:
  * Terminology, Natural Language Understanding and Generation, Steps of NLP
  * Applications and Challenges of NLP
* **Machine Vision and Robotics**:
  * Concepts, Stages of Machine Vision, Robotics

### **9.5 Machine Learning**

* **Introduction to Machine Learning**
* **Learning Concepts**:
  * Supervised, Unsupervised, and Reinforcement Learning
* **Types of Learning**:
  * Inductive Learning (Decision Tree), Statistical-Based Learning (Naive Bayes Model), Fuzzy Learning
* **Fuzzy Inference System and Methods**
* **Genetic Algorithm**:
  * Genetic Algorithm Operators, Encoding, Selection Algorithms, Fitness Function, and Genetic Algorithm Parameters

### **9.6 Neural Networks**

* **Biological vs. Artificial Neural Networks (ANN)**
* **McCulloch-Pitts Neuron and Mathematical Model of ANN**
* **Activation Functions**
* **Neural Network Architectures**:
  * Perceptron, Learning Rate, Gradient Descent, Delta Rule, Hebbian Learning
  * Adaline Network, Multilayer Perceptron Neural Networks, Backpropagation Algorithm, Hopfield Neural Network