Introduction to Artificial Intelligence

Artificial Intelligence (AI) Introduction

• AI is the branch of computer science focused on creating intelligent machines capable of human-like behavior and decision-making.
• AI techniques aim to capture knowledge from data and information.
• AI encompasses various approaches to enable machines to perform tasks needing human-like intelligence, encompassing:
  • Machine Learning (ML)
  • Deep Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Expert Systems
  • Fuzzy Logic
  • Robotics

AI Models

• AI models are computational structures utilizing algorithms for artificial intelligence and machine learning.
• These models are trained on data to perform tasks or make decisions without direct programming.
• Common AI model types include: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, Neural Networks, Recurrent Neural Networks, LSTM, GRU, Transformer Models, K-means Clustering, Hierarchical Clustering, PCA, Autoencoders, and Generative Adversarial Networks (GANs).
• Model selection depends on the dataset's characteristics, task complexity, and desired outcomes, among other factors.

Data Acquisition and Learning in AI

• Knowledge discovery and data mining are essential for obtaining useful information.
• Data cleaning, preprocessing, and pattern identification are key steps in extracting relevant information.
• Computational learning theory provides models for analyzing algorithm efficiency and feasibility.
• Neural and evolutionary computation assist in faster data mining.
• Intelligent agents and multi-agent systems (MAS) are used for complex decision-making in various scenarios, including flexible automated systems.

Problem Solving with AI

• AI excels in structured problem-solving, where definite solutions exist with the right algorithm.
• Well-structured problems include mathematical equations, calculating trajectories, network analysis, and games like Tic-Tac-Toe.
• Ill-structured problems are characterized by multiple, not always clear solutions and involve scenarios, like waste disposal, security threat analysis, or goal specification in complex domains.
• Problem-solving involves identifying, analyzing, formulating, and executing solutions to problems.

Problem Solving Process

• Problem-solving is a process for generating solutions for specific situations.
• This process often includes problem identification, information gathering, creation of knowledge base, action planning, executing actions on intermediate states, and evaluating the goal.

Problem Space and Search

• Problem space search is crucial in AI for finding paths within a set of possible states, seeking solutions or optimal outcomes.
• It involves evaluating possible state sequences, with strategies like forward and backward search, and uninformed searches (generating all states) and informed searches (choosing path based on knowledge).
• This is especially important for problems with multiple possible solutions.

Problem Types and Characteristics

• Problems in AI can be categorized based on various aspects, including:
  • Deterministic/Observable
  • Non-Observable
  • Non-Deterministic/Partially Observable
• Identifying whether a problem is decomposable, if solution steps can be reversed, if the universe/environment is predictable, and if a good solution is absolute or relative will determine the best approach to solve the problem.
• The role of knowledge and need for interaction with a human are other important characteristics for selecting a problem-solving strategy.

Toy Problems

• Simple problems like Tic-Tac-Toe, Missionaries and Cannibals, and the Traveling Salesman Problem are used to demonstrate AI concepts and strategies.
• They provide controlled environments for testing and evaluating algorithms in various problem-solving situations.