Introduction to Artificial Intelligence Practice Flashcards

A comprehensive set of practice flashcards covering basic definitions, historical milestones, AI techniques, and various search algorithms based on the Unit 1 lecture transcript.

Artificial Intelligence (AI)

A branch of computer science by which we can create intelligent machines which can behave like humans, think like humans, and are able to make decisions; or "Man-Made Thinking Power."

ELIZA

The first chatbot, created in 1966.

WABOT-1

The first AI robot, developed in 1972.

IBM Deep Blue

The first computer to beat a world chess champion in 1997.

Machine Learning (ML)

A subset of AI that focuses on giving machines the ability to learn from data instead of being programmed manually.

Deep Learning (DL)

A subset of ML that uses neural networks, similar to how the human brain works, to learn from large amounts of data.

Data Science (DS)

A field that incorporates machine learning and deep learning techniques along with statistics, data visualization, and data cleaning.

Production System

A problem-solving system that uses a set of rules and knowledge to make decisions, commonly used in expert systems.

State Space Search

A process used to find solutions by exploring different possible states of a problem, represented as nodes and transitions.

Initial State

The starting point of a search process in a state space.

Goal State

The desired end configuration or objective in a state space search.

Transition

An action that changes one state to another within a state space.

Breadth-First Search (BFS)

An uninformed search strategy that traverses a tree or graph level by level using a QUEUE [FIFO] data structure.

Depth-First Search (DFS)

An uninformed search strategy that follows each path to its greatest depth using a STACK [LIFO] data structure.

General Problem Solving (GPS)

The ability of an AI system to solve a wide range of problems, such as puzzle solving or route finding, by mimicking human reasoning.

Heuristic Search

A search technique that uses an evaluation function to find the most efficient path from a starting point to a goal.

A* Search Algorithm

A heuristic search that finds the least-cost path using the evaluation function $f(n) = g(n) + h(n)$, where $g(n)$ is the path cost and $h(n)$ is the heuristic estimate.

Best-First Search

A search algorithm that expands the node closest to the goal as estimated by a heuristic function, using OPEN and CLOSED lists.

Hill Climbing

A heuristic local search method that iteratively moves from the current state to a better neighboring state to find an optimal solution.

Global Maximum

The best state in a state-space diagram where the objective function achieves its highest value.

Local Maximum

A state that is better than its neighbors but not the best overall state in the space.

Problem Reduction

A method where a complex problem is broken down into smaller subproblems until each becomes easy to solve.

AO* Algorithm

A search method that divides difficult problems into smaller groups using AND-OR graphs and is designed to adapt to changing environments.

Constraint Satisfaction Problems (CSPs)

Problems solved by identifying values for variables that satisfy a set of rules or constraints, consisting of Variables, Domains, and Constraints.

Means-End Analysis (MEA)

A problem-solving technique that breaks down a goal into sub-goals to reduce the difference or gap between the current state and the goal state.

Uninformed Search

Also known as Blind Search, these algorithms operate in a brute-force way without additional information about the search space.