Assignment 1: AI Agents and Search Algorithms

Vocabulary flashcards covering AI agent structures, search algorithms (GBFS, A*, Hill Climbing), reflex agent types, and AI classification levels (ANI, AGI, ASI).

AI Agent

An entity designed to interact with the environment, collect information through sensors, analyze it, and perform actions to achieve a goal.

Sensor

A component of an AI agent that collects information from its environment, such as through sensors, cameras, or user input.

Effectors (Actuators)

Components of an AI agent that perform actions in the environment to achieve a goal.

Greedy Best-First Search (GBFS)

An informed search algorithm that expands the node that appears closest to the goal based on a heuristic function $f(n) = h(n)$.

$$h(n)$$

The estimated cost from current node $n$ to the goal node in heuristic search algorithms.

$A^*$ Algorithm

An efficient informed search algorithm that combines the actual cost of reaching a node with the estimated cost from that node to the goal using $f(n) = g(n) + h(n)$.

$$g(n)$$

The actual cost from the start node to the current node in the $A^*$ algorithm.

$$f(n)$$

The total estimated cost of the path in informed search algorithms such as $A^*$.

Simple Reflex Agent

A type of AI agent that selects actions based only on the current percept, ignoring percept history, using condition-action rules.

Condition-Action Rule

An IF-THEN rule used by a simple reflex agent to map the current percept directly to an action.

Artificial Narrow Intelligence (ANI)

AI designed for a specific task with low flexibility, such as voice recognition or recommendation systems.

Artificial General Intelligence (AGI)

AI capable of performing any intellectual task like a human, featuring high flexibility based on real-world experience.

Artificial Super Intelligence (ASI)

A hypothetical future AI concept that would surpass human intelligence across all intellectual tasks with very high flexibility.

Hill Climbing Algorithm

A local heuristic search algorithm used to solve optimization problems by starting at an initial state and continuously moving to a neighboring state with a better heuristic value.