Untitled Flashcards Set

Constraint Satisfaction Problem (CSP)

A problem in AI where a set of variables must be assigned values that satisfy specific constraints.

Domain (in CSP)

The set of possible values that a variable can take in a Constraint Satisfaction Problem.

Scope (in CSP)

A tuple of variables that are involved in a specific constraint.

Rel (in CSP)

A relationship that lists the possible solutions for the variables to meet the constraints.

State-space (in CSP)

The set of all possible configurations of variable assignments, representing potential solutions.

Finite Domain

A domain that has a limited number of possible values, such as colors or integers.

Infinite Domain

A domain that has an unlimited number of possible values, such as real numbers.

Continuous Domain

A domain with an infinite range of values that can be represented using a finite set of parameters.

Unary Constraint

A constraint that applies to a single variable.

Binary Constraint

A constraint that involves two variables and specifies a condition on their values.

Global Constraint

A constraint involving more than two variables that specifies complex relationships among them.

Crypt-Arithmetic Problem

A type of Constraint Satisfaction Problem where letters are converted to unique digits following specific rules.

Constraint Propagation

The process of reducing the domain of variables in CSPs and communicating these reductions to constraints.

Backtracking

A problem-solving method that involves trying different assignments and reverting when a violation of constraints occurs.

Live Node

A node in a search tree that can still lead to further exploration and potential solutions.

Success Node

A node that provides a feasible solution to the problem.

Dead Node

A node that cannot yield further solutions and does not meet the necessary constraints.

Adversarial Search

A search process where multiple agents with conflicting goals explore a search space to find solutions.

Minimax Algorithm

A decision rule for minimizing the possible loss for a worst-case scenario in game playing.

Alpha-Beta Pruning

An optimization technique for the minimax algorithm that reduces the number of nodes evaluated in the search tree.

Perfect Information Game

A game in which all players have complete knowledge of the game state.

Imperfect Information Game

A game where players do not have complete knowledge of the game state.

Depth-first Search

A strategy that explores as far down a branch as possible before backtracking.

Machine Learning-based Systems in AI

Systems that utilize algorithms to learn from experience and improve decision-making.

Rule-based Systems in AI

Systems that operate on a predefined set of rules to make decisions.