97 terms

Probability Theory

Assigns each sentence a degree of belief ranging from 0 to 1

Conditional Independence

P(a ∧b | c) = P(a | c) P(b | c)

Independence

P(a ∧b) = P(a) P(b)

Product Rule (Chain Rule)

P(a ∧b∧c) = P(a | b∧c) P(b | c) P(c)

Bayes' Rule

P(a | b) = P(b | a) P(a) / P(b)

Logic

Formal symbol system for representation and inference

Valid

True in every possible world

Complete

Inference system can derive any sentence that is entailed

Conjunctive Normal Form

A sentence expressed as a conjunction of clauses (disjuncts)

Sound

Inference system derives only entailed sentences

Satisfiable

True in at least one possible world

Entailment

The idea that a sentence follows logically from other sentences

Conditional Probability

Degree of belief accorded after some evidence is obtained

Unconditional Probability

Degree of belief accorded without any other information

Factored Representation (Probability Concept)

A possible world is represented by variable/value pairs

Random Variable

Takes values from its domain with specified probabilities

Joint Probability Distribution

Gives probability of all combinations of values of all variables

Agent

Perceives environment by sensors, acts by actuators

Percept

Agent's perceptual inputs at any given instant

Percept Sequence

Complete history of everything agent has perceived

Rational Agent

Agent that acts to maximize its expected performance measure

Deterministic Environment

Next state of environment is fixed by current state and action

Dynamic Environment

Environment can change while agent is deliberating

Performance Measure

Evaluates any given sequence of environment states for utility

Agent Function

Maps any given percept sequence to an action

Abstraction

Process of removing detail from a representation

Fully Observable

Sensors give the complete state of environment at each time

State Space

All states reachable from the initial state by a sequence of actions

Frontier

Set of all leaf nodes available for expansion at any given time

Uninformed Search

Uses no additional information beyond problem definition

Informed Search

Uses problem-specific knowledge beyond problem definition

Optimal Search

Guaranteed to find lowest cost among all solutions

Complete Search

Guaranteed to find a solution if one is accessible

Expand a state

Apply each legal action to state, generating a new set of states

Branching Factor

Maximum number of successors of any node

Heuristic Function

Estimates cost of cheapest path from current state to goal state

A* Search

Tries to minimize the total estimated solution cost

Greedy Best-First Search

Tries to expand the node believed to be closest to the goal

Consistent Heuristic

For n' a successor of n from actions a, h(n)'s cost(n, a, n') + h(n')

Admissible Heuristic

Never over-estimate cost of cheapest path to a goal state

Game Tree

Tree where nodes are game states and edges are game moves

Cut-off Test

Function that decides when to stop exploring this search branch

Alpha-Beta Pruning

Returns same move as MiniMax, but may prune more branches

Weighted Linear Function

Vector dot product of a weight vector and a state feature vector

Zero-sum Game

In all game instances, total pay-off summed over all players is the same

MiniMax Algorithm

Optimal strategy for 2‐player zero‐sum games of perfect information, but impractical given limited time to make each move

Game Strategy

Function that specifies a player's move in every possible game state

Heuristic Evaluation Function

Approximates the value of a game state (i.e., of game position)

Solution to a CSP

A complete and consistent assignment

Complete Assignment

Every variable is associated wit ha value

Constraint Graph

Nodes correspond to variables, links connect variables that participate in a constraint

Arc Consistency

All values in a variable's domain satisfy its binary constraints

Forward Checking

When variable X is assigned, delete any value of other variables that is inconsistent with the assigned value of X

Assignment

Associates values with some or all variables

Node Consistency

All values in a variable's domain satisfy its unary constraints

Domain

Set of allowed values for some variable

Constraint

Specifies an allowable combination of variable values

Consistent Assignment

The values assigned to variables do not violate any constraints

Semantics

Defines the truth of each sentence in each possible world

Syntax

Specifies all the sentences that are well formed

Learning

Improves performance of future tasks after observing the world

Regression

Supervised learning with numeric output values

Decision Boundary

Surface in a high-dimensional space that separates the classes

Overfitting

Choose an over-complex model based on irrelevant data patterns

Cross-validation

Randomly split the data in a training set and a test set

Unsupervised Learning

Agent learns patterns in the input with no explicit feedback

Factored Representation (Machine Learning Concept)

Fixed set, list, or vector of features/attributes paired with a value

Supervised Learning

Agent observes input-output pairs & learns to map input to output

Test Set

Examples distinct from training set, used to estimate accuracy

Training Set

Example input-output pairs, from which to discover a hypothesis

Classification

Supervised learning with a discrete set of possible output variables

PEAS

Performance measure, environment, actuators, sensors

Breadth-First Search

Complete? Yes

Time: O(b^d)

Space: O(b^d)

Optimal? Yes

Time: O(b^d)

Space: O(b^d)

Optimal? Yes

Depth-First Search

Complete? No

Time: O(b^m)

Space: O(bm)

Optimal? No

Time: O(b^m)

Space: O(bm)

Optimal? No

Uniform Cost Search

Complete? Yes

Time: O(b^d+1)

Space: O(b^d+1)

Optimal? Yes

Time: O(b^d+1)

Space: O(b^d+1)

Optimal? Yes

Iterative Deepening Search

Complete? Yes

Time: O(b^d)

Space: O(bd)

Optimal? Yes

Time: O(b^d)

Space: O(bd)

Optimal? Yes

Bidirectional Search

Complete? Yes

Time: O(b^(d/2))

Space: O(b^(d/2))

Optimal? Yes

Time: O(b^(d/2))

Space: O(b^(d/2))

Optimal? Yes

Depth-Limited Search

Complete? No

Time: O(b^l)

Space: O(bl)

Optimal? No

Time: O(b^l)

Space: O(bl)

Optimal? No

(T/F) The information gain from an attribute A is how much classifier accuracy improves when attribute A is added to the example feature vectors in the training set.

False

(T/F) Overfitting is a general phenomenon that occurs with most or all types of learners.

True

(T/F) An agent is learning if it improves its performance on future tasks after making observations about the world.

True

(T/F) A decision tree can learn and represent any Boolean function.

True

(T/F) Cross-validation is a way to improve the accuracy of a learned hypothesis by reducing over-fitting using Ockham's razor.

False

(T/F) A constraint satisfaction problem (CSP) consists of a set of variables, a set of domains (one for each variable), and a set of constraints that specify allowable combinations of values.

True

(T/F) A consistent assignment is one in which every variable is assigned.

False

(T/F) A complete assignment is one that does not violate any constraints.

False

(T/F) A partial assignment is one that violates only some of the constraints.

False

(T/F) The nodes of a constraint graph correspond to variables of the problem, and a link connects any two variables that participate in a constraint.

True

(T/F) A constraint consists of a pair <scope, rel>, where scope is a tuple of variables that participate and rel defines the values those variables can take on.

True

(T/F) Performing constraint propagation involves using the constraints to reduce the number of legal values for a variable, which in turn can reduce the legal values for another variable, and so on.

True

(T/F) A variable in a CSP is arc-consistent iff, for each value in its domain and each of its binary constraints, that constraint is satisfied by that domain value together with some value in the domain of the other variable in that constraint.

True

(T/F) Constraint satisfaction problems are semi-decidable because they may never terminate if the problem has no legal solution.

False

(T/F) The minimum-remaining-values (MRV) heuristic chooses the variable with the fewest remaining legal values to assign next.

True

(T/F) The degree heuristic is used to set the temperature in methods for solving CSPs based on Simulated Annealing.

False

(T/F) The least-constraining-value heuristic prefers the value that rules out the fewest choices for the neighboring variables in the constraint graph.

True

(T/F) The min-conflicts heuristic for local search prefers the value that results in the minimum number of conflicts with other variables.

True

(T/F) The min-conflicts heuristic is rarely used because it is only effective when the constraint graph is a tree.

False