Midterm review

Question 1

What’s a rational agent

Answer 1

An agent that performs an action that will be most successful/max performance measure based on what it perceives/percept sequence and actions it can perform

Question 2

What does PEAS stand for what are the parts

Answer 2

Performance measure (goal ie safety ), environment (location/what’s in location), actuators (perform actions through), sensors(how thing will sense things for input)

Question 3

What do we mean by PEAS analysis?

Answer 3

M

Question 4

What is an example of software agent

Answer 4

An AI program/ program to do something in ai, ex program that brakes when it sees a car too close: camera sensors, mechanism to push brake as effector

Question 5

What is abstraction and example

Answer 5

Remove details, focus on bigger idea . Ex. Graph of sibiu

Question 6

Simple reflex agent

Answer 6

Looks at latest percept or 2, acts as a reflex, ex dirty so clean

Question 7

Model based agents

Answer 7

Has a ds that models environment (other situations going on, what my actions will do) makes decisions off of that

Question 8

Goal based agents

Answer 8

Model with a goal, ie destination, more sophisticated

Question 9

Utility based agents

Answer 9

Model, goal, and utility function, considers future state ie will this action help me meet my goal

Question 10

Percept sequence

Answer 10

Everything the agent has perceived so far

Question 11

Uninformed searches

Answer 11

Uninformed is blind, has no info about steps or path cost (good with no additional info, but less effective )

Question 12

Informed searches

Answer 12

Considers problem specific knowledge, may use heuristics or functions that consider extra info, more effective

Question 13

Uninformed searches examples

Answer 13

Breadth first, depth first, iterative deepening depth first, uniforms cost

Question 14

Informed searches examples

Answer 14

Greedy best, a*

Question 15

Graph vs tree based models

Answer 15

Tree has at most 1 parent, may have many children, graph nodes may have many parents

Question 16

G (n) function

Answer 16

Cost of getting to node

Question 17

H (n) function

Answer 17

Cost of getting from node to goal

Question 18

fully observable vs partially observable env

Answer 18

sensors give access to complete state of env at ea pt in time (chess with a clock) (poker)

Question 19

deterministic vs stochatic env

Answer 19

next state of env is completely determined by the current state and action executed by agent(chess with clock)(poker)

Question 20

strategic env

Answer 20

env is deterministic except for actions of other agents(chess with clock)

Question 21

episodic vs sequential

Answer 21

agent’s exp is divided into episodes (perceive then one action) and choice of action in ea episode depends only on the episode itself (part picking robot)(kchess with clock)

Question 22

static vs dynamic env

Answer 22

env is unchanged while an agent is deliberating (poker)(taxi driving)

Question 23

semidynamic env

Answer 23

env itself doesn’t change with passing time but agent’s performance score does(image analysis)

Question 24

discrete vs cont env

Answer 24

limited number of distinct, clearly defined percepts and actions (chess with clock)(taxi driving)

Question 25

single agent vs multiagent env

Answer 25

agent operating by itself in env

Question 26

real world env types?

Answer 26

partially observable, stochastic, sequential, dynamic, continuous, multi-agent

Question 27

greedy best-first search

Answer 27

uses h(n) - cost from n to goal, gets to goal but may not be optimal, can get stuck in loops, keeps nodes in memory

Question 28

A* search

Answer 28

uses f(n) = g(n) + h(n) total cost from n to goal = cost so far to get to n + cost from n to goal; optimal solution when heuristic is admissible, fairly efficient; puts children in fringe list; keeps all nodes

Question 29

heuristic is admissible

Answer 29

h(n)

Question 30

breadth-first search

Answer 30

goes across all children in first node, FIFO put at end of fringe list; lots of time and space

Question 31

depth-first

Answer 31

LIFO, children put at front of fringe list; could go infinitely down

Question 32

iterative deepening depth-first

Answer 32

same as depth first but can have branching factor of say 10, then goes down from next original child … doesn’t keep track forgets memory, more efficient

Question 33

uniform cost search

Answer 33

uses g(n) if g is low puts at front of fringe list, if high puts at end of fringe list; takes timme

Question 34

steps of search (for all)

Answer 34

take node off fringe list, expand by rule, is it goal? if yes return value, if no follow rule for which to expand/where to put children