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Artificial Intelligence on the WebPowerPoint Presentation

Artificial Intelligence on the Web

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Artificial Intelligence on the Web

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Artificial Intelligence on the Web

Wednesday, Week 9

- What is Intelligence?
- What activities require intelligence?

- “The automation of activities that we associate with human thinking, activities such as decision-making, problem solving, learning…”

- “The study of how to make computers do things at which, at the moment, people are better.”

- “The study of computations that make it possible to perceive, reason, and act.”

- “The branch of computer science that is concerned with the automation of intelligent behavior.”

- Systems that think like humans
- Systems that act like humans
- Systems that act rationally

- Problem is figuring out how humans think.
- This is an interesting question apart from AI.
- We are concerned with solving the problem as a human would.

- This idea is pretty much summed up in the Turing Test.
- Can we get a program to seem human enough to fool a human interrogator?

- “Acting to achieve one’s goals, given one’s beliefs.”
- So, based on what we know about the world, we should always do the right thing.
- It is generally too difficult to find the right thing to do.

- Let’s focus on Definition #2: Getting computers to do things that human’s are currently better at.
- This sweeps away troubling philosophical questions, and allows us to take an engineering perspective.

- Many AI problems involve finding a sequence of actions to reach a goal.
- Chess - find a series of moves to win a game.
- Robot control - find a series of movements that leads to a particular room.

- We formalize search by dividing the world into a set of states and actions.
- States:
- Chess - legal board positions.
- Robot navigation - the robot’s current room.

- Actions:
- Chess - Legal chess moves.
- Robot - move East, West, North or South.

- We need to know two more things:
- The successor function tells us how actions change the state.
- The goal state tells us where we are trying to get.

- Our robot is trying to get from A to P.

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

- Each of our four actions will result in a new state.
- From each of those new states, we again have four possible actions to choose from.
- The process can be viewed as a tree…

A

North

West

East

South

A

E

B

A

North

West

East

South

…

…

A

I

E

F

- We can move through a search tree in different ways.
- One possibility: Breadth First Search
- First consider every possible action sequence of length N.
- Then move on to every possible action sequence of length N+1.

- We’ll consider other options in lab.

- With breadth first search, how large will our tree get before we reach P?

- 46 = 4096.
- In general?

- BD
- B is the branching factor - The number of actions.
- D is the depth - The number of steps to the goal.

- We can do better if we have an evaluation function - something that tells us if one state is better than another.
- Chess is a good example:
- Branching factor is around 35.
- Number of moves until goal is about 100.
- Search tree size: 35100

- We can do much better by using board evaluation - some configurations are clearly better than others.

- This is an example of an adversarial game.
- In this sort of search we need to consider:
- The results of our own actions AND
- The possible responses of our opponent.

- What would the tree look like?
- …

- Our two examples so far don’t really feel like intelligence.
- What if our states are sets of logical claims?
- Germany is a country.
- If something is a country, it has a flag.

- Our goals are to answer logical questions:
- Does Germany have a flag?

- Actions are logical operators:
- (A AND A->B) -> B

- Intelligence through theorem proving.
- This was a popular idea early in the history of AI.
- Can you guess what problems arise?

- The state space is huge.
- The action space is big.
- It relies on statements being either true or false, when we usually don’t know for sure.

- Our discussion so far has pre-supposed that the world is deterministic and knowledge is certain:
- If the robot tries to move North, he always succeeds.
- Every country ALWAYS has a flag.

- In fact, we almost never have determinism or certainty.

- Probability theory gives us a formal framework for reasoning under uncertainty.
- Some notation:
- P(A) = the probability that statement A is true.
- P(SNOW_TOMORROW) = .4
- 40% chance it will snow. 60% chance it will not.

- P(A | B) = the probability that A is true if we know B to be true.
- P(SNOW_TOMORROW | SUMMER) = .001

- P(A) = the probability that statement A is true.

- Let’s say I have a fever. I want to know the following:
- P(PNEUMONIA | FEVER)

- I do know this:
- P(FEVER | PNEUMONIA) = .9
- P(PNEUMONIA) = .001
- P(FEVER) = .1

P(E | H) * P(H)

P(H | E) =

P(E)

- Where H is a hypothesis and E is evidence.
- P(PNEU. | FEV.)= P(FEV. | PNEU.) * P(PNEU.)
- P(PNEU. | FEV.) = .9 * .001 / .1 = .009
- Why is Bayes’ rule helpful?
- We want one probability, we need three others to get it.

P(FEV.)

- Let’s ask a doctor:
- How likely is it that a patient with pneumonia has a fever?
- “Very likely. I’d say 90%” EASY

- What is the probability that a patient with fever has pneumonia?
- “I dunno. People get fevers for all sorts of reasons. Flu, infections, etc…” HARD

- This happens all the time. It is often easy to estimate a conditional probability in one direction, and not the other.

- How likely is it that a patient with pneumonia has a fever?

- A nice approach to handling general reasoning while taking probabilities into account.
- Here is an example…