Artificial intelligence on the web
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Artificial Intelligence on the Web. Wednesday, Week 9. Intelligence Exercise. What is Intelligence? What activities require intelligence?. AI Definition #1. “The automation of activities that we associate with human thinking, activities such as decision-making, problem solving, learning…”.

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Artificial Intelligence on the Web

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Artificial intelligence on the web

Artificial Intelligence on the Web

Wednesday, Week 9

Intelligence exercise

Intelligence Exercise

  • What is Intelligence?

  • What activities require intelligence?

Ai definition 1

AI Definition #1

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

Ai definition 2

AI Definition #2

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

Ai definition 3

AI Definition #3

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

Ai definition 4

AI Definition #4

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

Main ai definitions

Main AI Definitions

  • Systems that think like humans

  • Systems that act like humans

  • Systems that act rationally

Thinking humanly

Thinking humanly:

  • 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.

Acting humanly

Acting humanly:

  • 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 rationally

Acting Rationally:

  • “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.

Ai topics

AI Topics

  • 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.

Search problems

Search Problems

  • 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.

Search problems1

Search Problems

  • 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.

Search problems2

Search Problems

  • 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.

Robot example

Robot Example

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

















Search tree

Search Tree

  • 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…

Search tree1

Search Tree


















Navigating a search tree

Navigating a Search Tree

  • 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.

Search efficiency

Search Efficiency

  • 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.

Making search more efficient

Making Search More Efficient

  • 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.

Speaking of chess

Speaking of Chess…

  • 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?

General reasoning

General Reasoning

  • 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

General reasoning1

General Reasoning

  • 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.

A big stumbling block

A Big Stumbling Block

  • 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 as a tool in ai

Probability as a Tool in AI

  • 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

Bayes rule

Bayes’ Rule

  • Let’s say I have a fever. I want to know the following:


  • I do know this:

    • P(FEVER | PNEUMONIA) = .9

    • P(PNEUMONIA) = .001

    • P(FEVER) = .1

Bayes rule1

Bayes’ Rule

P(E | H) * P(H)

P(H | 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.


Bayes rule2

Bayes’ Rule

  • 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.

Bayes nets

Bayes’ Nets

  • A nice approach to handling general reasoning while taking probabilities into account.

  • Here is an example…

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