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日期:2022-05-30 08:02

Introduction to Artificial Intelligence

May 24, 2022

Administrative stuff

Final exam next week

Midterm exam grades – real soon now

Quiz 4 – Tuesday of next week

HW5 – out this week, due next week – it’s about…

2

Learning

Learning is the “holy grail” of artificial intelligence

because it is the essential element in intelligence

-- both natural and artificial. Why?

Learning

Learning is the “holy grail” of artificial intelligence

because it is the essential element in intelligence

-- both natural and artificial. Why?

People change as a result of their experiences.

We adapt to new situations and learn from our

experiences. An intelligent agent must be able to

do the same.

Learning

Learning is the “holy grail” of artificial intelligence

because it is the essential element in intelligence

-- both natural and artificial. Why?

It’s probably impossible to build in the large

amount of knowledge required for any realistic

domain by hand.

Learning

Learning is the “holy grail” of artificial intelligence

because it is the essential element in intelligence

-- both natural and artificial. Why?

Dealing with novel input inherently requires

adaptation and learning (otherwise the system

will only be able to deal with situations for which it

was designed).

Learning

Learning is the “holy grail” of artificial intelligence

because it is the essential element in intelligence

-- both natural and artificial. Why?

Dealing with changing environments requires

learning (since the knowledge base may

otherwise become obsolete).

Learning

Learning is the “holy grail” of artificial intelligence

because it is the essential element in intelligence

-- both natural and artificial. Why?

It’s the only way that artificially intelligent systems

will seem really intelligent to people.

Learning

Definition: learning is the adaptive changes that occur in

a system which enable that system to perform the same

task or similar tasks more efficiently or more effectively

over time.

This could mean:

The range of behaviors is expanded: the agent can

do more

The accuracy on tasks is improved: the agent can

do things better

The speed is improved: the agent can do things faster

What kinds of learning do we do?

Here are some examples of the kinds of learning

that people do. This is not an exhaustive list...

What kinds of learning do we do?

Rote learning

“1 times 3 is 3, 2 times 3 is 6, 3 times 3 is 9,...”

Taking advice from others

“If you have a choice between sliding and jumping in the peg puzzle,

always jump.”

Learning from problem solving experiences

“I have to stack these blocks again...what do I know from last time that’ll

make this time easier so I don’t have to do the planning thing again?”

Learning from examples

“Hmmm, last time at the watering hole, Og was eaten. The time before

that, Zorg was eaten. I’m getting kind of thirsty, should I…”

Learning by experimentation and discovery

“I wonder what will happen if I move this pawn to that space?”

What kinds of learning do AI folks study?

supervised learning: given a set of pre-classified

examples, learn to classify a new instance into its

appropriate class

unsupervised learning: learning classifications when the

examples are not already classified

reinforcement learning: learning what to do based on

rewards and punishments

analytic learning: learning to reason faster

(again, this is not an exhaustive list)

What kinds of learning do AI folks study?

supervised learning: given a set of pre-classified

examples, learn to classify a new instance into its

appropriate class

unsupervised learning: learning classifications when the

examples are not already classified

reinforcement learning: learning what to do based on

rewards and punishments

analytic learning: learning to reason faster

(again, this is not an exhaustive list)

Example: Supervised learning of concept

Say it’s important for your system to know what an arch is,

in a structural sense. You want to teach the program by

a series of examples. You tell your system that this is an

arch:

What does your system know

about “archness” now?

Example: Supervised learning of concept

Now you tell it that this isn’t an arch:

What does your system know

about “archness” now?

Example: Supervised learning of concept

And then you tell it that this isn’t an arch:

What does your system know

about “archness” now?

Example: Supervised learning of concept

This may not seem all that exciting, but consider the same

sort of task in a different domain....

What does your system know

about “archness” now?

Example: Supervised learning of concept

What about classifying chickens being processed for retail

sale? “They’ll buy this one, but they wouldn’t buy that one…”

Example: Supervised learning of concept

What about classifying chickens being processed for retail

sale? “They’ll buy this one, but they wouldn’t buy that one…”

Example: Supervised learning of concept

What about classifying chickens being processed for retail

sale? “They’ll buy this one, but they wouldn’t buy that one…”

Example: Supervised learning of concept

What does your system

know about “winning

horses” now?

Example: Supervised learning of concept

Let’s go back to the simpler arch problem and see how a

computer program could learn the concept

Example: Supervised learning of concept

So let’s say our arch-learning program doesn’t yet have

a concept for arch. We need to provide a representation

language for these arch examples. A semantic network

with nodes like “upright block” and “sideways block”

and relations like “supports” and “has_part” works.

This is now what it knows about

“archness”...its internalized arch


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