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辅导ECS 170 Introduction to Artificial Intelligence

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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