Chapter 13

Artificial Intelligence

Chapter Goals

• Discuss types of problems that– humans do best– computers do best

• Turing test

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Chapter Goals

• Knowledge representation and semantic networks

• Search Trees

• Expert systems

• Biological and artificial neural networks13-3

Chapter Goals

• Natural language processing

• Natural language comprehension ambiguities

• Understand the main kinds of AI used in autonomous robots

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What is Artificial Intelligence?

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All I have to offer is the truth…

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• The truth is, Hollywood movies are great, but they are not reality!

AI Today

• No single computer today is close to being considered “intelligent” like a human

• However, computers can solve particular human-like tasks are typically considered to require intelligence, such as:– Playing chess– Diagnosing diseases– Identifying objects in images

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What is Artificial Intelligence?

• This is an important question…• Computer Science has a particular viewpoint

• There are 2 Big Slices:– A Practical Definition (Task oriented)– A Philosophical Definition (Perception Oriented)

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What is Artificial Intelligence?

• Practical Definition: Making a computer do things tasks that are easy for humans to do, like:–Find kitty (aka “Machine Vision”)–Use natural human languages–Apply expert human knowledge–Play chess

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What is Artificial Intelligence?

• Philosophical Definition:–Making a computer fool humans into

thinking it is a human (Turing test). –(I perceive that you are intelligent).

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The Turing Test

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The Turing Test

• Alan Turing wrote a landmark paper: “Can machines think?”

• How will we know when we’ve succeeded?

• The Turing test used empirically determine if a computer is “intelligent”

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The Turing Test

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Figure 13.2 In a Turing test, the interrogator must determine which respondent is the computer and which is the human

Tasks

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Us vs. The Machines

• Some tasks are easy for computers• Some tasks are hard for computers• Some tasks are easy for humans• Some tasks are hard for humans

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Easy For a Computer…

• Adding a thousand four-digit numbers• Counting the letters in a book• Searching a list of 1,000,000 numbers for

duplicates• Matching finger prints

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Easy for a Human…

Where’s Kitty? • A computer would have difficulty pointing out the cat in

this picture, which is easy for a human

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Specific AI Tasks

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SPECIFIC AI TECHNIQUES

• !MAGIC FREE ZONE!

• We will look at techniques to perform specific tasks that we consider to be “intelligent”

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Knowledge Representation

• Humans use knowledge for certain tasks• AI systems must have a way to represent

knowledge

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Semantic Networks

• Semantic network A knowledge representation technique that focuses on the relationships between objects

• A directed graph is used to represent a semantic network or net

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Semantic Networks

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Figure 13.3 A semantic network

Search Trees

• Search tree A structure that represents all possible moves in a game, for both you and your opponent

• The paths down a search tree represent a series of decisions made by the players

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Search Tree Example: Nim

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Figure 13.4 A search tree for a simplified version of Nim

Search Trees

• Search tree also work for more complicated games such as chess

• Because these trees are so large, only a fraction of the tree can be analyzed in a reasonable time limit, even with modern computing power

• Now, the biggest supercomputers compete against each other

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Expert Systems

• Simulates a Human Expert– Car Mechanic– Medical Doctor– Gardener

• Using:– A Set of Rules – The “Data”– An Inference Engine – The SW that asks questions and

applies the rules

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Expert Systems

• Example: What type of treatment should I put on my lawn?– NONE—apply no treatment at this time– TURF—apply a turf-building treatment– WEED—apply a weed-killing treatment– BUG—apply a bug-killing treatment– FEED—apply a basic fertilizer treatment– WEEDFEED—apply a weed-killing and fertilizer

combination treatment

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Expert Systems

• Questions:

– BARE—the lawn has large, bare areas

– SPARSE—the lawn is generally thin

– WEEDS—the lawn contains many weeds

– BUGS—the lawn shows evidence of bugs

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Expert Systems

• Rules:

– if (BARE) then TURF

– if (SPARSE and not WEEDS) then FEED

– if (BUGS and not SPARSE) then BUG

– if (WEEDS and not SPARSE) then WEED

– if (WEEDS and SPARSE) then WEEDFEED

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Expert Systems

• An execution of our inference engine– System: Does the lawn have large, bare areas?– User: No– System: Does the lawn show evidence of bugs?– User: No– System: Is the lawn generally thin?– User: Yes– System: Does the lawn contain significant weeds?– User: Yes– System: You should apply a weed-killing and fertilizer

combination treatment.

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Artificial Neural Network

• Attempts to mimic the actions of the neural networks of the human brain

• Good at things like:– Will it rain?– Is that a Kitty?– Is that a male or female face?

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Biological Neurons

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Figure 13.6 A biological neuron

Neural Networks

– Each neuron has multiple input tentacles called dendrites and one primary output tentacle called an axon

– A series of connected neurons forms a pathway– The gap between axons and dendrites is called a

synapse– Strong connections creates a strong pathway

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Biological Neural Nets

• Your brain

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Neural Networks

• Each connection between elements has a particular strength

• Particular combinations of inputs will make it through the network and produce an output

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Artificial Neural Nets

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Artificial Neural Networks

• The process of adjusting the connection strength is called training

• A neural net can be trained to produce whatever results are required

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Natural Language Processing• Three separate problems– Voice synthesis

• Recreating human speech• Making computers talk• Easy to do

– Voice recognition• recognizing human words• Making computers listen• Harder to do

– Voice comprehension• Making computers understanding• Very hard to do

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Voice Recognition is HARD

• The sounds that each person makes when speaking are unique– unique shape to our mouth, tongue, throat, and nasal

cavities that affect the pitch and resonance of our spoken voice

– mumbling, volume, regional accents, complicate the problem

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Voice Recognition is HARD

• Humans speak in a continuous, flowing manner– Words are strung together into sentences– Sometimes it’s difficult to distinguish between phrases like “ice

cream” and “I scream”– Also, homonyms such as “I” and “eye” or “see” and “sea”

• Humans can often clarify these situations by the context of the sentence, but that processing requires another level of comprehension

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Voice Comprehension is HARD

• Human speech is inherently ambiguous• 3 kinds of ambiguity– Lexical– Syntactic– Referential

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Lexical Ambiguity

– The meaning of individual words

Time flies like an arrow.

– What do you mean, “flies” ?– The computer gets confused.

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Syntactic Ambiguity

• Phrases can be put together in various ways

I saw the Grand Canyon flying to New York.

• What is flying, the Grand Canyon or me?

• The computer gets confused.

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Referential Ambiguity

• When using pronouns, for example:

The brick fell on the computer but it is not broken.

• To what does “it” refer to?• The computer gets confused.

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Robots and AI

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Autonomy

• Some robots use AI• Some robots do NOT use AI

• The difference is AUTONOMY• Autonomy – the ability to adapt to new

situations without outside help

Autonomy requires AI13-47

Robotics

• Not Autonomous – Uses “Simpler”

algorithm consisting of a list of steps

• Autonomous – Uses more

“Complex” Artificial Intelligence

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2 Robotic AI Architectures

• Sense Plan Act– The “old way”– A Top-Down approach

• Subsumption– The “newer way”– A Bottom-Up approach– Similar to how nature works

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Robotics - Sense Plan Act Architecture

• In the sense-plan-act (SPA) paradigm the world is represented in a complex semantic net in which the sensors on the robot are used to capture the data to build up the net

• The “old way”• Very hard to do…

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Robotics - Subsumption Architecture

• Rather than trying to model the entire world all the time, the robot is given a simple set of behaviors each associated with the part of the world necessary for that behavior

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