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Artificial Intelligence
BE IT
Urmila Kalshetti
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Objectives
To introduce the basic principles and applicationsof Artificial Intelligence
To Understand the basic areas of artificialintelligence such as problem solving, knowledgerepresentation, reasoning, planning, perception,vision and learning
To develop the ability to design and implementkey components of intelligent agents and expertsystems of moderate complexity
To study different Heuristic Search Techniquesand their applications
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References Text Books:
1. Artificial Intelligence: A Modern Approach by Peter and NorvigISBN-0-13-103805-2,
2. Artificial Intelligence by Elaine Rich, Kevin Knight and Nair ISBN-978-0-07-008770-5, TMH
Reference Books:1. George F. Luger , Artificial Intelligence: Structures and Strategies
for Complex Problem Solving, Pearson, ISBN-10: 0321545893
2. N.P. Padhy, Artificial Intelligence And Intelligent Systems, OxfordUniversity Publishers, ISBN 9780195671544
3. Ivan Bratko, PROLOG : Programming for Artificial Intelligence,Pearson Education, 3 Edition, ISBN10: 0-201-40375-7
4. Saroj Kaushik, Artificial Intelligence, Cengage Learning, , ISBN-13:9788131510995
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Why study AI?
AI helps computer scientists and engineers build more useful
and user-friendly computers,
psychologists, linguists, and philosophers understand
the principles that constitute what we callintelligence.
AI is an interdisciplinary field of study.
Many ideas and techniques now standard in CS
(symbolic computation, time sharing, objects,declarative programming, . . . ) were pioneeredby AI-related research.
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AI is among us!
Recent applications using AI techniques:
Sony Aibo
Entertainment robot with pet-like behaviour
Dragon NaturallySpeaking
(Dictation and voice recognition software)
(http://www.dragonsys.com)
You talk, it types.
to use our voice to create documents, write papers, send email, andsearch the Web
KIROBO Robot project
Japan's Kirobo spacebot performs on video There are already quite a few robots on the International Space
Station (namely, Robonaut and a bunch ofSPHEREs), but later thisyear, a little humanoid from Japan will be joining the team:
TOPIO, a humanoid robot,
played ping pong at
Tokyo International RobotExhibition (IREX) 2009.
http://localhost/var/www/apps/conversion/tmp/scratch_4/Aibo%20-%20Sony%20Robo%20Dog%20%20%20%20-%20YouTube.flvhttp://www.dragonsys.com/http://spectrum.ieee.org/tag/robonauthttp://spectrum.ieee.org/tag/sphereshttp://spectrum.ieee.org/tag/robonauthttp://spectrum.ieee.org/tag/sphereshttp://en.wikipedia.org/wiki/TOPIOhttp://en.wikipedia.org/wiki/Ping_ponghttp://en.wikipedia.org/wiki/International_Robot_Exhibitionhttp://en.wikipedia.org/wiki/International_Robot_Exhibitionhttp://en.wikipedia.org/wiki/International_Robot_Exhibitionhttp://en.wikipedia.org/wiki/International_Robot_Exhibitionhttp://en.wikipedia.org/wiki/Ping_ponghttp://en.wikipedia.org/wiki/TOPIOhttp://spectrum.ieee.org/tag/sphereshttp://spectrum.ieee.org/tag/robonauthttp://www.dragonsys.com/http://localhost/var/www/apps/conversion/tmp/scratch_4/Aibo%20-%20Sony%20Robo%20Dog%20%20%20%20-%20YouTube.flvhttp://localhost/var/www/apps/conversion/tmp/scratch_4/Aibo%20-%20Sony%20Robo%20Dog%20%20%20%20-%20YouTube.flv -
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AI is among us! More applications using AI techniques:
Honda Humanoid Robot Demo walking robot
Deep Blue(now retired) a new version (Watson) Over three nights, it took on two of the all-time most successful human
players of the game and beat them in front of millions of television viewers inFEB, 2011.
(http://researchweb.watson.ibm.com/deepblue) Mars Pathfinder (1997)
Autonomous land vehicle sent to Mars
(http://mars.jpl.nasa.gov/MPF)
NASA's Juno Spacecraft Launches to Jupiter
Juno's detailed study of the largest planet in our solar system will helpreveal Jupiter's origin and evolution.
Marvel Real-time expert system for monitoring data sent by Voyager spacecraft.
ChatterBot Eliza She is programmed to behave as a Rogerian psychotherapist, and is an
interesting example of the limitations of early artificial intelligence programs
http://nlp-addiction.com/eliza/
http://mars.jpl.nasa.gov/MPFhttp://nlp-addiction.com/eliza/http://nlp-addiction.com/eliza/http://nlp-addiction.com/eliza/http://nlp-addiction.com/eliza/http://nlp-addiction.com/eliza/http://nlp-addiction.com/eliza/http://nlp-addiction.com/eliza/http://nlp-addiction.com/eliza/http://mars.jpl.nasa.gov/MPF -
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What is AI?
A scientific and engineering discipline devoted
to:
Understanding principles that make intelligent
behavior possible in natural or artificial systems;
Developing methods for the design and
implementation of useful, intelligent artifacts
Artificial Intelligence is concerned with thedesign of intelligence in an artificial device.
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What is AI?
What is intelligence?
Is it that which characterize humans? Or is there anabsolute standard of judgement?
Accordingly there are two possibilities:
A system with intelligence is expected to behave as intelligentlyas a human
A system with intelligence is expected to behave in the bestpossible manner
Secondly what type of behavior are we talking about?
Are we looking at the thought process or reasoning ability of thesystem?
Or are we only interested in the final manifestations of thesystem in terms of its actions?
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What is AI?
different interpretations have been used by
different researchers
AI is about designing systems that are as
intelligent as humans.
understand human thought and an effort to build
machines that emulate the human thought process
The concept of the Turing Test
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Turing Test
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AI is pretty hard stuff!
I went to the grocery store, I saw the milk on theshelf and I bought it.
What did I buy? The milk?
The shelf?
The store?
An awful lot of knowledge of the world is needed toanswer simple questions like this one.
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Typical AI problems
Recognizing people, objects.
Communicating (through natural language).
Navigating around obstacles on the streets Expert tasks include:
Medical diagnosis.
Mathematical problem solving Playing games like chess
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What is AI?
Views of AI fall into four categories:
Thinking humanly Thinking rationallyActing humanly Acting rationally
The textbook advocates "acting rationally"Rationally means sensibly, logically.
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Acting humanly: Turing Test Turing (1950) "Computing machinery and intelligence":
"Can machines think?" "Can machines behaveintelligently?"
Operational test for intelligent behavior: the Imitation Game
Predicted that by 2000, a machine might have a 30% chance
of fooling a lay person for 5 minutes Anticipated all major arguments against AI in following 50
years
Suggested major components of AI: knowledge, reasoning,language understanding, learning
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Thinking humanly: cognitive modeling
1960s "cognitive revolution": information-processingpsychology
Getting inside the actual working of human minds Introspection
Pshychological experiments
-- How to validate? Requires1) Predicting and testing behavior of human subjects (top-down)
or 2) Direct identification from neurological data (bottom-up)
Both approaches (roughly, Cognitive Science andCognitive Neuroscience) are now distinct from AI
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Thinking rationally: "laws of thought"
Aristotle: what are correct arguments/thought processes?(right thinking)
Socrates is a man. All men are mortal Therefore Socrates is mortal.
Several Greek schools developed various forms oflogic:notation and rules of derivation for thoughts.
Direct line through mathematics and philosophy to modernAI
Challenges:1. Stating informal knowledge in formal in formal terms
2. Solving a problem in principle and doing so in practice
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Acting rationally: rational agent
Rational behavior: doing the right thing
The right thing: that which is expected tomaximize goal achievement, given the availableinformation
Doesn't necessarily involve thinking e.g.,blinking reflex but thinking should be in theservice of rational action
Laws of thought- emphasis is on correct inference
Correct inference is not all of rationality becausethere are often situations where there is noprovably correct thing to do, yet something muststill be done.
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AI prehistory
Philosophy Logic, methods of reasoning, mind as physicalsystem foundations of learning, language,rationality
Mathematics Formal representation and proof algorithms,computation, (un)decidability, (in)tractability,probability
Economics utility, decision theory
Neuroscience physical substrate for mental activity
Psychology phenomena of perception and motor control,experimental techniques
Computer building fast computers
engineering Control theory design systems that maximize an objective
function over time
Linguistics knowledge representation, grammar
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Abridged history of AI
1943 McCulloch & Pitts: Boolean circuit model of brain
1950 Turing's "Computing Machinery and Intelligence"
1956 Dartmouth meeting: "Artificial Intelligence" adopted
195269 Look, Ma, no hands!
1950s Early AI programs, including Samuel's checkers
program, Newell & Simon's Logic Theorist,Gelernter's Geometry Engine
1965 Robinson's complete algorithm for logical reasoning
196673 AI discovers computational complexityNeural network research almost disappears
196979 Early development of knowledge-based systems
1980-- AI becomes an industry 1986-- Neural networks return to popularity
1987-- AI becomes a science
1995-- The emergence of intelligent agents
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Task Domains of AI
Routine Tasks Perception
Natural language Understanding, generation, translation
Commonsense reasoning
Robot control
Formal Tasks Games
Chess, Backgammon, Checkers
Mathematics Geometry, logic, integral calculus
Expert Tasks Engineering
Designing, fault finding, manufacturing planning
Scientific analysis
Financial analysis
Medical Analysis
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Agents
An agent is anything that can be viewed as perceivingits environment through sensors and acting uponthat environment through actuators
Human agent: eyes, ears, and other organs forsensors; hands, legs, mouth, and other body partsfor actuators
Robotic agent: cameras and infrared range finders forsensors; various motors for actuators
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Examples of agents
Humans can be looked upon as agents. Theyhave eyes, ears, skin, taste buds, etc. forsensors; and hands, fingers, legs, mouth for
effectors .
Robots are agents. Robots may have camera,
sonar, infrared, bumper, etc. for sensors. Theycan have grippers, wheels, lights, speakers,etc. for actuators.
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Agents and environments
The agentfunction maps from percept histories to actions:
[f: P*A]
The agentprogram runs on the physical architecture to
producef agent = architecture + program
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Vacuum-cleaner world
Percepts: location and contents, e.g., [A,Dirty]
Actions: Left, Right, Suck, NoOp
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A vacuum-cleaner agent
Percept Sequence The complete history of everything the agent has ever
perceived
Agent function Maps any given percept sequence to an action
Abstract mathematical description
Agent program Concrete implementation running on the agent
architecture
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Vacuum-cleaner world
Simple agent function
What is the right way to fill out the table?
What makes an agent good or bad, intelligent or stupid?
Percept Sequence Action
[A, clean] Right
[A, Dirty] Suck
[B, clean] Left
[B, Dirty] Suck
[A, clean], [A, clean] Right
[A, clean], [A, Dirty] Suck
.
.[A, clean], [A, clean], [A, clean] Right
[A, clean], [A, clean], [A, Dirty] Suck
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Rational agents
An agent should strive to "do the right thing", based on whatit can perceive and the actions it can perform. The right actionis the one that will cause the agent to be most successful.
Performance measure: An objective criterion for success of anagent's behavior
E.g., performance measure of a vacuum-cleaner agent could
be amount of dirt cleaned up, amount of time taken, amountof electricity consumed, amount of noise generated, etc.
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Rational agents
RationalAgent: For each possible percept
sequence, a rational agent should select an
action that is expected to maximize its
performance measure, given the evidenceprovided by the percept sequence and
whatever built-in knowledge the agent has.
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Rational agents
Rationality is distinct from omniscience (all-knowing with infinite knowledge)
Agents can perform actions in order to modifyfuture percepts so as to obtain useful information(information gathering, exploration)
An agent is autonomous if its behavior isdetermined by its own experience (with ability tolearn and adapt)
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PEAS
PEAS: Performance measure, Environment,Actuators, Sensors
Consider, e.g., the task of designing an automatedtaxi driver:
Performance measure
Environment
Actuators Sensors
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PEAS
Consider, e.g., the task of designing an automatedtaxi driver:
Performance measure: Safe, fast, legal, comfortable trip,
maximize profits Environment: Roads, other traffic, pedestrians, customers
Actuators: Steering wheel, accelerator, brake, signal, horn
Sensors: Cameras, sonar, speedometer, GPS, odometer,engine sensors, keyboard
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PEAS
Agent: Medical diagnosis system
Performance measure: Healthy patient,
minimize costs, lawsuits
Environment: Patient, hospital, staff
Actuators: Screen display (questions, tests,
diagnoses, treatments, referrals)
Sensors: Keyboard (entry of symptoms,
findings, patient's answers)
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PEAS
Agent: Part-picking robot
Performance measure: Percentage of parts in
correct bins
Environment: Conveyor belt with parts, bins
Actuators: Jointed arm and hand
Sensors: Camera, joint angle sensors
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PEAS
Agent: Interactive English tutor
Performance measure: Maximize student's
score on test
Environment: Set of students
Actuators: Screen display (exercises,
suggestions, corrections)
Sensors: Keyboard
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Software agents/Softbots Softbot designed to fly a flight simulator for a large
commercial airplane.
Softbot designed to scan internet news sources andshow the interesting items to its customers It will need NLP ability
It will need to learn what each customer is interested in It will need change its plan dyanmically
soft robots based on natural forms, including squidand starfish. Whitesides envisions using the
pneumatically powered robots to aid disaster recoveryefforts by squeezing into the rubble left by anearthquake to locate survivors, or as a way to free up asurgeons hands in the operating room.http://news.harvard.edu/gazette/story/2011/12/soft-
bots/
http://news.harvard.edu/gazette/story/2011/12/soft-bots/http://news.harvard.edu/gazette/story/2011/12/soft-bots/http://news.harvard.edu/gazette/story/2011/12/soft-bots/http://news.harvard.edu/gazette/story/2011/12/soft-bots/http://news.harvard.edu/gazette/story/2011/12/soft-bots/ -
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Environment types
Fully observable (vs. partially observable): An agent's sensors give itaccess to the complete state of the environment at each point intime. Partially observable- if noisy and inaccurate sensors
Deterministic (vs. stochastic): The next state of the environment iscompletely determined by the current state and the actionexecuted by the agent. (If the environment is deterministic exceptfor the actions of other agents, then the environment is strategic)
Episodic (vs. sequential): The agent's experience is divided intoatomic "episodes" (each episode consists of the agent perceivingand then performing a single action), and the choice of action ineach episode depends only on the episode itself.
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Environment types
Static (vs. dynamic): The environment is unchangedwhile an agent is deliberating. (The environment issemidynamic if the environment itself does notchange with the passage of time but the agent's
performance score does)
Discrete (vs. continuous): A limited number ofdistinct, clearly defined percepts and actions.
Single agent (vs. multiagent): An agent operating byitself in an environment.
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Environment types
Chess with Chess without Taxi drivinga clock a clock
Fully observable Yes Yes No
Deterministic Strategic Strategic No
Episodic No No No
Static Semi Yes NoDiscrete Yes Yes No
Single agent No No No
The environment type largely determines the agent design
The real world is (of course) partially observable, stochastic, sequential,
dynamic, continuous, multi-agent
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Agent types
Four basic types in order of increasing
generality:
Simple reflex agents
Model-based reflex agents
Goal-based agents
Utility-based agents
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Simple reflex agents
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Simple reflex agents
\input{algorithms/d-agent-algorithm}
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Model-based reflex agents
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Model-based reflex agents
\input{algorithms/d+-agent-algorithm}
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Goal-based agents
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Utility-based agents
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Learning agents
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Chatterbot Eliza