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Dr. Anupam Shukla
Some material adopted from notes by
Artificial Intelligence by Rich Knight
Artificial Intelligence and Expert System by Patterson
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Introduction
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If human beings can think why not machines?
If machines If machines
can think, How? can not think, Why?
Can they surpass human And what does this
performance? say about the mind?
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What is artificial intelligence?
There are no clear consensus on the definition of AI
INTELLIGENCE
Intelligence is the computational part of the ability to achieve
goals in the world. Varying kinds and degrees of intelligence
occur in people, many animals and some machines.
ARTIFICIAL INTELLIGENCE
It is the science and engineering of making intelligent machines,
especially intelligent computer programs. It is related to the
similar task of using computers to understand human intelligence,but AI does not have to confine itself to methods that are
biologically observable.
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AI is a collection of hard problems which can be solved by
humans and other living things, but for which we dont have
good algorithms for solving.
e. g., understanding spoken natural language, medical
diagnosis, circuit design, learning, self-adaptation,
reasoning, chess playing, proving math theories, etc.
A program that
Acts like human (Turing test)Thinks like human (human-like patterns of thinking steps)
Acts or thinks rationally (logically, correctly)
Other possible AI definitions
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Easy Problems in AI
Its been easier to mechanize many of the high level cognitive
tasks we usually associate with intelligence in people
e. g., symbolic integration, proving theorems, playing chess,
some aspect of medical diagnosis, etc.
Hard Problems in AI
Its been very hard to mechanize tasks that animals can do easily
walking around without running into thingscatching prey and avoiding predators
interpreting complex sensory information (visual, aural, )
modeling the internal states of other animals from their
behavior
working as a team (ants, bees)
Is there a fundamental difference between the two categories?
Why some complex problems (e.g., solving differential equations,
database operations) are not subjects of AI
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Foundations of AIComputerScience &
Engineering
AI
Mathematics
Cognitive
Science
Philosophy
Psychology Linguistics
BiologyEconomics
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Foundations of AI Philosophy: Logic, reasoning, mind as a physical
system, foundations of learning, language andrationality.
Mathematics: Formal representation and proofalgorithms, computation, (un)decidability,(in)tractability, probability.
Psychology: adaptation, phenomena of perception andmotor control.
Economics: formal theory of rational decisions, gametheory.
Linguistics: knowledge represetatio, grammar. Neuroscience: physical substrate for mental activities.
Control theory: homeostatic systems, stability, optimalagent design.
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AI has roots in a number of scientific disciplines computer science and engineering (hardware and software)
philosophy (rules of reasoning)
mathematics (logic, algorithms, optimization)
cognitive science and psychology (modeling high level
human/animal thinking)
neural science (model low level human/animal brain activity)
linguistics
A Brief History of Artificial
Intelligence
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A Brief History of ArtificialIntelligence
The birth of AI (19431956)
Pitts and McCulloch (1943): simplified mathematical
model of neurons (resting/firing states) can realize all
propositional logic primitives (can compute all Turing
computable functions)
Allen Turing: Turing machine and Turing test (1950)
Claude Shannon: information theory; possibility of chess
playing computers
Tracing back to Boole, Aristotle, Euclid (logics,
syllogisms)
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Early enthusiasm (19521969)
1956 Dartmouth conference
John McCarthy (Lisp);
Marvin Minsky (first neural network machine);
Alan Newell and Herbert Simon (GPS);
Emphasize on intelligent general problem solving
GSP (means-ends analysis);Lisp (AI programming language);
Resolution by John Robinson (basis for automatic theorem
proving);
heuristic search (A*, AO*, game tree search)
A Brief History of Artificial
Intelligence
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A Brief History of ArtificialIntelligence
Emphasis on knowledge (1966 1974) domain specific knowledge is the key to overcome
existing difficulties knowledge representation (KR) paradigms
declarative vs. procedural representation Knowledge-based systems (1969 1999)
DENDRAL: the first knowledge intensive system(determining 3D structures of complex chemicalcompounds)
MYCIN: first rule-based expert system (containing 450rules for diagnosing blood infectious diseases)EMYCIN: an ES shell
PROSPECTOR: first knowledge-based system that madesignificant profit (geological ES for mineral deposits)
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AI became an industry (19801989)
wide applications in various domains
commercially available tools Current trends (1990present)
more realistic goals
more practical (application oriented)
distributed AI and intelligent software agents
resurgence of neural networks and emergence of genetic
algorithms
A Brief History of Artificial
Intelligence
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The relational languages like PROLOG [ PROgramming inLOgic] AND LISP [LISt Processing in AI.
LISP is well suited for handling lists, where as PROLOG isdesigned
For logic Programming
A procedural language that offers call for relational function
or a relational language that allows interface with aprocedural.
Recently a number of shell are available, where the userneeds to submit knowledge only and the shall offers the
implementation of both symbolic processing simultaneously.
Programming languages forAI
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At the early stage of programs of AI, commonmachine used for conventional programmingwere also used for AI programming.
AI programs deal with more relationaloperators than number crusting, hence newarchitecture was proposed for the evolution ofAI programs.
Most of this architecture are used in researchlaboratory, and are not available in the opencommercial market. This special architecture,called LISP and PROLOG machine.
Architecture of AI machine
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Possible Approaches
Think
Act
Like
humans Well
GPS
Eliza
Rationalagents
Heuristicsystems
AI tends to
work mostly
in this area
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Think well
Develop formal models of knowledge representation,reasoning, learning, memory, problem solving, thatcan be rendered in algorithms.
There is often an emphasis on a systems that are
provably correct, and guarantee finding an optimalsolution.
Think
Act
Like
humans Well
GPS
Eliza
Rational
agents
Heuristic
systems
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Act well For a given set of inputs, generate anappropriate output that is not necessarilycorrect but gets the job done.
Aheuristic (heuristic rule, heuristic method) is a rule ofthumb, strategy, trick, simplification, or any other kind of devicewhich drastically limits search for solutions in large problemspaces.
Heuristics do not guarantee optimal solutions; in fact, they donot guarantee any solution at all: all that can be said for a
useful heuristic is that it offers solutions which are goodenough most of the time.Feigenbaum and Feldman, 1963, p. 6
Think
Act
Like
humans Well
GPS
Eliza
Rational
agents
Heuristic
systems
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Think like humans Cognitive science approach
Focus not just on behavior and I/Obut also look at reasoning process.
Computational model should reflect how resultswere obtained.
Provide a new language for expressing cognitivetheories and new mechanisms for evaluating them
GPS (General Problem Solver): Goal not just toproduce humanlike behavior (like ELIZA), but toproduce a sequence of steps of the reasoningprocess that was similar to the steps followed by aperson in solving the same task.
Think
Act
Like
humans Well
GPS
Eliza
Rational
agents
Heuristic
systems
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Act like humans
Behaviorist approach.
Not interested in how you get results, just thesimilarity to what human results are.
Exemplified by the Turing Test (Alan Turing,1950).
Think
Act
Like
humans Well
GPS
Eliza
Rational
agents
Heuristic
systems
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Turing Test Three rooms contain a person, a computer,
and an interrogator.
The interrogator can communicate with theother two by teleprinter.
The interrogator tries to determine which isthe person and which is the machine.
The machine tries to fool the interrogator intobelieving that it is the person.
If the machine succeeds, then we concludethat the machine can think.
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Eliza
ELIZA: A program that simulated a psychotherapist interactingwith a patient and successfully passed the Turing Test.
Coded at MIT during 1964-1966 by Joel Weizenbaum.
First script was DOCTOR.
The script was a simple collection of syntactic patterns not
unlike regular expressions Each pattern had an associated reply which might include
bits of the input (after simple transformations (my your)
Weizenbaum was shocked at reactions:
Psychiatrists thought it had potential.
People unequivocally anthropomorphized.
Many thought it solved the NL problem.
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What can AI systems do
Here are some example applications
Computer vision: face recognition from a large set
Robotics: autonomous (mostly) automobile
Natural language processing: simple machine translation
Expert systems: medical diagnosis in a narrow domain
Spoken language systems: ~1000 word continuous speech
Planning and scheduling: Hubble Telescope experiments
Learning: text categorization into ~1000 topics User modeling: Bayesian reasoning in Windows help (the
infamous paper clip)
Games: Grand Master level in chess (world champion),checkers, etc.
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State of the art
Deep Blue defeated the reigning world chess championGarry Kasparov in 1997 Proved a mathematical conjecture (Robbins conjecture)
unsolved for decades No hands across America (driving autonomously 98% of
the time from Pittsburgh to San Diego) During the 1991 Gulf War, US forces deployed an AI
logistics planning and scheduling program that involved upto 50,000 vehicles, cargo, and people
NASA's on-board autonomous planning program controlled
the scheduling of operations for a spacecraft Proverb solves crossword puzzles better than most
humans
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Why is AI different than conventionalprogramming?
Strive for GENERALITY
EXTENSIBILITY
Capture rational deduction patterns
Tackle problems with no algorithmic solution
Represent and manipulate KNOWLEDGE, ratherthan DATA
A new set of representation and programmingtechniques: HEURISTICS
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What cant AI systems do yet?
Understand natural language robustly (e.g., read andunderstand articles in a newspaper)
Surf the web
Interpret an arbitrary visual scene Learn a natural language
Play Go well
Construct plans in dynamic real-time domains
Refocus attention in complex environments Perform life-long learning
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Areas of AI and their inter-dependencies
Search
Vision
PlanningMachine
Learning
Knowledge
RepresentationLogic
Expert
SystemsRoboticsNLP
Traveling Salesman Problem
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states
locations / cities
illegal states
each city may be visited only once
visited cities must be kept as state information
initial state
starting point
no cities visited
successor function (operators)
move from one location to another one
goal testall locations visited
agent at the initial location
path cost
distance between locations
Traveling Salesman Problem
VLSI Layout
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states
positions of components, wires on achip
initial state
incremental: no components placed
complete-state: all components placed(e.g. randomly, manually)
successor function (operators)
incremental: place components, route
wirecomplete-state: move component,move wire
goal test
all components placed
components connected as specified
path cost
may be complex
distance, capacity, number ofconnections per component
VLSI Layout
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Robot Navigationstates
locations
position of actuators
initial state
start position (dependent on the
task)
successor function (operators)
movement, actions of actuators
goal testtask-dependent
path cost
may be very complex
distance, energy consumption
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Assembly Sequencing
stateslocation of components
initial state
no components assembled
successor function (operators)
place component
goal test
system fully assembled
path cost
number of moves
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Searching for Solutions
traversal of the search spacefrom the initial state to a goal state
legal sequence of actions as defined by successor function (operators)
general procedure
check for goal state
expand the current state
determine the set of reachable states
return failure if the set is emptyselect one from the set of reachable states
move to the selected state
a search tree is generated
nodes are added as more states are visited
Some references;
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5/26/2
012 33
AI 1
Some references;
Daniel C. Dennet.Consciousness
explained. M. Posner (edt.)
Foundations ofcognitive science
Francisco J. Varelaet al. The EmbodiedMind
J.-P. Dupuy. The
mechanization ofthe mind
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/ / 3
Some references Understanding Intelligence by RolfPfeifer and Christian Scheier. Artificial Intelligence: Structures and
Strategies for Complex Problem-solving by George Luger.
Computation and Intelligence:Collective readings edited by GeorgeLuger.
Paradigms of Artificial IntelligenceProgramming: Case Studies inCommon Lisp by Peter Norvig.