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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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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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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.