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How the electronic mind can How the electronic mind can emulate the human mind: some emulate the human mind: some

applications of Artificial applications of Artificial Intelligence Intelligence

77thth International Interdisciplinary Seminar International Interdisciplinary Seminar

Luca Arcara, Federico Cassoli, Mattia Ferrini

Politecnico di Milano – Campus of Como

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AgendaAgenda

IntroductionIntroduction

Expert SystemsExpert Systems

Neural NetworksNeural Networks

A sample applicationA sample application

Genetic AlgorithmsGenetic Algorithms

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IntroductionIntroduction

What is Artificial Intelligence?What is Artificial Intelligence?

Systems that think like humansSystems that think like humans

Systems that think rationallySystems that think rationally

Systems that act like humansSystems that act like humans

Systems that act rationallySystems that act rationally

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Acting humanly – the Turing testActing humanly – the Turing test

Described in “Computing machinery and Described in “Computing machinery and intelligence” – Turing (1950)intelligence” – Turing (1950)

““Can machines think?” becomes “Can machines Can machines think?” becomes “Can machines behave intelligently?”behave intelligently?”

Operational test for intelligent behavior: the Operational test for intelligent behavior: the Imitation Game.Imitation Game.

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Acting humanly – the Turing testActing humanly – the Turing test

Suggested major components of AISuggested major components of AI KnowledgeKnowledge ReasoningReasoning Language understandingLanguage understanding LearningLearning

Problem: Turing test is not Problem: Turing test is not reproduciblereproducible, , constructiveconstructive or apt to or apt to mathematical mathematical analysisanalysis

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Subjects linked to A. I.Subjects linked to A. I.PhilosophyPhilosophy

Logic, methods of reasoningLogic, methods of reasoning

Mind as physical systemMind as physical system

Foundations of learning, Foundations of learning, language, rationalitylanguage, rationality

MathematicsMathematicsFormal representation and proofFormal representation and proof

Algorithms, computation, Algorithms, computation, (un)decidability, (in)tractability(un)decidability, (in)tractability

ProbabilityProbability

PsychologyPsychologyAdaptationAdaptation

Phenomena of perception and Phenomena of perception and motor controlmotor control

EconomicsEconomicsFormal theory of rational Formal theory of rational decisionsdecisions

LinguisticsLinguisticsKnowledge representationKnowledge representation

GrammarGrammar

NeuroscienceNeurosciencePlastic physical substrate for Plastic physical substrate for mental activitymental activity

Control theoryControl theoryStability of systemsStability of systems

Simple optimal agent designsSimple optimal agent designs

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State of the artState of the art

Artificial Intelligence

Expert systemsNeural networks

Genetic algorithms…

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Expert systemsExpert systems

Def:Def: software systems simulating expert-like software systems simulating expert-like decision making while keeping knowledge decision making while keeping knowledge separate from the reasoning mechanism.separate from the reasoning mechanism.

Replace Replace human expert decision makinghuman expert decision making when when not availablenot available

Assist human expert when Assist human expert when integratingintegrating various various decisionsdecisions

Provides a user with:Provides a user with: an an appropriate hypothesisappropriate hypothesis;; methodologymethodology for knowledge storage and reuse. for knowledge storage and reuse.

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Expert systems architectureExpert systems architecture

Knowledge base

Inference engine

Update module

Explanation module

User interface

User

Lets the user change the

k.b.

Uses the k.b. to infer new facts and produce

solutions

Tells the user the steps that produced the

solution

Expert’s knowledge –

facts and rules

Simplifies the user’s

interaction

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Rule-Based SystemsRule-Based Systems

Knowledge in the form of Knowledge in the form of if condition if condition

then effectthen effect rules rules

Example:Example:here here fine fine

not here not here absent absent

absent and not seen absent and not seen at home at home

absent and seen absent and seen in the building in the building

in the building in the building fine fine

at home and not holiday at home and not holiday sick sick

here and holiday here and holiday sick sick

? here no? seen no? holiday nosick

? here yesfine

? here yes? holiday yessick?

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Expert systems vs. conventional programsExpert systems vs. conventional programsAspectAspect Expert systemsExpert systems Conventional programsConventional programs

ParadigmParadigm Heuristical rules, exploration of Heuristical rules, exploration of the space of statesthe space of states

Algorithms, explicit pre-Algorithms, explicit pre-defined stepsdefined steps

ApproachApproach DeclarativeDeclarative ProceduralProcedural

Data Data manipulatedmanipulated

Knowledge, often rulesKnowledge, often rules Vectors and matrixes of Vectors and matrixes of datadata

Control systemControl system Inferential engine separated from Inferential engine separated from the knowledge basethe knowledge base

Data and information Data and information integrated with programsintegrated with programs

User interfaceUser interface Highly interactive, usually Highly interactive, usually questions and answersquestions and answers

No standard typologyNo standard typology

Explanation Explanation capabilitycapability

It presents the steps that led to It presents the steps that led to the proposed solutionthe proposed solution

Not availableNot available

Learning Learning capabilitycapability

PresentPresent Not availableNot available

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ApplicationsApplications

InterpretationInterpretation

DiagnosisDiagnosis

MonitoringMonitoring

Planning and schedulingPlanning and scheduling

ForecastingForecasting

Project and configurationProject and configuration

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Def:Def: mathematical models that mathematical models that try to emulatetry to emulate the the human nervous system.human nervous system.

Final target of neural networks is to simulate the Final target of neural networks is to simulate the process of learning of the human brain, so that it process of learning of the human brain, so that it can interact with the external environment can interact with the external environment without human help, except for the creation.without human help, except for the creation.

The first models were developed by The first models were developed by W. W. McCullochMcCulloch and and W. PittsW. Pitts in in 19431943, with their , with their manifest: “A logical calculus of the ideas manifest: “A logical calculus of the ideas immanent in nervous activity”.immanent in nervous activity”.

Neural NetworksNeural Networks

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Brain and NeuronsBrain and Neurons

General Structure of a NeuronGeneral Structure of a Neuron

LearningLearning

nucleus

dendrites

synapses

axon

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Artificial neural networks are typically composed Artificial neural networks are typically composed of interconnected units which serve as a model of interconnected units which serve as a model for for neuronsneurons..

The synapse is modelled by a The synapse is modelled by a modifiable weightmodifiable weight associated with each particular associated with each particular connectionconnection..

Each unit converts the pattern of incoming Each unit converts the pattern of incoming activities that it receives into a single outgoing activities that it receives into a single outgoing activity that it sends to other units.activity that it sends to other units. First: First: biased weighted sumbiased weighted sum Second: Second: transfer functiontransfer function

The structure of neural networksThe structure of neural networks

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Example of a two levels networkExample of a two levels network

1

1

0

0.19

0.88

0

-0.13

0

x

x

x

0.4x

x

1

00

0

0.19 + 0.88 + 0 - 0.8 = 0.27

-0.13 + 0 - 0.82 = -0.95

0.4 + 0 - 0.73 = -0.33

How do they work?How do they work?

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After a neural network has been created it can After a neural network has been created it can be trained using one of the supervised learning be trained using one of the supervised learning algorithms (an example is algorithms (an example is back propagationback propagation), ), which uses the data to adjust the network's which uses the data to adjust the network's weights and thresholds so as to minimize the weights and thresholds so as to minimize the error in its predictions on the training set.error in its predictions on the training set.

LearningLearning

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If the network is properly trained, it can model If the network is properly trained, it can model the (unknown) function thatthe (unknown) function that relates relates the the input input variables to thevariables to the output output variables, and can variables, and can subsequently be used to subsequently be used to make predictions make predictions where thewhere the output is not known output is not known..

They are based on the concept that often (not They are based on the concept that often (not always), it is possible to teach to a mathematical always), it is possible to teach to a mathematical system some laws that system some laws that we did not know beforewe did not know before, , only by letting it analyze a lot ofonly by letting it analyze a lot of real cases real cases..

Why are they useful?Why are they useful?

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ApplicationsApplications

Common fields of application are when the Common fields of application are when the statistical analysis of all the problem’s variables statistical analysis of all the problem’s variables is is too difficulttoo difficult or or expensiveexpensive (at the calculation (at the calculation level), but overall islevel), but overall is not clear beforehand not clear beforehand what what kind of deterministic kind of deterministic relationships relationships there are there are between the different variables.between the different variables.

OCR (Optical character recognition)OCR (Optical character recognition) DiagnosisDiagnosis Control of industrial productions qualityControl of industrial productions quality Recognition of potentially dangerous molecules (using Recognition of potentially dangerous molecules (using

“electronic noses”)“electronic noses”) Engine managementEngine management Control of robotsControl of robots

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A sample application (1)A sample application (1)

Problem:Problem:We want to create a neural network that is able We want to create a neural network that is able

to determine if one binary number with 4 to determine if one binary number with 4 figures is even.figures is even.

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A sample application (2)A sample application (2)

We use a network with:We use a network with: 4 input4 input nodes; nodes; 2 hidden2 hidden nodes; nodes; 1 output1 output node (1 if the number is even, 0 if it node (1 if the number is even, 0 if it

is odd).is odd).

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A sample application (3)A sample application (3)

Training Data:Training Data:

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A sample application (4)A sample application (4)

DEMODEMO

To solve the problem we will use a program To solve the problem we will use a program that was produced at the Laboratory for that was produced at the Laboratory for Computational Intelligence at the Computational Intelligence at the University of British Columbia.University of British Columbia.

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GAs Definition – The ideaGAs Definition – The idea

Genetic algorithms are based on a biological metaphor: they view learning as a competition among a population of evolving candidate problem solutions. So a GA is a probabilistic optimization algorithm that makes use of a population of test solutions which artificially reproduce through operations analogous to gene transfer in sexual reproduction.

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HistoryHistory

Genetic AlgorithmsGenetic Algorithms (GAs) originated from the (GAs) originated from the studies conducted by studies conducted by John H. HollandJohn H. Holland and his and his colleagues at the University of Michigan. colleagues at the University of Michigan. Holland’s book “Adaptation in Natural and Holland’s book “Adaptation in Natural and Artificial Systems”, published in 1975, is Artificial Systems”, published in 1975, is generally acknowledged as the beginning of the generally acknowledged as the beginning of the research of genetic algorithms.research of genetic algorithms.

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Definitions(1) – ChromosomeDefinitions(1) – Chromosome

A chromosome, a collection of genes, represents a A chromosome, a collection of genes, represents a possible solution of the problem. possible solution of the problem.

ENCODING EXAMPLESENCODING EXAMPLES

Binary encodingBinary encoding – A chromosome is a collection of bits – A chromosome is a collection of bits

Tree encodingTree encoding - In the tree encoding every chromosome is a tree of some objects, such as functions or commands in a programming language ( i.e. LISP )

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Definitions(2) – Fitness FunctionDefinitions(2) – Fitness Function

Fitness FunctionFitness Function

A fitness function evaluates the ability of A fitness function evaluates the ability of a candidate solution to solve the given a candidate solution to solve the given problem. problem.

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101FATHER - 101100

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Definitions(3) – CrossoverDefinitions(3) – Crossover

Crossover Crossover operates on selected genes from parent operates on selected genes from parent chromosomes and creates new offspring.chromosomes and creates new offspring.

EXAMPLE – SINGLE POINT CROSSOVER WITH BINARY ENCODING

MOTHER - 010111

OFFSPRING

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Definitions(4) – MutationDefinitions(4) – Mutation

The chromosome is randomly mutated to prevent premature The chromosome is randomly mutated to prevent premature convergence upon a local maximum. convergence upon a local maximum.

It’s a further techique through wich a GA explores the It’s a further techique through wich a GA explores the solution space: mutation gives an extra-probability to every solution space: mutation gives an extra-probability to every possible solution of the problem out of the finite population possible solution of the problem out of the finite population of solutions generated by the GA. of solutions generated by the GA.

Mutation should not occur very often, because then GA will in Mutation should not occur very often, because then GA will in fact change to random search.fact change to random search.

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Outline of a basic GA: FlowchartOutline of a basic GA: Flowchart

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Parameters(1)Parameters(1)

Crossover probability ( Pc )Crossover probability ( Pc ):: how often crossover will be how often crossover will be performed. If there are too few chromosomes, GAs have few performed. If there are too few chromosomes, GAs have few possibilities to perform crossover and only a small part of search possibilities to perform crossover and only a small part of search space is explored. On the other hand, if there are too many space is explored. On the other hand, if there are too many chromosomes, GA slow down. Crossover probability is usually chromosomes, GA slow down. Crossover probability is usually beetween beetween 0.40.4 and and 0.70.7. .

Mutation probability ( Pm )Mutation probability ( Pm ):: how often parts of chromosome will how often parts of chromosome will be mutated. Mutation should not occur very often, because then be mutated. Mutation should not occur very often, because then GA will in fact change to random search. Tipical Pm is GA will in fact change to random search. Tipical Pm is 0.01-0.01-0.0010.001..

Population sizePopulation size:: how many chromosomes are in a population (in how many chromosomes are in a population (in one generation). Good population size is reported to be about one generation). Good population size is reported to be about 20-100.20-100.

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Parameters(2)Parameters(2)

Elitism NumberElitism Number: Elitism is the name of the method that : Elitism is the name of the method that first copies the best chromosome (or few best first copies the best chromosome (or few best chromosomes) to the new population. Elitism can rapidly chromosomes) to the new population. Elitism can rapidly increase the performance of GA, because it prevents a increase the performance of GA, because it prevents a loss of the best found solution. Elitism number specifies loss of the best found solution. Elitism number specifies how many chromosomes copy directly in the new how many chromosomes copy directly in the new population.population.

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GAs vs. GAs vs. Ad-hocAd-hoc approach approach

Genetic Approach is a rather brutal approach, requiring large amounts of processing power, but with the immense advantage of supplying solutions to things we don't know how to solve, or don't know how to solve quickly. In fact no knowledge of how to solve the problem is needed BUT you need to be able to encode the chromosome and design the fitness function. This means implementation relies on a problem-independent "engine”.

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What are GAs used for ? What are GAs used for ?

GAs are excellent for all tasks requiring optimization and are highly effective in any GAs are excellent for all tasks requiring optimization and are highly effective in any situation where many inputs (variables) interact to produce a large number of possible situation where many inputs (variables) interact to produce a large number of possible outputs (solutions). outputs (solutions).

Some example situations are:Some example situations are:

OptimizationOptimization such as data fitting, clustering, trend spotting, path finding, ordering. such as data fitting, clustering, trend spotting, path finding, ordering.

ManagementManagement: Distribution, scheduling, project management, courier routing, container : Distribution, scheduling, project management, courier routing, container packing, task assignment, time tables. packing, task assignment, time tables.

FinancialFinancial: Portfolio balancing, budgeting, forecasting, investment analysis and payment : Portfolio balancing, budgeting, forecasting, investment analysis and payment scheduling. scheduling.

Data MiningData Mining

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