Title:Smart systems opportunities in an IoT ageSpeaker: Ann Nowé

AI lab, VUB

YOURLOGO

YOURLOGO

Smart phonesSmart TV’s

Smart washing machines

Smart boards

Smart cars

Smart watch

Ann Nowé

YOURLOGO

Smart phonesSmart TV’s

Smart washing machines

Smart boards

Smart cars

Smart watch

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Ann Nowé

YOURLOGOJohn McCarthy Marvin Minsky Nathaniel Rochester Claude Shannon

“ We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning

or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.”

IJCAI 1956

Ann Nowé

YOURLOGOJohn McCarthy Marvin Minsky Nathaniel Rochester Claude Shannon

IJCAI 1956

Ann Nowé

“ We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.”

2 month, 10 man

YOURLOGOJohn McCarthy Marvin Minsky Nathaniel Rochester Claude Shannon

IJCAI 1956

Ann Nowé

“ We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.”

learning

YOURLOGOAnn Nowé

• Knowledgerepresentationlanguagesshouldhaveprecisesyntaxandsemantics.

• Youmustknowexactlywhatanexpressionmeansintermsofobjectsintherealworld.

Representationof facts in World

Real World

New conclusions

Real World

Computer Computer

Map to KR language Map back to real world

Inference

ClassicalAI: Knowledgerepresentationandstatespacesearch

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Machine Learning

Ann Nowé

Symbolic AIReasoning & Dedicated languages

Statistical Machine LearningDatamining

Sub-symbolic AI

Shift in AI

Ann Nowé

Symbolic AIReasoning & Dedicated languages

Bigdata

IoT :heterogeneous data

Ann Nowé

StatisticsversusDatamining

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Fromhomogenousdatatoheterogeneousdata

ReinforcementLearning

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Whatisit?

Learningfrominteraction

Learningabout,from,andwhileinteractingwithanexternalenvironment

Learningwhattodo—howtomapsituationstoactions—soastomaximizeanumericalrewardsignal

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ReinforcementLearning

Trialanderrorbutnotrandom

Performanceisgraduallyimproving

Domainknowledgecanbeincorporatedtospeeduplearning

Theoreticalguaranteesonconvergence

YOURLOGOAnn Nowé

ReinforcementLearning:interestingapproachwhenproblemishardtomodelitalsoallowsforeasyinteractionwiththesolution

Wie is Probo? Probo is een imaginair dier met een eigen identiteit. Hij heeft als bijzonder kenmerk een opvallende slurf. De naam Probo is afgeleid van het woord proboscidea. Proboscidea (slurfdieren of olifantachtigen) is een orde van zoogdieren waartoe de mastodonten, mammoeten en olifanten behoren. De meeste vertegenwoordigers zijn echter uitgestorven tijdens de laatste ijstijd. In de naam Probo zit ook het woord ROBO dat de gerobotiseerde aard van Probo benadrukt.

De mechanica is omhuld met een pels jasje om de robot zacht en knuffelbaar te maken. De kleur van de robot is groen, een kleur die positieve emoties oproept zoals ontspanning, natuur en comfort. De vorm werd bepaald op basis van testen met kinderen.

Er werd bewust gekozen om Probo het uiterlijk te geven van een imaginair diertje. Hierdoor worden er geen specifieke verwachtingen gecreëerd bij de kinderen die er wel zouden zijn indien er voor een kat of hond was gekozen. Dit geeft de mogelijkheid om speciale elementen zoals bvb een aanraakscherm in de buik te plaatsen en een eigen taal voor Probo te ontwikkelen.

http://mech.vub.ac.be/multibody_mechanics.htm

https://ai.vub.ac.be/research/projects/lecopro

Reinforcement learning asaplanning systemforairtraffic control

YOURLOGOAnn Nowé

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PerpetualPersonalizedProductsEmergingfromTailoredUserAdaptingLogic

SBOprojectjointlywithKULeuven (CIB)andFlandersMake(FMTC)

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HouseholdthermostatsSettostandardprofileSetonceandneverchanged

Savingenergywithoutlossofcomfort?Adaptheatingbasedonpresenceandactivities

Profilesareadaptedonlinetakingintoaccounttheuserpreferences

YOURLOGOAnn Nowé

Celsius

Time

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SCANERGY

Substation S

EP/DSO

NRGcoinProtocol

Prosumer P

x

Consumer Ch(y,tp,tc)

y

f(x)

xg(x,tp,tc)

CurrencyExchange

Market

m

€

€

n

SCANERGY:aSCAlable andmodularsystemforeNERGY tradingbetweenprosumers

COREConcepts:NRG-X-ChangeNRGcoin

EUIAPPincollaboration withSensing&ControlBarcelona

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Repast MAS simulator with real life data

Demand response

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http://www.scanergy-project.eu/ BestdemoawardatAAMASconference 2015

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IoT bringsmoreheterogeneous multi-agent systemswithnewchallenges andopportunitiesDevices canlearn fromeachotherbyfindingcorrelations inusagepatterns

YOURLOGOAnn Nowé

IoT bringsmoreheterogeneous multi-agent systemswithnewchallenges andopportunitiesDevices canlearn fromeachotherbyfindingcorrelations inusagepatternsSmart

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YOURLOGOAnn Nowé

ai.vub.ac.be