an intelligent agent

8/12/2019 An Intelligent Agent

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an intelligent agent (IA ) is an autonomous entity which observes through sensors and acts upon anenvironment using actuators (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it isrational).[1] Intelligent agents may also learn or use knowledge to achieve their goals. They may be very simpleor very comple ! a re"le machine such as a thermostat is an intelligent agent#[$] as is a human being# as is acommunity o" human beings working together towards a goal.

A multi%agent system (&.A.'.) is a computeri ed system composed o" multiple interacting intelligentagents within an environment. &ulti%agent systems can be used to solve problems that are di""icult orimpossible "or an individual agent or a monolithic system to solve. Intelligence may include some methodic#"unctional# procedural or algorithmic search# "ind and processing approach. Although there is considerableoverlap# a multi%agent system is not always the same as an agent%based model (A &).

In machine learning# the problem o" unsupervised learning is that o" trying to "ind hidden structure in unlabeleddata. 'ince the e amples given to the learner are unlabeled# there is no error or reward signal to evaluate apotential solution. This distinguishes unsupervised learning "rom supervised learning and rein"orcementlearning.

*nsupervised learning is closely related to the problem o" density estimation in statistics.[1] +oweverunsupervised learning also encompasses many other techni,ues that seek to summari e and e plain key"eatures o" the data. &any methods employed in unsupervised learning are based on data mining methods

used to preprocess[citation needed] data.

Approaches to unsupervised learning include !

-lustering (e.g.# k%means# mi ture models# hierarchical clustering)#[$]

+idden &arkov models#

lind signal separation using "eature e traction techni,ues "or dimensionality reduction (e.g.# principalcomponent analysis# independent component analysis# non%negative matri "actori ation# singular valuedecomposition).[ ]

Among neural network models# the sel"%organi ing map ('/&) and adaptive resonance theory (A0T) arecommonly used unsupervised learning algorithms.

*nsupervised learning techni,ues are widely used to reduce the dimensionality o" high dimensionalgenomic data sets that may involve hundreds o" thousands o" variables. or e ample# weighted correlationnetwork analysis is o"ten used "or identi"ying clusters (re"erred to as modules)# modeling the relationshipbetween clusters# calculating "u y measures o" cluster (module) membership# identi"ying intramedullary hubs#and "or studying cluster preservation in other data sets.[citation needed]


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'upervised learning is the machine learning task o" in"erring a "unction "rom labeled training data.[1] Thetraining data consist o" a set o" training e amples. In supervised learning# each e ample is a pair consisting o"an input ob2ect (typically a vector) and a desired output value (also called the supervisory signal). A supervisedlearning algorithm analy es the training data and produces an in"erred "unction# which can be used "or mappingnew e amples. An optimal scenario will allow "or the algorithm to correctly determine the class labels "orunseen instances. This re,uires the learning algorithm to generali e "rom the training data to unseen situations

in a 3reasonable3 way (see inductive bias).

There are several ways in which the standard supervised learning problem can be generali ed!

'emi%supervised learning! In this setting# the desired output values are provided only "or a subset o" the trainingdata. The remaining data is unlabeled.

Active learning! Instead o" assuming that all o" the training e amples are given at the start# active learningalgorithms interactively collect new e amples# typically by making ,ueries to a human user. /"ten# the ,ueriesare based on unlabeled data# which is a scenario that combines semi%supervised learning with active learning.

'tructured prediction! 4hen the desired output value is a comple ob2ect# such as a parse tree or a labeledgraph# then standard methods must be e tended.

5earning to rank! 4hen the input is a set o" ob2ects and the desired output is a ranking o" those ob2ects# thenagain the standard methods must be e tended.

Applications [edit]

ioin"ormatics

-hemin"ormatics

6uantitative structure7activity relationship

8atabase marketing

+andwriting recognition

In"ormation retrieval5earning to rank

/b2ect recognition in computer vision

/ptical character recognition

'pam detection

9attern recognition

'peech recognition


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&ulti%agent systems (&A') are no longer a pure research ob2ect# they become more and more popularand success"ul in commercial applications. This area o" Arti"icial Intelligence# the 8istributed Arti"icialIntelligence (8AI)# where &A' belong to# is highly interdisciplinary and connects ideas "rom sociology# biologyand psychology with computer science.

A special topic o" &ultiagentsystems is the 'warm Intelligence! A huge number o" agents with very limitedabilities# the so called particles# can be seen as a problem solving system "or comple numerical and ,ualitativeoptimi ation problems.

'warm Intelligence is motivated by natural species (i.e. ant colonies). These systems try to shi"tintelligence "rom single particles to the whole group using a network o" interactions to produce emergentbehavior. In this area we concentrate onto di""erent aspects# i.e. coordinated generation o" structures andautomatically creation o" hierarchies.

Definition Intelligent behaviors "rom a large number (i.e.# a'warm) o" simple individuals

-ollectively doing something seemingly :intelligent;/r :use"ul;

4here no one o" the individual can claimIntelligence

'o the intelligence is not in the composition o"'imple intelligences

0ather# intelligence :emerges; as a conse,uences o"The interactions Is a property o" the system# not o" its components It is the system in its whole that does something

Intelligence

Actually# there are a number o" systems which seemsTo e hibit swarm intelligence

Animal colonies and speci"ically Insect colonies like ants# termites# and bees

Bacteria (e.g.# the 8ictostelyum)# which appear able toAct in a "inali ed wayThe Brain ! intelligence and mind arises "rom theInteraction o" simple neuronsThe Cell ! homeostasis and the capability o" adapting and0eproducing arise "orm protein interactions There"ore 'warm intelligence seems not to be an :accident; but0ather a property o" a variety o" systems

8e"initely# evolution has played an important role in this


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'warm 'ystems vs. &ultiagent'ystemsActually# swarms are ensembles o" simple agents# i.e.# multisampling< Agent systems-omponents are autonomous# i.e.# they act based on local8ecisions

-omponents are situated in an environment they interact with each other (via the mediation o" the

=nvironment 7 stigmergy) +owever# the basic philosophy is somewhat di""erent "rom

That o" multiagent systems the accent is more on the ensemble than on the rationality o" Agents Agents may be irrational or probabilistic There is much more emphasis on the role o" the environment

>ot simply a way to get in"ormation but a way to coordinate with each other

And the environmental processes counts 'o# given that most modern distributed systems can be Assimilated# modeled# as agents# swarm intelligence may have

'ome relevance to them

'warm vs. Individual Intelligence >ot only :stupid; animals e hibit swarm intelligence 'ometimes# even mammals or humans do it

o -astori does water walls on rivero u""alos :"lock; in the wild

4olves surround a prey +umans "orms global sel"%organi ed patterns when walking

This implies that sometimeso The power o" interactions overcomes the power o"o Individualso 4hatever the reason an individual act in a speci"ic wayo 4hat matter is its interactive behavior# i.e.# the way it act

And interact in the system There is also a role "or stochastic variables 'ince swarm intelligence in :stupid; animals is sub2ect to

9robabilistic choices by animals The capability "or :intelligent; animals to do rational

Actions di""erent "rom those o" the group may be perceivedAs a sort o" probabilistic behavior


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Fundamentals of Context-aware systems:

Context-aware models

Context-aware control

Context-aware algorithms

Context-aware networks

Context-aware computing

Context-awareness calculi

Context-awareness representation

Context-awareness-based systems

Logic in context-awareness

Context-awareness reasoning

Formal methods of context-awareness

Context-awareness-based optimization and swarm Intelligence

Knowledge-based awareness

Context-aware Systems:

Routing transport and reliability issues of context-aware systems

!echni"ues for data dissemination and replication in context-aware systems

#pplications and middleware support mobile social networking applications

$obility models and statistical analysis of mobility traces

Context and social awareness mechanisms and algorithms

Co-existence of opportunistic networks with infrastructure mobile wireless networks

%er&ice composition in autonomic and opportunistic networks

Cognition-dri&en information processing and decision making

'erformance modeling scaling laws and fundamental limits for autonomic and opportunistic communications

'articipatory and urban sensing in autonomic and opportunistic networks

!rust security and reputation

#utonomic and opportunistic communication testbeds and prototypes measurement data from real experiments

%ocio-economic models for autonomic and opportunistic communications

Context-aware Technologies:

Context-aware information retrie&al

Context-aware profiling clustering and collaborati&e filtering

$achine learning for context-aware information retrie&al and ontology learning

Context-aware e-learning(tutoring

)se of context-aware technologies in )I(*CI

Context-aware ad&ertising

Recommendations for mobile users

Context-awareness in portable de&ices

Context-aware ser&ices


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%ocial #gents and #&atars

+motion and 'ersonality

,irtual *umans

#utonomous #ctors

#wareness-based #nimation

%ocial and Con&ersational #gents

Inter-#gent Communication

%ocial eha&ior

Crowd %imulation

)nderstanding *uman #cti&ity

$emory and Long-term Interaction

Context-awareness .C#/ has attracted increasing attention in computing and communication communities since it allows

automatic adaptation of de&ices systems and applications to user0s context change1 !he context is the information related

to the situation of an entity such as the present status of people places things and de&ices in the en&ironment1 #n entity is

a person de&ice place or ob2ect rele&ant to the interaction between a user and an application such as location time

acti&ities and ser&ices1

Context awareness allows for customization or creation of the application to match the preferences of the indi&idual user

based on current context such as enterprise en&ironment or home network1

Currently context has been considered as part of a process in which users are in&ol&ed hence specifying and de&eloping

context models are needed to support context-aware applications to .a/ adapt interfaces .b/ tailor the set of application-

rele&ant data .c/ increase the precision of information retrie&al .d/ disco&er ser&ices .e/ make the user interactionimplicit or .f/ build smart en&ironments1 Context related to human factors is structured into three categories3 .a/

information on the user .b/ the user4s social en&ironment and .c/ the user4s tasks1 Likewise context related to physical

en&ironment is structured into three categories3 .a/ location .b/ infrastructure and .c/ physical conditions1

Arti"icial >eural >etworks

? -onsists o" interconnected processing elements called nodes or neurons that work togetherto produce an output "unction. The output o" a neural network replies on the connection o" theindividual neurons within the network to operate.

0elationship between Arti"icial >eural >etworks @ the +uman rain

>eural networks are conceptually modeled on the human brain metaphor.

The general structure o" a neural network tries to mimic what we know about the structure andoperation o" the human brain.

In computer science and related "ields# arti"icial neural networks (A>>s) are computationalmodels inspired by an animal s central nervous systems (in particular the brain) which are capable o"machine learning as well as pattern recognition. Arti"icial neural networks are generally presented assystems o" interconnected 3neurons3 which can compute values "rom inputs.


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or e ample# a neural network "or handwriting recognition is de"ined by a set o" input neurons which may be activated by the pi els o" an input image. The activations o" these neurons are thenpassed on# weighted and trans"ormed by a "unction determined by the network s designer# to otherneurons. This process is repeated until "inally# an output neuron is activated. This determines whichcharacter was read.

5ike other machine learning methods# systems that learn "rom data# neural networks have beenused to solve a wide variety o" tasks that are hard to solve using ordinary rule%based programming#including computer vision and speech recognition.

>atural language processing (>59) is a "ield o" computer science# arti"icialintelligence# and linguistics concerned with the interactions between computers and human (natural)languages. As such# >59 is related to the area o" human7computer interaction. &any challenges in>59 involve natural language understanding# that is# enabling computers to derive meaning "romhuman or natural language input# and others involve natural language generation.

>59 using machine learning

&odern >59 algorithms are based on machine learning# especially statistical machine learning. Theparadigm o" machine learning is di""erent "rom that o" most prior attempts at language processing.9rior implementations o" language%processing tasks typically involved the direct hand coding o" largesets o" rules. The machine%learning paradigm calls instead "or using general learning algorithms Bo"ten# although not always# grounded in statistical in"erence B to automatically learn such rulesthrough the analysis o" large corpora o" typical real%world e amples. A corpus (plural# 3corpora3) is aset o" documents (or sometimes# individual sentences) that have been hand%annotated with the

correct values to be learned.

&a2or tasks in >59

Automatic summari ation

-o re"erence resolution

8iscourse analysis

&achine translation

&orphological segmentation

>amed entity recognition (>=0)

>atural language generation

>atural language understanding

/ptical character recognition (/-0)

9art%o"%speech tagging

9arsing

'peech recognition


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-ontemporary Issues and AI

>'A -ollecting &illions o" aces rom 4eb Images

+ow to &ake 0obots 'eem 5ess -reepy

Team 8evelops a 'o"tware Able to Identi"y and Track an 'peci"ic Individual 4ithin a Croup

'ecurity and 9rivacyD >ow They -an Co +and in +and

Think ast# 0obot

+ow &IT and -altech s -oding reakthrough -ould Accelerate &obile >etwork 'peeds

Coogle Invests in 'atellites to 'pread Internet Access

A'-1E &arks 'eventh 4in "or C9*s

+ow = ay s 0esearch 5aboratories Are Tackling the Tricky Task o" ashion0ecommendations

'e +arassment App +elps 4omen &ap Abuse

Coogle Turns to &achine 5earning to uild a etter 8ata -enter

8igital Actors Co eyond the *ncanny Falley

NSA Collecting Millions of Faces From Web Images

The New York Times (06/01/14) James Risen; Laura Poitras

The U.S. National Security Agency (NSA) is intercepting massive numbers of facial images from communicationstapped from its global surveillance operations for use in facial-recognition programs according to top-secretdocuments ac!uired from former NSA contractor "d#ard $. Sno#den. The documents sho# the agency deemsfacial images and other physical identifiers to be %ust as valuable in trac&ing do#n intelligence targets as #rittenand oral communications.

How to Make Robots Seem Less CreepyThe Wall treet Journal (06/01/14) !"am Wa#t$; %i&hael Norton

'ecent research has sho#n the uncanny valley hypothesis for human-robot interaction is overstated and #henemotional %obs must be botsourced people actually prefer robots that seem capable of conveying some degreeof human emotion. The latest human-robot interaction research combines brea&throughs in robotics andpsychology to suggest five important design features. The first idea is giving robots faces help improve human-robot interaction. or e*ample the +assachusetts ,nstitute of Technology s Ne*i robot has more of a baby faceand appears more capable of feeling than robots #ith longer chins #hich appear more professorial.

Team e!elops a Software Able to I"entify an" Track an Specific In"i!i"#al Wit$in a %ro#p'anish National Resear&h oun&il ( ) (06/01/14)

'esearchers from the Spanish National 'esearch ouncil ( S, ) say they have ta&en a ne# approach tomonitoring animals that move in groups #ith hopes of learning their rules of interaction. A team from the a%al,nstitute developed algorithms that enable the identification of each animal in a group and then developedsoft#are called the ,D Trac&er. The soft#are identification system first performs a search of the species #hen theyare separated and can be differentiated then identifies and recogni/es its image in every frame of the video.

%oogle T#rns to Mac$ine Learning to il" a &etter ata Center*+Net (0,/-./14) Ni&k eath

0oogle is loo&ing to neural net#or&s to improve the efficiency of its data centers. Neural net#or&s are machine-
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learning algorithms that imitate the functioning of the human brain specifically the interactions bet#een neurons.0oogle mechanical engineer and data analyst $im 0ao says a typical large-scale data center generates millions ofdata points across thousands of sensors daily but this data is primarily used for monitoring purposes only.1o#ever 0ao says advances in processing po#er and monitoring capabilities open a large opportunity for machinelearning to improve efficiency.

an intelligent agent

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