1/48 VIRTUAL PRESENCE Authors: Voislav Galić, [email protected] Dušan Zečević, [email protected] Đorđe Đurđević, [email protected] Veljko Milutinović,

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VIRTUAL PRESENCEVIRTUAL PRESENCE

Authors: Voislav Galić, [email protected]šan Zečević, [email protected]Đorđe Đurđević, [email protected] Milutinović, [email protected]

http://galeb.etf.bg.ac.yu/~vm/tutorial

Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović 2/48

SUMMARYSUMMARY

- Introduction to Virtual Presence

- Data Mining for Virtual Presence

- A New Software Paradigm

- Selected Case Studies


INTRODUCTION TO VPINTRODUCTION TO VP

- Definitions

- VP applications

- Psychological aspects


DATA MINING FOR VPDATA MINING FOR VP

- Definitions

- What can Data Mining do?

- Growing popularity of Data Mining

- Algorithms


SOFTWARE AGENTSSOFTWARE AGENTS

- A new software paradigm

- Standardization

-FIPA specifications

- Agent management- Agent Communication Language


CASE STUDIESCASE STUDIES

• GoodNews (CMU*)– Categorization of financial news articles

• iMatch (MIT**)– help students find resources they need– advanced, agent-based system architecture

• “Tourist city” in the future (ETF***)– represents a qualitative step forward in the domain of

maximization of customer satisfaction– technologies:

• Data Mining• Software Agents (mobile)

* Carnegie Mellon University, Pittsburgh, USA** Massachusetts Institute of Technology, USA*** Faculty of Electrical Energinering, University of Belgrade, Serbia and Montenegro


CONCLUSIONCONCLUSION

This tutorial will attempt to familiarize you with:

- The concept of VP (Virtual Presence) as a new technological challenge

- The new paradigms and technologies that will bring the VP to everyday life:

- Data Mining- Software Agents

INTRODUCTIONINTRODUCTION

Virtual presence will arguably be one of the most important aspects of personal

communication in the twenty-first century


DefinitionDefinition

Virtual presence is a term with various shades of meanings in different industries,

but its essence remains constant; it is a new tool that enables some form of telecommunication in which the individual may substitute their physical presence

with an alternate, typically, electronic presence


How to Accomplish it?How to Accomplish it?

• The presence is accomplished through the Internet, video, or other communications, perhaps even psychically one day

• Technological advance will sophisticate virtual presence, altering the very meaning of the word “presence”

• The ability to conduct everyday tasks by being virtually or electronically present


VP ApplicationsVP Applications

• in government– “Sunshine laws”– Voting

• in business– Online board meetings– Shareholder voting online

• in education– interactive lectures and courses

• in medicine– Telemedicine (Diagnostics, Remote surgery)

– Risks (Privacy)

• in everyday life– Telecommuting/Telework– Software agents as our virtual “shadows”


Psychological AspectsPsychological Aspects

• Cyberspace and Mind

• Presence in Virtual Space

DATA MININGDATA MINING

Knowledge discovery is a non-trivial process of identifying valid, novel, potentially useful, and ultimately

understandable patterns in data


Many DefinitionsMany Definitions

• Data mining is also called data or knowledge discovery• It is a process of inferring knowledge

from large oceans of data• Search for valuable information in large volumes of data• Analyzing data from different perspectives

and summarizing it into useful information


What Can Data Mining Do?What Can Data Mining Do?

• DM allows you to extract knowledge from historical data and predict outcomes of future situations

• Optimize business decisions and improve customers’ satisfaction with your services

• Analyze data from many different angles, categorize it, and summarize the relationships identified

• Reveal knowledge hidden in data and turn this knowledge into a crucial competitive advantage

• Predict cross-sell opportunities and make recommendationsetc.


The Power of Data MiningThe Power of Data Mining

• Having a database is one thing, making sense of it is quite another

• It does not rely on narrow human queries to produce results, but instead uses AI related technology and algorithms

• Data mining produces usually more general (=more powerful) results than those obtained by traditional techniques

• Using more than one type of algorithm to search for patterns in data


Reasons for the Growing Reasons for the Growing Popularity of Data MiningPopularity of Data Mining

• Growing Data Volume• Low Cost of Machine Learning• Limitations of Human Analysis

…


Tasks Solved by Data MiningTasks Solved by Data Mining

• Predicting• Classification• Detection of relations• Explicit modeling• Clustering• Market basket analysis• Deviation detection

Data mining includes three major components, with corresponding algorithmsalgorithms:

–Clustering (Classification)–Association Rules–Sequential Analysis


Classification AlgorithmsClassification Algorithms

• Statistical algorithms• Neural networks algorithms• Genetic algorithms• Nearest neighbor method• Rule induction• Data visualization• Decision tree building algorithms• Parallel algorithms


Association Rule AlgorithmsAssociation Rule Algorithms

• Association rule implies certain association relationship among the set of objects in a database

• These objects “occur together”, or “one implies the other”• Formally: X Y, where X and Y are sets of items (itemsets)• Key terms

– Confidence– Support

• The goal – to find all association rules that satisfy user-specified minimum support and minimum confidence constraints

• Apriori algorithm and its variations• Distributed / Parallel algorithms


Sequential AnalysisSequential Analysis

• Sequential Patterns• The problem – finding all sequential patterns

with user-specified minimum support• Elements of a sequential pattern need not to be:

– consecutive– simple items

• Algorithms for finding sequential patterns– “count-all” algorithms– “count-some” algorithms


Conclusion Conclusion

• Various applications (market, banking, sports)• Drawbacks of existing algorithms

– Data size– Data noise– Query complexity

• The infrastructure has to be significantly enhanced to support larger applications

• Solutions– Adding extensive indexing capabilities– Using new HW architectures

to achieve improvements in query time

THE NEW SOFTWARE THE NEW SOFTWARE PARADIGMPARADIGM

All software agents are programs, but not all programs are agents


Many DefinitionsMany Definitions

• Computational systems that inhabit some dynamic environment, sense and act autonomously and realize a set of goals or tasks for which they are designed

• Hardware or (more usually) software-based computer system that enjoys the following properties:- Reactive (sensing and acting)- Autonomous- Goal-oriented (pro-active purposeful)- Temporally continuous- Communicative (socially able)

- Learning (adaptive)- Mobile- Flexible- Character


What Problems do Agents What Problems do Agents Solve ?Solve ?

• Client/server network bandwidth problem• In the design of a client/server architecture• The problems created by intermittent

or unreliable network connections• Attempts to get computers to do real thinking for us


The New Software ParadigmThe New Software Paradigm

• Unless special care has been taken in the design of the code, two software programs cannot interoperate

• The promise of agent technology is to move the burden of interoperability from software programmers to programs themselvesThis can happen if two conditions are met: – A common language (Agent Communication Language – ACL)– An appropriate architecture

• They draw on and integrate many diverse disciplines of computer science and other areas


FIPA SpecificationsFIPA Specifications

• The Foundation for Intelligent Physical Agents (FIPA), established in 1996 in Geneva

• FIPA specifications:– Agent Management – Agent Communication Language – Agent/Software Integration– Agent Management Support for Mobility– Human-Agent Interaction – Agent Security Management– Agent Naming – FIPA Architecture – Agent Message Transport

etc.


Agent ManagementAgent Management

• Provides the normative framework within which FIPA agents exist and operate

• Establishes the logical reference model for the creation, registration, location, communication, migration and retirement of agents

- The entities contained in the reference model are logical capability sets and do not imply any physical configuration

- Additionally, the implementation details of individual APs and agents are the design choices of the individual agent system developers


Components of the ModelComponents of the Model

•Agent

•Directory Facilitator

•Agent Management System

•Message Transport Service

•Agent Platform

•Software

- computational process- fundamental actor on an AP- as a physical software process has a life cycle that has to be managed by the AP- yellow pages to other agents- supported function are:

-register-deregister-modify-search

- white pages services to other agents- maintains a directory of AIDs which contain transport addresses- supported function are:

-register-deregister-modify-search-get-description-operations for underlying AP

- communication method between agents

- physical infrastructure in which agents can be deployed

- all non-agent, executable collections of instructions accessible through an agent


Agent Life CycleAgent Life Cycle

• FIPA agents exist physically on an AP and utilize the facilities offered by the AP for realising their functionalities

• In this context, an agent, as a physical software process, has a physical life cycle that has to be managed by the AP

The state transitions of agents can be described as:

- create- invoke- destroy- quit- suspend

- resume- wait- wake up- move*- execute*


Agent Communication Agent Communication LanguageLanguage

– Implementing a subset of the pre-defined message types and protocols– Sending and receiving the not-understood message– Correct implementation of communicative acts defined in the specification– Freedom to use communicative acts with other names, not defined in the specification– Obligation of correctly generating messages in the transport form– Language must be able to express propositions, objects and actions– The use of Agent Management Content Language and ontology

• Pre-defined message parameters:

:sender

:receiver

:content

:reply-with

:in-reply-to

:language

:ontology

:reply-by

:protocol

• Communicative acts:

confirmdisconfirminformnot-understoodquery-ifquery-refrefuseetc.

• Requirements:

• The specification consists of a set of message types and the description of their meanings


Communication ExamplesCommunication Examples

- Agent i asks agent j for its available services:(query-ref :sender i :receiver j :content (iota ?x (available-services j ?x)) …)

- Agent j replies that it can reserve trains, planes and automobiles:(inform :sender j :receiver i :content (= (iota ?x (available-services j ?x)) ((reserve-ticket train) (reserve-ticket plane) (reserve automobile)) ) …)

- Agent j refuses to i reserve a ticket for i, since i there are insufficient funds in i's account:(refuse :sender j :receiver i :content ( (action j (reserve-ticket LHR, MUC, 27-sept-97)) (insufficient-funds ac12345) ) :language sl)

- Agent i did not understand an query-if message because it did not recognize the ontology:(not-understood :sender i :receiver j :content ((query-if :sender j :receiver i …) (unknown (ontology www))) :language sl)

- Agent i confirms to agent j that it is, in fact, true that it is snowing today:(confirm :sender i :receiver j :content "weather( today, snowing )" :language Prolog)

- Agent i, believing that agent j thinks that a shark is a mammal, attempts to change j's belief:(disconfirm :sender i :receiver j :content (mammal shark))

- Agent i asks agent j if j is registered with domain server d1:(query-if :sender i :receiver j :content (registered (server d1) (agent j)) :reply-with r09)...(inform :sender j :receiver i :content (not (registered (server d1) (agent j))) :in-reply-to r09)

- Auction bid(inform :sender agent_X :receiver auction_server_Y :content (price (bid good02) 150) :in-reply-to round-4 :reply-with bid04 :language sl :ontology auction)

GoodNewsGoodNews

A system that automatically categorizesnews reports that reflect positively or negatively

on a company’s financial outlook


IntroductionIntroduction

• Correlation between news reports on a company’s financial outlook and its attractiveness as an investment

• Text categorization – very difficult domainfor the use of machine learning– Very large number of input features– High level of noise (metaphors, irony,…)– Large percent of irrelevant features

• A new text classification algorithm – “Domain Experts”• Two types of data

– (Human-)labeled– Unlabeled

• The algorithm classifies financial news into the predefined five categories

• FCP (Frequently Co-located Phrase) the building elementfor the categorization algorithm


CategorizationCategorization

• The algorithm categorizes each given news article into the predefined categories

– GOOD – strong and explicit evidences of the company’s financial status

• …shares of ABC company rose 2 percent…– GOOD, UNCERTAIN – predictions and forecasts of future

profitability• … ABC company predicts fourth-quarter earnings will be high…

– NEUTRAL – nothing is mentioned about the financial well-being of the company

• … ABC announced plans to focus on products based on recycledmaterials…

– BAD, UNCERTAIN – predictions of future loses• … ABC announced today that fourth-quarter results could

fall short of expectations…– BAD – explicitly bad evidences

• … shares of ABC fell $0.57 to $44.65 in early NY trading…


Co-located PhraseCo-located Phrase

• The proposed algorithm labels the “unlabeled” news articlesthrough voting process among experts that are FCP’s

• Definition – a co-located phrase is a sequence of nearby, but not necessarily consecutive words– …shares of ABC rose 8.5%… (shares, rose): GOOD– …ABC presented its new product… (present, product): NEUTRAL

class selected FCP

+ “share & gains | rose”, “profit | revenue & rose”

+/? “except | forecasts & earnings”

+/- “alliance & company”, “deal | present & product”

-/? “short & expectation”

- “share & down | lost”, “profit | sales & decrease”


ConclusionConclusion

• Problems with construction of the training (i.e. labeled)data set – “inter-indexer inconsistency”

• Problems with small sets of labeled (training) data– Very expensive labeled data,

while unlabeled data are cheaply available

• The accuracy is around 75% (total of 2000 news articles);• Comparison of a few different methods (picture)

Naive-Bayes v Domain Experts

iMatchiMatch

The vision of each MIT studenthaving a personal software agent,

which helps to manage its owner's academic life



• The aim - bring together MIT students and staff who may usefully collaborate with each other– completing final projects– studying for exams– tutoring one another

• Facilitate students and faculty matching for:– Research– Teaching– Internship


Ceteris Paribus PreferenceCeteris Paribus Preference

• Ceteris paribus relations express a preference over sets of possible outcomes

• All possible outcomes are considered to be describable by some (large) set of binary features (true or false)– The specified features are instantiated to either true or false– Other features are ignored

I prefer ice cream

I prefer chocolate

I prefer train

I prefer airplane

I prefer cell phone

I prefer e-mail


CPP Agent ConfigurationCPP Agent Configuration

• Specify a domain for preference– Agent methods of communication and notification– Different security settings of different servers

• Preference statements themselves– How to get users to easily adjust C.P. rules (graphical interface)– Pose hypothetical preference questions to user to help complete

the preferences of an ambivalent user

• People will only put down their true profile, if they know that the system is secure



• Benefit MIT students by matching them to appropriate resources

• Static interest matching– Group together similar users for specific context– This enables viewing a human user as a resource

for dynamic resource discovery (locate experts, enthusiasts,...)

• Dinamic interest matching– Location and/or temporal specific resource matching

As students and their agents move from one physical location to another, iMatch services for matching the closest resources can be offered

• Help students manage their lives

The near future…The near future…

The focus of the research is on e-tourism after the year 2005, but the applications

of the proposed infrastructure are multifold



• The assumptions:– after the year 2005, each tourist in Europe will be equiped with a

cell phone of the power same or better than the Pentium IV– whenever a tourism-based service or product is purchased, a

mobile agent is assigned to that cell phone PC, to monitor the behaviour of the customer

– all tourist cell phone PCs create an AD-HOC networkaround the points of touristic attractions, and link to a data mine that collects all information of interest


How to accomplish it?How to accomplish it?

• The information of interest is not collected by asking the customer to fill out the forms, but by monitoring the behaviour of the customer

• The collected information, sorted in the data mine, is made available to other tourists, as an on-line owner-independent source of information about the given services and/or products


What can it do…What can it do…

• If a tourist would like to know, at that very moment, what restaurant has good food/atmosphere and happy customers, he/she can access the data mine (via the Internet) and can obtain the information that is linked to that very moment, and is not created by the owner of the business, but by the customers

• Accessing the given restaurant’s website has two drawbacks:– the information is not fresh - periodically updated– the information is made by the owner of the restaurant,

and therefore not completely objective



• Consequently, the proposed approach works much better, and represents a qualitative step forward in the domain of maximization of customer satisfaction

• This may mean that the privacy of the customers is jeopardized,however, if the monitored behaviour is non-personalized, and if the customer obtains a discount based on the fact that mobile agents are welcome, the privacy stops to be an issue, and people will sign up voluntarily

THE ENDTHE ENDQuatenus nobis denegatum diu vivere, relinquamus aliquid, quo nos vixisse testemur

Authors:Voislav Galić, [email protected]šan Zečević, [email protected]Đorđe Đurđević, [email protected] Milutinović, [email protected]


References:http://www.marconi.comhttp://www.blueyed.comhttp://www.fipa.orghttp://www.rpi.eduhttp://research.microsoft.comhttp://imatch.lcs.mit.edu………

mailto:[email protected]






http://www.marconi.com/

http://www.marconi.com/

http://www.blueyed.com/

http://www.blueyed.com/

http://www.fipa.org/

http://www.fipa.org/

http://www.rpi.edu/

http://www.rpi.edu/

http://research.microsoft.com/

http://research.microsoft.com/

http://imatch.lcs.mit.edu/

Documents

1/48 VIRTUAL PRESENCE Authors: Voislav Galić, [email protected] Dušan Zečević, [email protected] Đorđe Đurđević, [email protected] Veljko Milutinović,