VIRTUAL%20PRESENCE

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VIRTUAL PRESENCE
Authors
Voislav Galic, vgalic_at_bitsyu.net
Dušan Zecevic, zdusan_at_softhome.net
Ðorde Ðurdevic, madcat_at_tesla.rcub.bg.ac.yu
Veljko Milutinovic, vm_at_etf.bg.ac.yu
http//galeb.etf.bg.ac.yu/vm/tutorial
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SUMMARY
- Introduction to Virtual Presence - Data Mining
for Virtual Presence - A New Software
Paradigm - Selected Case Studies
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INTRODUCTION TO VP

• - Definitions
• VP applications
• Psychological aspects

4
DATA MINING FOR VP

• Definitions
• What can Data Mining do?
• Growing popularity of Data Mining
• - Algorithms

5
SOFTWARE AGENTS

• A new software paradigm
• Standardization
• FIPA specifications
• Agent management
• Agent Communication Language

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CASE 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
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CONCLUSION

• 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

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INTRODUCTION

• Virtual presence will arguably be one of the
  most important aspects of personal communication
  in the twenty-first century

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Definition
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
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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

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VP 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

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Psychological Aspects

• Cyberspace and Mind
• Presence in Virtual Space

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DATA MINING

• Knowledge discovery is a non-trivial process of
  identifying valid, novel, potentially useful, and
  ultimately understandable patterns in data

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Many 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

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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
  recommendations
• etc.

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The 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

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Reasons for the Growing Popularity of Data Mining

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

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Tasks 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 algorithms
• Clustering (Classification)
• Association Rules
• Sequential Analysis

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Classification Algorithms

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

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Association 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

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Sequential 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

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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

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THE NEW SOFTWARE PARADIGM

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

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Many 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
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What Problems do Agents 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

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The 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 themselves
• This 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

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FIPA 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.

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Agent 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

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Components of the Model

• Agent

- computational process - fundamental actor on an
AP - as a physical software process has a life
cycle that has to be managed by the AP

• Directory Facilitator

• - yellow pages to other agents
• supported function are
• register
• deregister
• modify
• search

• Agent Management System

• - 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

• Message Transport Service

- communication method between agents

• Agent Platform

- physical infrastructure in which agents can be
deployed

• Software

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

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

• Requirements

• 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-t
o language ontology reply-by protocol

• Communicative acts

confirm disconfirm inform not-understood query-if
query-ref refuse etc.
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Communication 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 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, 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)
- 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 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
• )

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GoodNews

• A system that automatically categorizesnews
  reports that reflect positively or negativelyon
  a companys financial outlook

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Introduction

• Correlation between news reports on a companys
  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

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Categorization

• The algorithm categorizes each given news article
  into the predefined categories
• GOOD strong and explicit evidences of the
  companys 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
  couldfall short of expectations
• BAD explicitly bad evidences
• shares of ABC fell 0.57 to 44.65 in early NY
  trading

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Co-located Phrase

• The proposed algorithm labels the unlabeled
  news articlesthrough voting process among
  experts that are FCPs
• 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
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Conclusion

• 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

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iMatch

• The vision of each MIT student
• having a personal software agent,
• which helps to manage its owner's academic life

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Introduction

• 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

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Ceteris 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

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CPP 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

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Conclusion

• 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

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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

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Introduction

• 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

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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

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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 restaurants 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

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Conclusion

• 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

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THE END

• Quatenus nobis denegatum diu vivere, relinquamus
  aliquid, quo nos vixisse testemur

References http//www.marconi.com http//www.bl
ueyed.com http//www.fipa.org http//www.rpi.edu
http//research.microsoft.com http//imatch.lcs
.mit.edu
Authors Voislav Galic, vgalic_at_bitsyu.net Dušan
Zecevic, zdusan_at_softhome.net Ðorde Ðurdevic,
madcat_at_tesla.rcub.bg.ac.yu Veljko Milutinovic,
vm_at_etf.bg.ac.yu http//galeb.etf.bg.ac.yu/vm/t
utorial