VIRTUAL PRESENCE

1. VIRTUAL PRESENCE Authors: Voislav Galić, vgalic@bitsyu.net Dušan Zečević, zdusan@softhome.net Đorđe Đurđević,madcat@tesla.rcub.bg.ac.yu Veljko Milutinović, vm@etf.bg.ac.yu http://galeb.etf.bg.ac.yu/~vm/tutorial

2. 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 Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

3. SUMMARY - Introduction to Virtual Presence - Data Mining for Virtual Presence - A New Software Paradigm - Selected Case Studies Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

4. INTRODUCTION TO VP • - Definitions • VP applications • Psychological aspects Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

5. DATA MINING FOR VP • Why Data Mining? • What can Data Mining do? • Growing popularity of Data Mining • - Algorithms Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

6. SOFTWARE AGENTS • A new software paradigm • Standardization • FIPA specifications • Agent management • Agent Communication Language Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

7. GoodNews (CMU*) • Categorization of financial news articles • Co-located phrases • Domain Experts • Implementation and results * Carnegie Mellon University, Pittsburgh, USA Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

8. iMatch (MIT*) • The idea • - associate MIT students and staff in order to ease their cooperation; • - help students find resources they need • Implementation • advanced, agent-based system architecture • - Tomorrow? * Massachusetts Institute of Technology, USA Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

9. “Tourist city” (ETF*) • A qualitative step forward in the domain of maximization of customer satisfaction • Technologies: • Data Mining • Software Agents (mobile) * Faculty of Electrical Engineering, University of Belgrade, Serbia and Montenegro Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

10. 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 Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

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

12. Essence of VP • The usefulness and reliability of virtual presence • The ability to conduct everyday tasks by being virtually or electronically present Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

13. 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” Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

14. VP Applications • VP in government • “Sunshine laws” • Voting Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

15. VP Applications • VP in business • Online board meetings • Shareholder voting online Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

16. VP Applications • VP in education • interactive lectures and courses Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

17. VP Applications • VP in medicine • Telemedicine • Diagnostics • Remote surgery • Risks • Privacy Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

18. VP Applications • VP in everyday life • Telecommuting/Telework • Software agents as our virtual “shadows” Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

19. Psychological Aspects • Cyberspace and Mind • Presence in Virtual Space • Communal Mind and Virtual Community Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

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

21. 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 Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

22. Why Data Mining ? • 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 Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

23. What Can Data Mining Do? • Identify your best prospects and then retain them as customers • Predict cross-sell opportunities and make recommendations • Learn parameters influencing trends in sales and margins • Segment markets and personalize communications etc. Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

24. 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 • Inductive reasoning • Using more than one type of algorithm to search for patterns in data • Data mining produces usually more general (=more powerful) results than those obtained by traditional techniques • Relational DB storage and management technology is OK for data mining applications less than 50 gigabytes Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

25. Reasons for the Growing Popularity of Data Mining • Growing Data Volume • Low Cost of Machine Learning • Limitations of Human Analysis … Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

26. Tasks Solved by Data Mining • Predicting • Classification • Detection of relations • Explicit modeling • Clustering • Market basket analysis • Deviation detection Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

27. Algorithms • Generally, their complexity is around n (log n)(n is the number of records) • Data mining includes three major components, with corresponding algorithms: • Clustering (Classification) • Association Rules • Sequential Analysis Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

28. Classification Algorithms • The aim is to develop a description or model for each class in a database, based on the features present in a set of class-labeled “training data” • Data Classification Methods: • Statistical algorithms • Neural networks • Genetic algorithms • Nearest neighbor method • Rule induction • Data visualization Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

29. Classification-rule Learning • Data abstraction • Classification-rule learning – finding rules or decision trees that partition given data into predefined classes • Hunt’s method • Decision tree building algorithms: • ID3 / C4.5 algorithm • SLIQ / SPRINT algorithm (IBM) • Other algorithms Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

30. Parallel Algorithms • Basic Idea: N training data items are randomly distributed to P processors. All the processors cooperate to expand the root node of the decision tree • There are two approaches for future progress (the remaining nodes): • Synchronous approach • Partitioned approach Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

31. 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. Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

32. Association Rule Algorithms • Apriori algorithm and its variations • AprioriTid • AprioriHybrid • FT (Fault-tolerant) Apriori • Distributed / Parallel algorithms (FDM, …) Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

33. 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 Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

34. Conclusion • Drawbacks of existing algorithms • Data size • Data noise • There are two critical technological drivers: • Size of the database • 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 Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

35. THE NEW SOFTWARE PARADIGM All software agents are programs, but not all programs are agents

36. 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: Many Definitions - Reactive (sensing and acting) - Autonomous - Goal-oriented (pro-active purposeful) - Temporally continuous - Communicative (socially able) - Learning (adaptive) - Mobile - Flexible - Character Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

37. Interesting Topic of Study • They draw on and integrate many diverse disciplines of computer science and other areas: • objects and distributed object architectures • adaptive learning systems • artificial intelligence and expert systems • collaborative online social environments • security • knowledge based systems, databases • communications networks • cognitive science and psychology … Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

38. 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 Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

39. 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 Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

40. The Need for Standards • Anywhere, anytime consumer access to the Universal bouquet of information and services is the new goal of the information revolution • The scope of Internet standards makes the scope of choices extreme • The Foundation for Intelligent Physical Agents (FIPA), established in 1996 in Geneva • international non-profit association of companies and organizations • specifications of generic agent technologies. Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

41. 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. Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

42. 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 Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

43. 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 Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

44. 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* Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

45. 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 Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

46. ACL Syntax Elements • Pre-defined message parameters: • Communicative acts: • :sender • :receiver • :content • :reply-with • :in-reply-to • :envelope • :language • :ontology • :reply-by • :protocol • :conversation-id accept-proposal agree cancel cfp confirm disconfirm failure inform inform-if inform-ref not-understood propose query-if query-ref refuse reject-proposal request request-when request-whenever subscribe Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

47. Communication Examples • 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 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) - 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, 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 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) - 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 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 asks agent j for its available services: (query-ref     :sender i     :receiver j    :content       (iota ?x (available-services j ?x))    …) Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

48. Agent/Software Integration • Integration of services provided by non-agent software into a multi-agent community • Definition of the relationship between agents and software systems • Allowing agents to describe, broker and negotiate over software systems • Allowing new software services to be dynamically introduced into an agent community • Defining how software resources can be described, shared and dynamically controlled in an agent community Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

49. New Agent Roles • To support specification, two new agent roleshave been identified: • Agent Resource Broker (ARB) • WRAPPER Agent Voislav Galić, Dušan Zečević,Đorđe Đurđević, Veljko Milutinović

50. GoodNews A system that automatically categorizesnews reports that reflect positively or negativelyon a company’s financial outlook