TLI371 – Distributed Artificial Intelligence in Mobile Environment Course Introduction

1. TLI371 – Distributed Artificial Intelligence in Mobile EnvironmentCourse Introduction Vagan Terziyan Department of Mathematical Information Technology University of Jyvaskyla vagan@it.jyu.fi ; terziyan@yahoo.com http://www.cs.jyu.fi/ai/vagan +358 14 260-4618

2. Contents • Course Introduction • Lectures and Links • Course Assignment • Course Exercise • Examples of course-related research

3. Practical Information • 8 Lectures (2 x 45 minutes each, in English) during period 21 November - 13 December according to the schedule; • Slides:available online plus hardcopies will be given; • Exercise (Write 3-5 pages description of possible solution of a given research problem); • Assignment (Make PowerPoint presentation file based on a research paper. Oral presentation is not required); • Exercise and assignment should be sent electronically to the lecturer until 13 December (24:00); • Exam 16 December, 12:00. There will be no exam for students who will submit the course exercise and assignment within the deadline and at least of satisfactory quality. Course mark in this case will be given based on the exercise and assignment.

4. Lectures Topics and Schedule 21 November 2002 – DAI Introduction and Web Content Personalization (today) Lecture 0 - ”DAI in Mobile Environment: Course Introduction” Lecture 1 - ”Web Content Personalization Overview” 22 November 2002 – Overview of Intelligent Agents Lecture 2 - ”What is an Intelligent Agent ?” 28 November 2002 – Overview of (Multi)Agent Technologies I Lecture 3 - ”Agent Technologies (1)” 29 November 2002 – Overview of (Multi)Agent Technologies II Lecture 4 - ”Agent Technologies (2)” 5 December 2002 – Overview of Filtering Techniques for Personalization Lecture 5 - ”Collaborative Filtering” 6 December 2002 – Probabilistic Networks for Personalization * Lecture 6 - ”Introduction to Bayesian Networks” 12 December 2002 – Profile and Location-Based Personalization with Agents Lecture 7 - ”Dynamic Integration of Virtual Predictors” 13 December 2002 – Metamodels for Profile Management in Mobile Commerce Lecture 8 - ”Metamodels for Managing Knowledge” *- selfstudy

5. DAI in Mobile Environment (example)

6. Distributed Artificial Intelligence[A. Tveit, DAI Course] • DAI is a sub-field of AI • DAI is concerned with problem solving where agents solve (sub-) tasks • Main areas of DAI • Multi-Agent Systems • Distributed Problem Solving

7. Distributed AI Applications Application Area Web Content Management Emerging Application Personalization Distributed transactions management Agent technologies Solutions Profile / Location management Data mining Knowledge metamodeling Beliefs management Filtering

8. Introduction:Semantic Web - new Possibilities for Intelligent Web Applications

9. Motivation for Semantic Web

10. Semantic Web Content: New “Users” applications agents

11. Some Professions around Semantic Web AI Professionals Content creators Content Logic, Proof and Trust Mobile Computing Professionals Web designers Ontologies Agents Annotations Ontology engineers Software engineers

12. Semantic Web: Resource Integration Semantic annotation Shared ontology Web resources / services / DBs / etc.

13. What else Can be Annotated for Semantic Web ? External world resources Web resources / services / DBs / etc. Web users (profiles, preferences) Shared ontology Web agents / applications Web access devices

14. Word-Wide Correlated Activities Semantic Web Agentcities is a global, collaborative effort to construct an open network of on-line systems hosting diverse agent based services. Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation Agentcities Grid Computing Wide-area distributed computing, or "grid” technologies, provide the foundation to a number of large-scale efforts utilizing the global Internet to build distributed computing and communications infrastructures. FIPA FIPA is a non-profit organisation aimed at producing standards for the interoperation of heterogeneous software agents. Web Services WWW is more and more used for application to application communication. The programmatic interfaces made available are referred to as Web services. The goal of the Web Services Activity is to develop a set of technologies in order to bring Web services to their full potential

15. University of Jyvaskyla Experience:Examples of Related Courses

16. DAI Course: Lectures

17. Lecture 0: This Lecture - DAI Introduction http://www.cs.jyu.fi/ai/vagan/DAI_Introduction.ppt

18. Lecture 1: Web Content Personalization Overview http://www.cs.jyu.fi/ai/vagan/Personalization.ppt

19. Lecture 2: What is an Intelligent Agent ? http://www.cs.jyu.fi/ai/vagan/Agents.ppt

20. Lectures 3-4: Agent Technologies http://www.cs.jyu.fi/ai/vagan/Agent_Technologies.ppt

21. Lecture 5: Collaborative Filtering http://www.cs.jyu.fi/ai/vagan/Collaborative_Filtering.ppt

22. Lecture 6: Introduction to Bayesian Networks http://www.cs.jyu.fi/ai/vagan/Bayes_Nets.ppt

23. Lecture 7: Dynamic Integration of Virtual Predictors http://www.cs.jyu.fi/ai/vagan/Virtual_Predictors.ppt

24. Lecture 8: Metamodels for Managing Knowledge http://www.cs.jyu.fi/ai/vagan/Metamodels.ppt

25. DAI Course: Exercise

26. Task for the Exercise (according to A. Raja) (1) Consider the home of the future where there are software agents in a mobile environment that are helping to manage the running of a house. There will be: • (1) Personal assistant agents that will know of your preferences of temperature, humidity, light, sound, etc., and who you want to interact with; • (2) There will be agents that can measure appropriate environmental conditions with specific devices; • (3) There will be agents that effect appropriate environmental conditions with specific devices; • (4) There will be agents that control expenses for the use of appropriate devices; • (5) There will be agents that manage the telephone communications; • (6) There will be agents that manage security issues such as fire, earthquake, flood protection, etc.

27. Task for the Exercise (according to A. Raja) (2) • Assume that the agents are heterogenous (i.e. have not be generated by one designer), for example when you get a new device it will come with an agent; for instance, the heating measurement agent may not come from the same company as the air-conditioning agent. • Think about the possibility of having these agents work together. What are the capabilities of the agents, what type of cooperation needs to occur among them, are there needs for the agents to negotiate, are there situations where local objectives are at odds with global objectives such as minimizing electrical usage? What type of information needs to be exchanged among the agents?

28. Task for the Exercise (according to A. Raja) (3) • How would you organize the agents – would you have a hierarchy of agents in terms of their control responsibilities? How would you allow agents to integrate new agents into the system, for instance, when you buy a new device. • What are the specific characteristics required by a language in order that these agents can share information? If there are no dedicated resources for each agent, but rather a pool of resources that can be used by agents, what new issues does this introduce? Do agents need to reason about the intentions of other agents?

29. Task for the Exercise (according to A. Raja) (4) • In answering these and related issues that you may consider, please be concrete with specific and numerous examples/scenarios. You should first start out the effort by detailing the collection of agents that you see in the house of the future, what their responsibilities are, and their patterns of interaction with other agents. Including figures, it should be at least 3 to 5 pages long.

30. Format, Submission and Deadlines • Format: MS Word doc. (winzip encoding allowed), name of file is student’s family name; • Presentation should contain all references to the materials used; • Deadline - 13 December 2002 (24:00); • Files with presentations should be sent by e-mail to Vagan Terziyan (vagan@it.jyu.fi); • Notification of evaluation - until 14 December (16:00).

31. DAI Course: Assignment

32. Assignment in brief • Students are expected to select one of below recommended papers, which is not already selected by some other student, register his/her choice from the Course Lecturer and make PowerPoint presentation based on that paper. The presentation should provide evidence that a student has got the main ideas of the paper, is able to provide his personal additional conclusions and critics to the approaches used.

33. Evaluation criteria for the assignment • Content and Completeness; • Clearness and Simplicity; • Discovered Connections to DAI Course Material; • Originality, Personal Conclusions and Critics; • Design Quality.

34. Format, Submission and Deadlines • Format: PowerPoint ppt. (winzip encoding allowed), name of file is student’s family name; • Presentation should contain all references to the materials used, including the original paper; • Deadline - 13 December 2002 (24:00); • Files with presentations should be sent by e-mail to Vagan Terziyan (vagan@it.jyu.fi); • Notification of evaluation - until 14 December (16:00).

35. Papers for Course Assignment (1) • Paper 1:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_1_P.pdf • Paper 2:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_2_P.pdf • Paper 3:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_3_CF.pdf • Paper 4:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_4_CF.pdf • Paper 5:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_5_MW.pdf • Paper 6:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_6_BN.pdf • Paper 7:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_7_BN.pdf • Paper 8:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_8_MM.pdf

36. Papers for Course Assignment (2) • Paper 9:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_9_WM.pdf • Paper 10:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_10_WM.pdf • Paper 11:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_11_III.pdf • Paper 12:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_12_III.pdf • Paper 13:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_13_KM.pdf • Paper 14:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_14_ES.pdf • Paper 15:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_15_MDB.pdf • Paper 16:http://www.cs.jyu.fi/ai/vagan/course_papers/Paper_16_MDB.pdf

37. University of Jyvaskyla Experience: Examples of Course-Related Research

38. Mobile Location-Based Service in Semantic Web

39. Mobile Transactions Management in Semantic Web

40. P-Commerce in Semantic Web Terziyan V., Architecture for Mobile P-Commerce: Multilevel Profiling Framework, IJCAI-2001 International Workshop on "E-Business and the Intelligent Web", Seattle, USA, 5 August 2001, 12 pp.

41. Semantic Metanetwork for Metadata Management Semantic Metanetwork is considered formally as the set of semantic networks, which are put on each other in such a way that links of every previous semantic network are in the same time nodes of the next network. In a Semantic Metanetwork every higher level controls semantic structure of the lower level. Terziyan V., Puuronen S., Reasoning with Multilevel Contexts in Semantic Metanetworks, In: P. Bonzon, M. Cavalcanti, R. Nossun (Eds.), Formal Aspects in Context, Kluwer Academic Publishers, 2000, pp. 107-126.

42. Petri Metanetwork for Management Dynamics • A metapetrinet is able not only to change the marking of a petrinet but also to reconfigure dynamically its structure • Each level of the new structure is an ordinary petrinet of some traditional type. • A basic level petrinet simulates the process of some application. • The second level, i.e. the metapetrinet, is used to simulate and help controlling the configuration change at the basic level. Terziyan V., Savolainen V., Metapetrinets for Controlling Complex and Dynamic Processes, International Journal of Information and Management Sciences, V. 10, No. 1, March 1999, pp.13-32.

43. Bayesian Metanetwork for Management Uncertainty Terziyan V., Vitko O., Bayesian Metanetworks for Mobile Web Content Personalization, In: Proceedings of 2nd WSEAS International Conference on Automation and Integration (ICAI’02), Puerto De La Cruz, Tenerife, December 2002.

44. Multidatabase Mining based on Metadata Puuronen S., Terziyan V., Logvinovsky A., Mining Several Data Bases with an Ensemble of Classifiers, In: T. Bench-Capon, G. Soda and M. Tjoa (Eds.), Database and Expert Systems Applications, Lecture Notes in Computer Science, Springer-Verlag, V. 1677, 1999, pp. 882-891.