Ambient Intelligence in Adaptive Online Experiments


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Ambient Intelligence in Adaptive Online Experiments

  1. 1. Ambient ntelligence in Adaptive Online Experiments Violeta Damjanovic PhD Thesis Defense : November 18, 2008 PhD Supervisor : Prof. Dr. Vladan Devedzic FON, University of Belgrade, Serbia
  2. 2. Outline of the thesis <ul><li>Part 1 : Statement of the problem </li></ul><ul><li>Part 2 : Research hypothesis </li></ul><ul><li>Part 3 : Related work </li></ul><ul><li>Part 4 : Purpose of the study </li></ul><ul><li>Part 5 : Proposed solution </li></ul><ul><li>Part 6 : Evaluation of the validity of results </li></ul><ul><li>Part 7 : Expected contribution of this work </li></ul><ul><li>Part 8 : Limitations </li></ul><ul><li>Part 9 : Comments on future directions </li></ul>
  3. 3. Part 1: Statement of the problem <ul><li>Ambient Intelligence </li></ul><ul><li>Adaptation (personalization + contextualization) </li></ul><ul><li>Online experiments </li></ul>Thesis’ Technology Lotus <ul><ul><li>Term: Pervasive Semantic Web (Vasquez, 2006) </li></ul></ul>
  4. 4. Ambient Intelligence <ul><li>Mark Weiser, Xerox PARC (1988) </li></ul><ul><li>Philips Research (1999) </li></ul><ul><li>AmI vision: a pervasive and unobtrusive intelligence in the surrounding environment that supports the activities and interactions of the users </li></ul>Part 1: Statement of the Problem <ul><li>“ sense of presence” (Riva, 2005): </li></ul><ul><ul><li>a function of our experience of a given medium </li></ul></ul><ul><ul><li>a neuropsychological phenomenon </li></ul></ul>
  5. 5. Ambient Intelligence <ul><li>AmI key features: (a) intelligence, (b) embedding </li></ul><ul><li>AmI builds on three key technologies: </li></ul><ul><ul><li>Ubiquitous computing </li></ul></ul><ul><ul><li>Ubiquitous communications </li></ul></ul><ul><ul><li>Intelligent user interfaces (User adaptive interfaces) </li></ul></ul><ul><li>AmI system has to support (Riva, 2005): </li></ul><ul><ul><li>User awareness </li></ul></ul><ul><ul><li>Activity awareness </li></ul></ul><ul><ul><li>Situation awareness </li></ul></ul>Part 1: Statement of the Problem
  6. 6. Adaptation <ul><li>The role of adaptation in ambient intelligence: analysis and detection of user activities </li></ul><ul><ul><li>Context awareness </li></ul></ul><ul><ul><li>Multimodal communication </li></ul></ul><ul><ul><li>User-centered adaptive interaction </li></ul></ul>Part 1: Statement of the Problem Adaptable system Adaptive system User-driven personalization System-driven personalization Adaptability Adaptation
  7. 7. Online Experiments <ul><li>OEs are remotely controlled experiment or software simulations of real experiments build for learning purposes </li></ul><ul><li>Learners get hands-on experience without the need to leave their workplace to go to the traditional local lab </li></ul><ul><li>OEs – components (Faltin, 2005): </li></ul><ul><ul><ul><ul><ul><li>Computer simulations of devices </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Remote control of devices </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Remote desktop assistance </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Computer based team work tools </li></ul></ul></ul></ul></ul>Part 1: Statement of the Problem
  8. 8. Online Experiments <ul><li>OEs & AmI – key challenges: </li></ul><ul><ul><li>Designing intelligent user interfaces that can be used in pervasive environments </li></ul></ul><ul><ul><li>Combining the Description Logic (DL) and uncertainty reasoning </li></ul></ul><ul><ul><li>Increasing optimal experience (the link between the highest level of presence-as-feeling with a positive emotional state (Riva, 2005)) through online experimenting </li></ul></ul>Part 1: Statement of the Problem
  9. 9. <ul><li>Pervasive Semantic Web – characterized by openness, decentralized process control and dynamic behaviour that may easily introduce ill-defined knowledge and inconsistencies </li></ul><ul><li>Combining the DL and uncertainty reasoning </li></ul><ul><ul><li>Albert Einstein: “ So far as the laws of mathematics refer to reality, they are not certain. And so far as they are certain, they do not refer to reality.” </li></ul></ul><ul><ul><li>Stephen Jay Gould (1944): “misunderstanding of probability may be the greatest of all general impediments to scientific literacy.” </li></ul></ul>Part 2 : Research hypothesis
  10. 10. <ul><li>Alternatives to probability theory: </li></ul><ul><ul><li>Default reasoning </li></ul></ul><ul><ul><li>Non-monotonic logic </li></ul></ul><ul><ul><li>Rule-based methods for uncertain reasoning </li></ul></ul><ul><ul><ul><li>certa i nty rule factors </li></ul></ul></ul><ul><ul><li>Dempster-Shafer theory; Fuzzy sets & Fuzzy logic </li></ul></ul><ul><ul><li>Bayesian networks (belief network) </li></ul></ul>Research hypothesis Part 2: Research hypothesis
  11. 11. Part 3: Related work <ul><li>Probabilistic extensions in DLs: </li></ul><ul><ul><li>P-Classic </li></ul></ul><ul><ul><li>PTDL </li></ul></ul><ul><ul><li>P-SHOQ </li></ul></ul><ul><ul><li>BayesOWL </li></ul></ul>
  12. 12. P-Classic <ul><li>(Koller et al., 1997): Probabilistic component of a P-CLASSIC kb includes: </li></ul><ul><ul><li>a number of different p-classes each of which is a Bayesian network over basic properties </li></ul></ul><ul><ul><li>a number of fillers for the different roles </li></ul></ul><ul><ul><li>p-classes from which the role fillers are chosen </li></ul></ul>Part 3: Related work PTDL <ul><li>(Yelland, 1999): It extends Tiny Description Logic (TDL) with “conjuction” and “role quantification” operators </li></ul>
  13. 13. <ul><li>(Ding & Peng, 2004): It translates a given ontology to a Bayesian network (BN), and then treats ontological reasoning as probabilistic inferences in the translated BN </li></ul>BayesOWL Part 3: Related work P-SHOQ(D) <ul><li>(Guigno & Lukasiewicz, 2002): Probabilistic extension of DL SHOQ(D) which is semantics behind DAML+OIL based on the notion of probabilistic lexicographic entailment from probabilistic default reasoning </li></ul>
  14. 14. Part 4 : Purpose of the study <ul><li>Research questions: </li></ul><ul><ul><li>Probabilistic knowledge integration on the Pervasive Semantic Web </li></ul></ul><ul><ul><li>Integration of the probabilistic models on process performance and execution (M1) into ontological models (M2) </li></ul></ul><ul><ul><ul><li>M1: Probabilistic models simulate process execution in the ambient environment for online experimenting that goes asynchronous </li></ul></ul></ul><ul><ul><ul><li>M1: Probabilistic Asynchronous Process Algebra (Herescu, 2002): to explain communication between various sensor systems </li></ul></ul></ul><ul><ul><ul><li>M2: Ontological models </li></ul></ul></ul>
  15. 15. M1: Probabilistic Asynchronous Process Algebra Part 4: Purpose of the study <ul><li>A kind of Parrow’ process algebra </li></ul><ul><li>The syntax of the process algebra: </li></ul><ul><ul><li>P ::= </li></ul></ul><ul><ul><li>0 (nil) </li></ul></ul><ul><ul><li>| x( u ) (input) </li></ul></ul><ul><ul><li>| x ‘( u ) (output) </li></ul></ul><ul><ul><li>| (x)P (restriction) </li></ul></ul><ul><ul><li>| P1 | P2 (parallel composition) </li></ul></ul><ul><ul><li>| ∑ x( u ) . P (alternative composition – sum) </li></ul></ul><ul><ul><li>| if (x = y) then P1 (conditional - match) </li></ul></ul><ul><ul><li>| if (x ≠ y) then P2 ( conditional - mismatch) </li></ul></ul><ul><ul><li>| ! x( u ).P (replication) </li></ul></ul>
  16. 16. <ul><li>An extension of the asynchronous process algebra enhanced with a notion of random choice (Herescu, 2002) </li></ul><ul><li>The operational semantics of the probabilistic asynchronous algebra is based on the probabilistic automata of Segala and Lynch, which distinguishes between probabilistic (random choice of the process) and nondeterministic behavior (arbitrary decisions of an external scheduler) (Herescu, 2002) </li></ul>Part 4: Purpose of the study M1: Probabilistic Asynchronous Process Algebra
  17. 17. The operational semantics of the probabilistic asynchronous algebra (Herescu, 2002)
  18. 18. Probabilistic Asynchronous Process Algebra - Metamodel
  19. 19. Part 5: Proposed solution … to integrate probabilistic asynchronous process‘ knowledge into ontologies Transformation mechanism  pa2OWL … to collect and ontologize knowledge on ambient processes AmIART-specific components P-OWL … to enable adaptive, user-oriented and intelligent experimental environment Adaptive & Semantic Ambient System AmIART
  20. 20. AmIART: conceptual model Part 5: Proposed solution
  21. 21. P-OWL: AmIART-specific components Adaptation Manager  pa2OWL Web Agent System Part 5: Proposed solution
  22. 22. Adaptation Manager Part 5: Proposed solution
  23. 23. Web Agent System &  pa2OWL Part 5: Proposed solution
  24. 24.  pa2OWL: Implementation details
  25. 25. Part 5: Proposed solution Transformation Rule
  26. 26. Part 5: Proposed solution
  27. 27. <ul><li>Inspired by: K ohavi & Longbotham, (2007 ). “Online Experiments: Lessons Learned”, Computer, Vol.40, No.9, pp. 103-105 </li></ul><ul><li>OEC – Overall Evaluation Criterion </li></ul><ul><li>OEC objectives: higher revenue, more users, greater user engagement… </li></ul><ul><li>Planning for sufficient sample size (= how long to run experiments to detect small effects) </li></ul><ul><li>Estimating standard deviation from data collected during experiments (historical data) </li></ul>Part 6: Evaluation of the validity
  28. 28. Part 6: Evaluation of the validity <ul><li>Scenario 1: online experimenting in learning about painting technology, techniques and materials (i.e. preventive conservation strategies, restoration, reproduction) </li></ul><ul><li>Scenario 2: online experimenting, analyzing art and craft, and detecting fine art fraud (i.e. painting damage diagnosis, original expertise) </li></ul><ul><li>User sample: user age, educational background, employment, religion, expectation </li></ul><ul><li>Descriptive statistics; Student t-test; </li></ul>AmIART evaluation
  29. 29. <ul><li>Integration of the AmI and the Pervasive Semantic Web </li></ul><ul><li>Integration of the Pervasive Semantic Web into the online experimenting systems </li></ul><ul><li>Integration of the Pervasive Semantic Web technologies into the adaptive systems </li></ul><ul><li>Considering uncertain and probabilistic knowledge from the Pervasive Semantic Web environments </li></ul><ul><li>Re-engineering between non-ontological probabilistic knowledge and ontologies </li></ul><ul><li>AmIART system design and implementation of the selected integration component </li></ul><ul><li>The use of the AmIART system in experimenting </li></ul><ul><li>OEs: flexible learning in time and place; cost savings through experiment sharing; time savings </li></ul>Part 7: Expected contribution
  30. 30. Part 8: Limitations <ul><li>OEs: real collaboration vs. sense of presence </li></ul>
  31. 31. <ul><li>The experimental environment should represent the real environment as closely as possible </li></ul><ul><li>Key challenge in combining OEs and AmI: Designing intelligent user interfaces that can be used in pervasive environments </li></ul><ul><li>Web agent system – design & implementation of the agent system to overcome the sensor inactivity in sense of knowledge management on the Pervasive Semantic Web </li></ul><ul><li>Increasing optimal experience (the link between the highest level of presence-as-feeling with a positive emotional state (Riva, 2005)) through online experimenting </li></ul>Part 9: Comments on future directions
  32. 32. <ul><li>[email_address] </li></ul><ul><li>[email_address] </li></ul>Thank you for your attention...