Modeling Decentralized Information Flow in Ambient Environments

1. Modeling Decentralized Information Flow in Ambient Environments Jurriaan van Diggelen, Robbert-Jan Beun, Rogier M. van Eijk, Peter J. Werkhoven Utrecht University, TNO the Netherlands AmI.d 2007, 17 September 2007

2. works as Postdoc started at 01/01/2007 ends at ICIS 31/12/2009 ESA TNO Utrecht University Robbert-jan Beun Rogier van Eijk Peter Werkhoven Who am I? Jurriaan van Diggelen Introduction

3. Ubiquitous computing for crisis management Humans, devices and sensors should collaborate as a team Motivation

4. Interoperability issues • Serendipitous interoperability: the ability of software systems to discover and utilize services they have not seen before, and that were not considered when the systems were designed. [Ora Lasilla ‘02] • Terminological misunderstandings: Agents should use the same terms to represent the same meanings. • Different world-views: Agents typically represent information at different levels of abstraction. • Information overload: When vast amounts of data are available, only the relevant information must be exchanged. Motivation

5. Terminological misunderstandings Agent 1 Agent 2 Crisis Crisis Flooding Flooding Fire Fire Bush-Fire House-Fire Bush-Fire House-Fire Ontologies specify the structure of information, i.e. they capture domain knowledge. By agreeing on an ontology, agents can achieve semantic interoperability Motivation

6. Research questions In an ontology-based approach: • How can heterogeneous world-views be reconciled? • How can information overload be prevented? Motivation

7. Preserving autonomy Situation • Fully shared ontologies conflict with the agents’ autonomy. • A temperature sensor views the world at a different level of abstraction than a crisis manager. • Local autonomy versus Central control Safe Temperature Emergency Dangerous Every agent needs its own world view Semantic interoperability requires standardization of ontologies Approach

8. Layered ontologies Ag-1 Ag-3 • Agents communicate using the shared layers of their ontologies • This enables semantic interoperability while preserving the agents’ private ontologies • Balance between local autonomy & central control Temp Temperature Ag-2 Crisis Dangerous Emergency Safe Fire Fire Mapping Temp Temperature Mapping Fire Fire Approach

9. No predefined communication patterns c5 c2 c1 c6 c2 c3 c8 c6 c7 c3 c8 c4 c4 We don’t want the communication network to be fixed in advance: • To prevent the system from becoming brittle • To allow communication between agents which are completely unknown to each other. Approach

10. A flexible interaction mechanism c5 c2 c1 c6 c2 c3 c8 c6 c7 c3 c8 c4 c4 • Agents dynamically establish their communication network at run-time • Instead of specifying who-talks-to-whom, we specify who-wants-to-know-what. Approach

11. Translating between different contexts • Ontologies are formally defined in description logic • Agents can translate fluently between different contexts by using the notion of informativeness • C1: Sunny is informative for C4:BlackIceDanger • C1: Rainy is informative for C4:BlackIceDanger • C1: Cloudy is not informative for C4:BlackIceDanger Approach

12. Reducing communication load • Suppose the agent intends to know whether C4:BlackIceDanger • the agent may query C1:Sunny • the agent may query C1:Rainy • the agent may not query C1:Cloudy Note that the answer to a query doesnot necessarily provide the agent with the desired information. For example: a negative answer to C1:Sunny is not informative. Approach

13. Using quantitative criteria • When multiple concepts are informative, which concept is best to query depends on the expected answer. • Suppose that • answers to query C are mostly negative • answers to query D are mostly positive query D would be the best to pose first, as this will reduce the average dialogue length Approach

14. An intuitive example Suppose someone with joint pains comes to see a doctor. The doctor is more likely to ask Have you been doing any heavy physical activity lately? rather than Have you been stung by a west-Nigerian mountain bee lately? Approach

15. Using entropy Entropy measures the purity of information Information gain measures the expected reduction in entropy Approach

16. Using information gain • Reduce information load by querying the concept with the highest information gain • Analogous to the ID3 algorithm for learning the smallest decision tree. c2:g F T c3:h F c2:e F ID3 T c1:d c3:h F F T c3:h F c3:h T Approach

17. UbiSmart OWL files Sensor data • Each UBISmart agent has its own ontology specified in OWL (developed by W3C) • The agents’ OWL knowledge bases are built on top of Protégé (developed at Stanford) • The agents perform reasoning by using the description logic reasoner Fact++ (developed at Manchester) • Sensor-agent obtain sensor data by reading a file UBISmart Fact++ UbiSmart

18. UbiSmart

19. Conclusion • As information plays a central role in ubiquitous systems, such systems should be programmed from an information perspective. • Ontologies tell the agent what information can be represented and communicated. • Information needs tell the agent what information it must seek. Conclusion

20. Relevant- Message Future work:Message Filtering for Mobile Police Officers UbiSmart is not restricted to dealing with time instances. Message AboutLocation HasLocation location1 officer1 message1 Conclusion

21. Query Query Establishing complex communication networks • Information flows may be circular • Information may flow from computer to human or from human to computer Police Centre Mobile officer … RelevantMessage Message AboutLocation HasLocation Message AboutLocation HasLocation … Conclusion