Active Perception over Machine and Citizen Sensing
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The document discusses the Ohio Center of Excellence on Knowledge-Enabled Computing's focus on active perception involving both machine and citizen sensing. It highlights the significance of sensor networks and background knowledge in enhancing perception abilities, as well as the integration of various cognitive theories into a general model of perception. Additionally, it showcases practical applications, such as event detection from weather observations, and emphasizes the importance of efficient information exchange in the perception cycle.
Active Perception over Machine and Citizen Sensing
- 1. Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis) Active Perception over Machine and Citizen Sensing Cory Henson and Amit Sheth Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing Wright State University, Dayton, Ohio, USA 1
- 2. Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis) A cross-country flight from New York to Los Angeles on a Boeing 737 plane generates a massive 240 terabytes of data - GigaOmni Media 2
- 3. Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis) In the next few years, sensors networks will produce 10-20 times the amount of generated by social media - GigaOmni Media 3
- 4. Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis) For example, both people and machines are capable of observing qualities, such as redness. Observer observes Quality * Formally described in a sensor/observation ontology 5
- 5. Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis) Sensor and Sensor Network (SSN) Ontology http://www.w3.org/2005/Incubator/ssn/wiki/ 6
- 6. Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis) The ability to perceive is afforded through the use of background knowledge, relating observable qualities to entities in the world. Quality inheres in Entity 7 * Formally described in domain ontologies (and knowledge bases)
- 7. Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis) http://linkedsensordata.com 8
- 8. Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis) With the help of sophisticated inference, both people and machines are also capable of perceiving entities, such as apples. Perceiver perceives Entity • the ability to degrade gracefully with incomplete information • the ability to minimize explanations based on new information • the ability to reason over data on the Web • fast (tractable) 9
- 9. Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis) minimize explanations tractable degrade gracefully Web reasoning Web Ontology Language (OWL) Parsimonious Covering Theory (PCT) 10
- 10. Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis) Conversion of PCT to OWL 2 (EL) Parsimonious Covering Theory (Abductive Logic) * OWL-DL * Cory Henson, Krishnaprasad Thirunarayan, Amit Sheth, Pascal Hitzler. Representation of Parsimonious Covering Theory in OWL-DL. In: Proceedings of the 8th International Workshop on OWL: Experiences and Directions (OWLED 2011), San Francisco, CA, United States, June 5-6, 2011. 11 11
- 11. Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis) The ability to perceive efficiently is afforded through the cyclical exchange of information between observers and perceivers. Observer sends observation sends focus Perceiver 12 Traditionally called the Perception Cycle (or Active Perception)
- 12. Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis) Nessier’s Perception Cycle 13
- 13. Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis) Cognitive Theory of Perception (timeline) • 1970’s - Perception is an active, cyclical process of exploration and interpretation - Nessier’s Perception Cycle • 1980’s - The perception cycle is driven by background knowledge in order to generate and test hypotheses. - Richard Gregory (optical illusions) • 1990’s - In order to effectively test hypotheses, some observations are more informative than others. - Norwich’s Entropy Theory of Perception 14
- 14. Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis) Integrated together, we have an general model – capable of abstraction – relating observers, perceivers, and background knowledge. Observer sends observation observes sends focus Perceiver Quality inheres in perceives 15 Entity
- 15. Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis) i 16 ntelleg “to perceive”
- 16. Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis) Application of Weather Traffic 17 17
- 17. Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis) Traffic Application 18
- 18. Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis) Detection of events, such as blizzards, from weather station observations on LinkedSensorData Weather Application 50% savings in resource requirements needed for detection 19
- 19. Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis) thank you, and please visit us at http://semantic-sensor-web.com Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing Wright State University, Dayton, Ohio, USA 20