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Modelling and Managing AmbiguousContext in Intelligent Environments    Aitor Almeida, Diego López-de-Ipiña   Deusto Instit...
Need for ambiguity modeling• We can’t take certainty for granted when modeling real world  environments   – Unreliable sen...
Components of the ambiguity• We have considered two aspects of the ambiguity:  uncertainty and vagueness• Uncertainty mode...
AMBI2ONT: An ontology for the          ambiguous reality• We have created an ontology that takes into account the  certain...
AMBI2ONT: An ontology for the     ambiguous reality
AMBI2ONT: An ontology for the          ambiguous reality• Each ContextData individual has the following properties:   – cr...
AMBI2ONT: An ontology for the     ambiguous reality
Semantic Context Management For        Ambiguous Data
Semantic Context Management For          Ambiguous Data1. Adding the measure   – The sensor must provide the measure type,...
Semantic Context Management For          Ambiguous Data• Example of semantic rules:
Semantic Context Management For          Ambiguous Data• Example of spatial rules:
Semantic Context Management For          Ambiguous Data3. The data fusion process   –   Each room can have multiple sensor...
Semantic Context Management For          Ambiguous Data3. The data fusion process   –   To determine the combined measure ...
Semantic Context Management For          Ambiguous Data4. Processing the ambiguity   –   We have modified the JFuzzyLogic ...
Semantic Context Management For          Ambiguous Data4. Processing the ambiguity   –   The second type of uncertainty ta...
Semantic Context Management For          Ambiguous Data4. Processing the ambiguity   –   Uncertainty and fuzziness can app...
Semantic Context Management For          Ambiguous Data4. Processing the ambiguity   –   CF in CRISP Simple Rule          ...
Semantic Context Management For          Ambiguous Data4. Processing the ambiguity   –   CF in FUZZY_CRISP Rules       Whe...
Semantic Context Management For          Ambiguous Data4. Processing the ambiguity   –   Finally in the case of FUZZY_FUZZ...
Future Work•   Create a mechanism that automatically assesses the certainty factor of a    sensor comparing its data with ...
Thanks for your attention                       Questions?This work has been supported by project grant TIN2010-20510-C04-...
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Modelling and Managing Ambiguous Context in Intelligent Environments

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Presentation of the paper "Modelling and Managing Ambiguous Context in Intelligent Environments "in the UCAMI 2011 conference.

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Transcript of "Modelling and Managing Ambiguous Context in Intelligent Environments"

  1. 1. Modelling and Managing AmbiguousContext in Intelligent Environments Aitor Almeida, Diego López-de-Ipiña Deusto Institute of Technology - DeustoTech University of Deusto Bilbao, Spain {aitor.almeida, dipina}@deusto.es
  2. 2. Need for ambiguity modeling• We can’t take certainty for granted when modeling real world environments – Unreliable sensors: Sensors usually have a known degree of precision – Conflicting measures: What happens if various sensors of a room provide different measures? – Users’ subjective view of reality: the does not carry the same exact meaning for all the users.
  3. 3. Components of the ambiguity• We have considered two aspects of the ambiguity: uncertainty and vagueness• Uncertainty models the truthfulness of the different context data by assigning to them a certainty factor – E.g. The temperature of this room is 23 ºC with a certainty of 0.9• Vagueness helps us to model those situations where the boundaries between categories are not clearly defined – E.g. The room can be cold, medium or hot.
  4. 4. AMBI2ONT: An ontology for the ambiguous reality• We have created an ontology that takes into account the certainty (represented by a certainty factor, CF) and the vagueness (represented by fuzzy sets) of the context. – “the temperature of the room is 27ºC with a certainty factor of 0.2 and 18ºC with a certainty factor of 0.8” – “the temperature of the room is cold with a membership of 0.7”
  5. 5. AMBI2ONT: An ontology for the ambiguous reality
  6. 6. AMBI2ONT: An ontology for the ambiguous reality• Each ContextData individual has the following properties: – crisp_value: the measure taken by the associated sensor. In our system a sensor is defined as anything that provides context information. – certainty_factor: the degree of credibility of the measure. This metric is given by the sensor that takes the measure and takes values between 0 and 1. – linguistic_term: each measure has its fuzzy representation, represented as the linguistic term name and the membership degree for that term.
  7. 7. AMBI2ONT: An ontology for the ambiguous reality
  8. 8. Semantic Context Management For Ambiguous Data
  9. 9. Semantic Context Management For Ambiguous Data1. Adding the measure – The sensor must provide the measure type, its value, location and a certainty factor2. Processing the semantic and spatial data: – We apply a semantic inference process to achieve two goals: • make explicit the hidden implicit knowledge in the ontology • infer the positional information of each measure – Two different sets of rules: the semantic rules and the spatial heuristic rules
  10. 10. Semantic Context Management For Ambiguous Data• Example of semantic rules:
  11. 11. Semantic Context Management For Ambiguous Data• Example of spatial rules:
  12. 12. Semantic Context Management For Ambiguous Data3. The data fusion process – Each room can have multiple sensors that provide the same context data (e.g various thermometers in the same room). – The values and certainty factor of those measures can differ. – The process refines those individual measures into a single global measure for each room. – We use two types of strategies for this process: tourney and combination. – Using the tourney strategy the measure with the best CF is selected as the global measure of the room. – The combination strategy has three different behaviors : • Severe: The worst certainty factor from all the input measures is assigned to the combined measure. • Indulgent: The best certainty factor from all the input measures is assigned to the combined measure. • Cautious: An average certainty factor is calculated using the certainty factor from the input measures.
  13. 13. Semantic Context Management For Ambiguous Data3. The data fusion process – To determine the combined measure value we weight the individual values: Where: m: the measure values. cf: the measure certainty factor.
  14. 14. Semantic Context Management For Ambiguous Data4. Processing the ambiguity – We have modified the JFuzzyLogic Open Source fuzzy reasoner to accept also uncertainty information – The modified reasoner supports two types of uncertainty, uncertain data and uncertain rules. – The first type occurs when the input data is not completely reliable
  15. 15. Semantic Context Management For Ambiguous Data4. Processing the ambiguity – The second type of uncertainty takes place when the outcome of a rule is not fixed • E.g. “if the barometric pressure is high and the temperature is low there is a 60% chance of rain”
  16. 16. Semantic Context Management For Ambiguous Data4. Processing the ambiguity – Uncertainty and fuzziness can appear in the same rule and influence each other. • To process this we use the model described in “R. A. Orchard. FuzzyCLIPS Version 6.04A User’s Guide. Integrated Reasoning Institute for Information Technology National Research Council Canada. 1998”. – This model contemplates three different situations: • CRISP Simple Rule where both antecedent and matching fact are crisp values, • FUZZY_CRISP Simple Rule where both the antecedent and matching fact are fuzzy and the consequent is crisp and finally the • FUZZY_FUZZY Simple rule where all three are fuzzy.
  17. 17. Semantic Context Management For Ambiguous Data4. Processing the ambiguity – CF in CRISP Simple Rule Where: CFc: the certainty factor of the consequent. CFr: the certainty factor of the rule CFf: the certainty factor of the fact
  18. 18. Semantic Context Management For Ambiguous Data4. Processing the ambiguity – CF in FUZZY_CRISP Rules Where S is the measure of similarity between both fuzzy sets and is calculated using the following formula: Where: And:
  19. 19. Semantic Context Management For Ambiguous Data4. Processing the ambiguity – Finally in the case of FUZZY_FUZZY Simple Rule the certainty factor of the consequent is calculated using the same formula than in the CRISP Simple RULE. – Currently we do not support this type of combined reasoning for complex rules that involve multiple clauses in their antecedent.
  20. 20. Future Work• Create a mechanism that automatically assesses the certainty factor of a sensor comparing its data with the one provided by other sensors. – This will allow us to identify and discard malfunctioning sensors automatically.• Develop an ecosystem of reasoners to distribute the inference process. – We hope that this distribution will lead to a more agile and fast reasoning over the context data, allowing us to combine less powerful devices to obtain a rich and expressive inference• Explore the possibility of including uncertainty in the membership functions.
  21. 21. Thanks for your attention Questions?This work has been supported by project grant TIN2010-20510-C04-03(TALIS+ENGINE), funded by the Spanish Ministerio de Ciencia e Innovación
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