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Compact representation of conditional probability
for rule-based mobile context-aware systems
Szymon Bobek, Grzeg...
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Outline I
1 Introduction
2 Previous works
3 Proposed solution
4 Probabilistic interpretation of XTT2 rules
5 Prob...
ual-logo
Outline
1 Introduction
2 Previous works
3 Proposed solution
4 Probabilistic interpretation of XTT2 rules
5 Probab...
ual-logo
Mobile context-aware systems (mCAS)
• Where you are, who you are with, what resources are nearby
(Schillit)
• Any...
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Mobile environment and uncertainty
SBK+GJN (AGH-UST) Indect 5 August 2015 5 / 28
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Different types of uncertainty
High-level classification
1 Uncertainty due to lack of knowledge – that comes from i...
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Different types of uncertainty
High-level classification
1 Uncertainty due to lack of knowledge – that comes from i...
ual-logo
Different types of uncertainty
High-level classification
1 Uncertainty due to lack of knowledge – that comes from i...
ual-logo
Different uncertainty modelling and handling
mechanisms
Uncertainty source
Lack of
knowledge
Semantic
imprecision
...
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Mobile environment and uncertainty
Nature of mCAS
The uncertainty of data is inevitable and it is dynamic
mCAS ar...
ual-logo
Outline
1 Introduction
2 Previous works
3 Proposed solution
4 Probabilistic interpretation of XTT2 rules
5 Probab...
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CF approach
Intelligibility
Mediation
Uncertainty
Dynamics
SBK+GJN (AGH-UST) Indect 5 August 2015 10 / 28
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CF approach
Intelligibility
Mediation
Uncertainty
Dynamics
Rules
SBK+GJN (AGH-UST) Indect 5 August 2015 10 / 28
ual-logo
CF approach
Intelligibility
Mediation
Uncertainty
Dynamics
Rules
CF
SBK+GJN (AGH-UST) Indect 5 August 2015 10 / 28
ual-logo
CF approach
Intelligibility
Mediation
Uncertainty
Dynamics
Rules
CF
Dynamic CF
SBK+GJN (AGH-UST) Indect 5 August ...
ual-logo
CF approach
Intelligibility
Mediation
Uncertainty
Dynamics
Rules
CF
Dynamic CF
SBK+GJN (AGH-UST) Indect 5 August ...
ual-logo
CF approach
Intelligibility
Mediation
Uncertainty
Dynamics
Rules
CF
Dynamic CF
HeaRTDroid
SBK+GJN (AGH-UST) Indec...
ual-logo
CF approach
Intelligibility
Mediation
Uncertainty
Dynamics
Rules
CF
Dynamic CF
HeaRTDroid
XTT2 rule representatio...
ual-logo
CF was not enough
Assumed system state
Weather forecast: sunny weather with certainty 0.3, cloudy with 0.1, and
r...
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Uncertainties
(?) weather (?) user profile (?) activity cf(conditions) cf(rule) cf(conclusion)
0.3 0.6 0.8 0.3 1 0...
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CF was not enough
Assumed system state
Weather forecast: sunny weather with certainty 0.0, cloudy with 0.0,
and r...
ual-logo
Uncertainties
(?) weather (?) user profile (?) activity cf(conditions) cf(rule) cf(conclusion)
0.0 0.6 0.8 0.0 1 0...
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Outline
1 Introduction
2 Previous works
3 Proposed solution
4 Probabilistic interpretation of XTT2 rules
5 Probab...
ual-logo
Use Bayesian Networks as ”backup” representation
Solution
XTT2 models can be immediately translated into Bayesian...
ual-logo
Use Bayesian Networks as ”backup” representation
Solution
XTT2 models can be immediately translated into Bayesian...
ual-logo
Use Bayesian Networks as ”backup” representation
Solution
XTT2 models can be immediately translated into Bayesian...
ual-logo
Use Bayesian Networks as ”backup” representation
Solution
XTT2 models can be immediately translated into Bayesian...
ual-logo
Use Bayesian Networks as ”backup” representation
Solution
XTT2 models can be immediately translated into Bayesian...
ual-logo
Outline
1 Introduction
2 Previous works
3 Proposed solution
4 Probabilistic interpretation of XTT2 rules
5 Probab...
ual-logo
ALSV(FD) logic
XTT2 rule in ALSV(FD) logic
(Ai ∝ di ) ∧ (Aj ∝ dj ) ∧ . . . (Am ∝ Vm) ∧ (An ∝ Vn) −→ RHS
Syntax In...
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Probabilistic interpretation of ALSV(FD) rule
XTT2 rule as conditional probability
(Ai ∝ di ) ∧ (Aj ∝ dj ) ∧ . . ...
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Probabilistic interpretation of XTT2 models
(?) location (?) daytime (?) today (->) action
= home
= outside
= wor...
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Probabilistic interpretation of XTT2 models
SBK+GJN (AGH-UST) Indect 5 August 2015 20 / 28
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Outline
1 Introduction
2 Previous works
3 Proposed solution
4 Probabilistic interpretation of XTT2 rules
5 Probab...
ual-logo
Hybrid reasoning
Data: E – the set of all known attributes values
A – the set of attributes which values are to b...
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Hybrid reasoning
Assumptions
Value of attribute G is needed
Only value of attribute C is known
Attribute F is set...
ual-logo
Hybrid reasoning
Assumptions
Value of attribute G is needed
Only value of attribute C is known
Attribute F is set...
ual-logo
Hybrid reasoning
Assumptions
Value of attribute G is needed
Only value of attribute C is known
Attribute F is set...
ual-logo
Hybrid reasoning
Assumptions
Value of attribute G is needed
Only value of attribute C is known
Attribute F is set...
ual-logo
Hybrid reasoning
Assumptions
Value of attribute G is needed
Only value of attribute C is known
Attribute F is set...
ual-logo
Hybrid reasoning
Assumptions
Value of attribute G is needed
Only value of attribute C is known
Attribute F is set...
ual-logo
Hybrid reasoning
Assumptions
Value of attribute G is needed
Only value of attribute C is known
Attribute F is set...
ual-logo
Hybrid reasoning
Assumptions
Value of attribute G is needed
Only value of attribute C is known
Attribute F is set...
ual-logo
Hybrid reasoning
Assumptions
Value of attribute G is needed
Only value of attribute C is known
Attribute F is set...
ual-logo
Hybrid reasoning
Assumptions
Value of attribute G is needed
Only value of attribute C is known
Attribute F is set...
ual-logo
Hybrid reasoning
Assumptions
Value of attribute G is needed
Only value of attribute C is known
Attribute F is set...
ual-logo
Outline
1 Introduction
2 Previous works
3 Proposed solution
4 Probabilistic interpretation of XTT2 rules
5 Probab...
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Prototype Implementation
Components
HeaRTDroid for deterministic reasoning and training set preparation
Translato...
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Outline
1 Introduction
2 Previous works
3 Proposed solution
4 Probabilistic interpretation of XTT2 rules
5 Probab...
ual-logo
Summary and future work
Summary
We provided probabilistic interpretation of XTT2 knowledge representation
We prop...
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Thank you for your attention!
Do you have any questions?
RuleML 2015
http://geist.agh.edu.pl
SBK+GJN (AGH-UST) In...
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RuleML2015: Compact representation of conditional probability for rule-based mobile context-aware systems

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Context-aware systems gained huge popularity in recent
years due to rapid evolution of personal mobile devices. Equipped with
variety of sensors, such devices are sources of a lot of valuable information
that allows the system to act in an intelligent way. However, the
certainty and presence of this information may depend on many factors
like measurement accuracy or sensor availability. Such a dynamic
nature of information may cause the system not to work properly or
not to work at all. To allow for robustness of the context-aware system
an uncertainty handling mechanism should be provided with it. Several
approaches were developed to solve uncertainty in context knowledge
bases, including probabilistic reasoning, fuzzy logic, or certainty
factors. In this paper, we present a representation method that combines
strengths of rules based on the attributive logic and Bayesian networks.
Such a combination allows efficiently encode conditional probability distribution
of random variables into a reasoning structure called XTT2.
This provides a method for building hybrid context-aware systems that
allows for robust inference in uncertain knowledge bases.

Published in: Science
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RuleML2015: Compact representation of conditional probability for rule-based mobile context-aware systems

  1. 1. ual-logo Compact representation of conditional probability for rule-based mobile context-aware systems Szymon Bobek, Grzegorz J. Nalepa AGH University of Science and Technology RuleML 2015 5 August 2015, Berlin http://geist.agh.edu.pl SBK+GJN (AGH-UST) Indect 5 August 2015 1 / 28
  2. 2. ual-logo Outline I 1 Introduction 2 Previous works 3 Proposed solution 4 Probabilistic interpretation of XTT2 rules 5 Probabilistic reasoning in XTT2 models 6 Implementation 7 Summary and future work SBK+GJN (AGH-UST) Indect 5 August 2015 2 / 28
  3. 3. ual-logo Outline 1 Introduction 2 Previous works 3 Proposed solution 4 Probabilistic interpretation of XTT2 rules 5 Probabilistic reasoning in XTT2 models 6 Implementation 7 Summary and future work SBK+GJN (AGH-UST) Indect 5 August 2015 3 / 28
  4. 4. ual-logo Mobile context-aware systems (mCAS) • Where you are, who you are with, what resources are nearby (Schillit) • Any informaiton that can be used to characterize the situation of an entity (Dey) • Individuality, activity, location, time, relations (Zimmerman) • Set of variables that may be of interest for an agent and that influence its actions (Bolchini) Context • Artificial intelligence methods Aware • Intelligent homes, intelligent cars, robotics • Ambient intelligence, pervasive environments, ubiquitous computing • Mobile computing (location aware mobile applicaitons) • Intelligent software (contextual advertising, etc.) Systems SBK+GJN (AGH-UST) Indect 5 August 2015 4 / 28
  5. 5. ual-logo Mobile environment and uncertainty SBK+GJN (AGH-UST) Indect 5 August 2015 5 / 28
  6. 6. ual-logo Different types of uncertainty High-level classification 1 Uncertainty due to lack of knowledge – that comes from incomplete information both at the model level or if the information is not provided by the sensors, 2 Uncertainty due to lack of semantic precision – that may appear due to semantic mismatch in the notion of the information, 3 Uncertainty due to lack of machine precision – which covers machine sensors imprecision and ambiguity. SBK+GJN (AGH-UST) Indect 5 August 2015 6 / 28
  7. 7. ual-logo Different types of uncertainty High-level classification 1 Uncertainty due to lack of knowledge – that comes from incomplete information both at the model level or if the information is not provided by the sensors, 2 Uncertainty due to lack of semantic precision – that may appear due to semantic mismatch in the notion of the information, 3 Uncertainty due to lack of machine precision – which covers machine sensors imprecision and ambiguity. SBK+GJN (AGH-UST) Indect 5 August 2015 6 / 28
  8. 8. ual-logo Different types of uncertainty High-level classification 1 Uncertainty due to lack of knowledge – that comes from incomplete information both at the model level or if the information is not provided by the sensors, 2 Uncertainty due to lack of semantic precision – that may appear due to semantic mismatch in the notion of the information, 3 Uncertainty due to lack of machine precision – which covers machine sensors imprecision and ambiguity. SBK+GJN (AGH-UST) Indect 5 August 2015 6 / 28
  9. 9. ual-logo Different uncertainty modelling and handling mechanisms Uncertainty source Lack of knowledge Semantic imprecision Machine imprecision Implementation effort Probabilistic Q H G High Fuzzy Logic H Q Q Medium Certainty Factors Q H G Low Machine learning G H G High Table : Comparison of uncertainty handling mechanisms. Full circles represent full support, whereas empty circles represent low or no support. SBK+GJN (AGH-UST) Indect 5 August 2015 7 / 28
  10. 10. ual-logo Mobile environment and uncertainty Nature of mCAS The uncertainty of data is inevitable and it is dynamic mCAS are build usually as a user centric systems Intelligibility is very important as it may improve users trust to the system Mediation may help resolve ambiguity SBK+GJN (AGH-UST) Indect 5 August 2015 8 / 28
  11. 11. ual-logo Outline 1 Introduction 2 Previous works 3 Proposed solution 4 Probabilistic interpretation of XTT2 rules 5 Probabilistic reasoning in XTT2 models 6 Implementation 7 Summary and future work SBK+GJN (AGH-UST) Indect 5 August 2015 9 / 28
  12. 12. ual-logo CF approach Intelligibility Mediation Uncertainty Dynamics SBK+GJN (AGH-UST) Indect 5 August 2015 10 / 28
  13. 13. ual-logo CF approach Intelligibility Mediation Uncertainty Dynamics Rules SBK+GJN (AGH-UST) Indect 5 August 2015 10 / 28
  14. 14. ual-logo CF approach Intelligibility Mediation Uncertainty Dynamics Rules CF SBK+GJN (AGH-UST) Indect 5 August 2015 10 / 28
  15. 15. ual-logo CF approach Intelligibility Mediation Uncertainty Dynamics Rules CF Dynamic CF SBK+GJN (AGH-UST) Indect 5 August 2015 10 / 28
  16. 16. ual-logo CF approach Intelligibility Mediation Uncertainty Dynamics Rules CF Dynamic CF SBK+GJN (AGH-UST) Indect 5 August 2015 10 / 28
  17. 17. ual-logo CF approach Intelligibility Mediation Uncertainty Dynamics Rules CF Dynamic CF HeaRTDroid SBK+GJN (AGH-UST) Indect 5 August 2015 10 / 28
  18. 18. ual-logo CF approach Intelligibility Mediation Uncertainty Dynamics Rules CF Dynamic CF HeaRTDroid XTT2 rule representation SBK+GJN (AGH-UST) Indect 5 August 2015 10 / 28
  19. 19. ual-logo CF was not enough Assumed system state Weather forecast: sunny weather with certainty 0.3, cloudy with 0.1, and rainy with 0.6. How much user is interested inn particular POIs: places for eating – 60%, culture – 20%, entertainment – 80%, sightseeing – 20%. the user have been recently walking with certainty 0.8, running with 0.1 certainty and driving with certainty 0.1. SBK+GJN (AGH-UST) Indect 5 August 2015 11 / 28
  20. 20. ual-logo Uncertainties (?) weather (?) user profile (?) activity cf(conditions) cf(rule) cf(conclusion) 0.3 0.6 0.8 0.3 1 0.3 0.6 0.6 0.8 0.6 1 0.6 0.6 0.6 0.1 0.1 1 0.1 0.6 0.84 0.8 0.6 1 0.6 0.6 0.36 0.8 0.36 1 0.36 0.3 0.36 0.8 0.3 1 0.3 Assumed system state with zero certainty Weather forecast: sunny weather with certainty 0.3, cloudy with 0.1, and rainy with 0.6. How much user is interested inn particular POIs: places for eating – 60%, culture – 20%, entertainment – 80%, sightseeing – 20%. the user have been recently walking with certainty 0.8, running with 0.1 certainty and driving with certainty 0.1. SBK+GJN (AGH-UST) Indect 5 August 2015 12 / 28
  21. 21. ual-logo CF was not enough Assumed system state Weather forecast: sunny weather with certainty 0.0, cloudy with 0.0, and rainy with 0.0. How much user is interested inn particular POIs: places for eating – 60%, culture – 20%, entertainment – 80%, sightseeing – 20%. the user have been recently walking with certainty 0.8, running with 0.1 certainty and driving with certainty 0.1. SBK+GJN (AGH-UST) Indect 5 August 2015 13 / 28
  22. 22. ual-logo Uncertainties (?) weather (?) user profile (?) activity cf(conditions) cf(rule) cf(conclusion) 0.0 0.6 0.8 0.0 1 0.0 0.0 0.6 0.8 0.0 1 0.0 0.0 0.6 0.1 0.0 1 0.0 0.0 0.84 0.8 0.0 1 0.0 0.0 0.36 0.8 0.0 1 0.0 0.0 0.36 0.8 0.0 1 0.0 Assumed system state with zero certainty Weather forecast: sunny weather with certainty 0.0, cloudy with 0.0, and rainy with 0.0. How much user is interested inn particular POIs: places for eating – 60%, culture – 20%, entertainment – 80%, sightseeing – 20%. the user have been recently walking with certainty 0.8, running with 0.1 certainty and driving with certainty 0.1. SBK+GJN (AGH-UST) Indect 5 August 2015 14 / 28
  23. 23. ual-logo Outline 1 Introduction 2 Previous works 3 Proposed solution 4 Probabilistic interpretation of XTT2 rules 5 Probabilistic reasoning in XTT2 models 6 Implementation 7 Summary and future work SBK+GJN (AGH-UST) Indect 5 August 2015 15 / 28
  24. 24. ual-logo Use Bayesian Networks as ”backup” representation Solution XTT2 models can be immediately translated into Bayesian networks HeaRTDroid stores historical states, which can be used to train Bayesian networks (?) location (?) daytime (?) today (->) action = home = outside = work = work = outside = home = home = home = outside = outside = morning = morning = dayatime = afternoon = afternoon = evening = night = any = evening = night = workday = workday = workday = workday = workday = any = any = weekend = any = any := leaving_home := travelling_work := working := leaving_work := travelling_home := resting := sleeping := resting := entertaining := travelling_home Table id: tab_4 - Actions (?) action (?) transportation (->) {application} = leaving_home ∈ {leaving_work,leaving_home} ∈ {travelling_home,travelling_work} ∈ {travelling_home,travelling_work} ∈ {resting,entertaining} = working = sleeping ∈ {resting,entertaining} = idle ∈ {walking,running} ∈ {driving,cycling} ∈ {bus,train} ∈ {running,cycling} = any = idle ∈ {driving,bus,train} := {news,weather} := {clock,navigation} := navigation := {news,clock} := {sport_tracker,weather} := {calendar,mail} := clock := trip_advisor Table id: tab_5 - Applications (?) action (->) profile ∈ {travelling_home,travelling_work,leaving_home,leaving_work} ∈ {working,resting,entertaining} = sleeping := loud := vibrations := offline Table id: tab_6 - Profile 1 2 3 4 5 6 7 8 1 2 3 1 2 3 4 5 6 7 8 9 10 SBK+GJN (AGH-UST) Indect 5 August 2015 16 / 28
  25. 25. ual-logo Use Bayesian Networks as ”backup” representation Solution XTT2 models can be immediately translated into Bayesian networks HeaRTDroid stores historical states, which can be used to train Bayesian networks SBK+GJN (AGH-UST) Indect 5 August 2015 16 / 28
  26. 26. ual-logo Use Bayesian Networks as ”backup” representation Solution XTT2 models can be immediately translated into Bayesian networks HeaRTDroid stores historical states, which can be used to train Bayesian networks XTT2 Model Manager Reasoning Engine Working Memory -n -n+1 -1 0 . . . Contex Providers HeaRTDroid States SBK+GJN (AGH-UST) Indect 5 August 2015 16 / 28
  27. 27. ual-logo Use Bayesian Networks as ”backup” representation Solution XTT2 models can be immediately translated into Bayesian networks HeaRTDroid stores historical states, which can be used to train Bayesian networks Intelligibility Mediation Uncertainty Dynamics Rules CF Dynamic CF HeaRTDroid XTT2 rule representation SBK+GJN (AGH-UST) Indect 5 August 2015 16 / 28
  28. 28. ual-logo Use Bayesian Networks as ”backup” representation Solution XTT2 models can be immediately translated into Bayesian networks HeaRTDroid stores historical states, which can be used to train Bayesian networks Intelligibility Mediation Uncertainty Dynamics Rules CF Dynamic CF HeaRTDroid Dual representation SBK+GJN (AGH-UST) Indect 5 August 2015 16 / 28
  29. 29. ual-logo Outline 1 Introduction 2 Previous works 3 Proposed solution 4 Probabilistic interpretation of XTT2 rules 5 Probabilistic reasoning in XTT2 models 6 Implementation 7 Summary and future work SBK+GJN (AGH-UST) Indect 5 August 2015 17 / 28
  30. 30. ual-logo ALSV(FD) logic XTT2 rule in ALSV(FD) logic (Ai ∝ di ) ∧ (Aj ∝ dj ) ∧ . . . (Am ∝ Vm) ∧ (An ∝ Vn) −→ RHS Syntax Interpretation Relation Ai = di value of Ai is pre- cisely defined as di eq Ai ∈ Vi value of Ai is in Vi in Ai = di shorthand for Ai ∈ (Di {di }) neq Ai ∈ Vi shorthand for Ai ∈ (Di Vi ) notin Table : Formula for simple attributes Syntax Interpretation Relation Ai = Vi Ai equal Vi eq Ai = Vi Ai does not equal Vi neq Ai ⊆ Vi Ai is a subset Vi subset Ai ⊇ Vi Ai is a superset Vi supset Ai ∼ Vi Ai has non-empty intersection with Vi sim Ai ∼ Vi Ai has empty in- tersection with Vi notsim Table : Formula for generalized attributes SBK+GJN (AGH-UST) Indect 5 August 2015 18 / 28
  31. 31. ual-logo Probabilistic interpretation of ALSV(FD) rule XTT2 rule as conditional probability (Ai ∝ di ) ∧ (Aj ∝ dj ) ∧ . . . (An ∝ dn) −→ (Ad = dd ) (Ai ∝ di ) ∧ (Aj ∝ dj ) ∧ . . . (An ∝ dn) −→ Ag = {v1, v2, . . . vn} P(DEC | COND) Interpretation Every rule is represented by a pair r, p , where r is an XTT2 rule and p ∈ [0; 1] defines a certainty of a rule given its preconditions. simple attributes p : P (Ad | Ai , Aj . . . , An) generalised attributes P(Ag = {v1, v2, . . . vn} | Ai , Aj , . . . , An) = P(v1 | Ai , Aj , . . . , An)· P(v2 | Ai , Aj , . . . , An)· . . . P(vn | Ai , Aj , . . . , An) SBK+GJN (AGH-UST) Indect 5 August 2015 19 / 28
  32. 32. ual-logo Probabilistic interpretation of XTT2 models (?) location (?) daytime (?) today (->) action = home = outside = work = work = outside = home = home = home = outside = outside = morning = morning = dayatime = afternoon = afternoon = evening = night = any = evening = night = workday = workday = workday = workday = workday = any = any = weekend = any = any := leaving_home := travelling_work := working := leaving_work := travelling_home := resting := sleeping := resting := entertaining := travelling_home Table id: tab_4 - Actions (?) action (?) transportation (->) {application} = leaving_home ∈ {leaving_work,leaving_home} ∈ {travelling_home,travelling_work} ∈ {travelling_home,travelling_work} ∈ {resting,entertaining} = working = sleeping ∈ {resting,entertaining} = idle ∈ {walking,running} ∈ {driving,cycling} ∈ {bus,train} ∈ {running,cycling} = any = idle ∈ {driving,bus,train} := {news,weather} := {clock,navigation} := navigation := {news,clock} := {sport_tracker,weather} := {calendar,mail} := clock := trip_advisor Table id: tab_5 - Applications (?) action (->) profile ∈ {travelling_home,travelling_work,leaving_home,leaving_work} ∈ {working,resting,entertaining} = sleeping := loud := vibrations := offline Table id: tab_6 - Profile 1 2 3 4 5 6 7 8 1 2 3 1 2 3 4 5 6 7 8 9 10 SBK+GJN (AGH-UST) Indect 5 August 2015 20 / 28
  33. 33. ual-logo Probabilistic interpretation of XTT2 models SBK+GJN (AGH-UST) Indect 5 August 2015 20 / 28
  34. 34. ual-logo Outline 1 Introduction 2 Previous works 3 Proposed solution 4 Probabilistic interpretation of XTT2 rules 5 Probabilistic reasoning in XTT2 models 6 Implementation 7 Summary and future work SBK+GJN (AGH-UST) Indect 5 August 2015 21 / 28
  35. 35. ual-logo Hybrid reasoning Data: E – the set of all known attributes values A – the set of attributes which values are to be found Result: V – values for attributes from the set A 1 Create a stack of tables T that needs to be processed to obtain V ; 2 while not empty T do 3 t = pop(T); 4 Identify schema (COND, DEC) of table t; 5 if ∀c ∈ COND, Val(c) ∈ E then 6 Execute table t; 7 ∀a ∈ DEC ∩ A : add Val(a) to E and V ; 8 else 9 Run probabilistic reasoning to obtain P(a)∀a ∈ DEC; 10 Select rule rmax , pmax such that: ∀ r, p ∈ t : p ≤ pmax ; 11 if pmax ≥ then 12 execute rule r; 13 ∀a ∈ DEC ∩ A : add Val(a) to E and V ; 14 else 15 ∀a ∈ DEC ∩ A : add P(a) to E and V ; 16 t = pop(T); 17 Identify schema (COND, DEC) of table t; 18 goto 9 19 end 20 end 21 end 22 return V ; Inference modes 1 Deterministic inference 2 Probabilistic inference 3 Hybrid inference SBK+GJN (AGH-UST) Indect 5 August 2015 22 / 28
  36. 36. ual-logo Hybrid reasoning Assumptions Value of attribute G is needed Only value of attribute C is known Attribute F is set to be in/out A B C D B D E E F E G SBK+GJN (AGH-UST) Indect 5 August 2015 23 / 28
  37. 37. ual-logo Hybrid reasoning Assumptions Value of attribute G is needed Only value of attribute C is known Attribute F is set to be in/out A B C D B D E E F E G SBK+GJN (AGH-UST) Indect 5 August 2015 23 / 28
  38. 38. ual-logo Hybrid reasoning Assumptions Value of attribute G is needed Only value of attribute C is known Attribute F is set to be in/out A B C D B D E E F E G SBK+GJN (AGH-UST) Indect 5 August 2015 23 / 28
  39. 39. ual-logo Hybrid reasoning Assumptions Value of attribute G is needed Only value of attribute C is known Attribute F is set to be in/out A B C D B D E E F E G SBK+GJN (AGH-UST) Indect 5 August 2015 23 / 28
  40. 40. ual-logo Hybrid reasoning Assumptions Value of attribute G is needed Only value of attribute C is known Attribute F is set to be in/out A B C D B D E E F E G SBK+GJN (AGH-UST) Indect 5 August 2015 23 / 28
  41. 41. ual-logo Hybrid reasoning Assumptions Value of attribute G is needed Only value of attribute C is known Attribute F is set to be in/out A B C D B D E E F E G SBK+GJN (AGH-UST) Indect 5 August 2015 23 / 28
  42. 42. ual-logo Hybrid reasoning Assumptions Value of attribute G is needed Only value of attribute C is known Attribute F is set to be in/out (->) B 1 2 3 4 5 No. (?) A B = a B = b B = c B = d B = e P(B=d | evidence) = 0.2 P(B=c | evidence) = 0.2 P(B=b | evidence) = 0.2 P(B=a | evidence) = 0.2 P(B=e | evidence) = 0.2 SBK+GJN (AGH-UST) Indect 5 August 2015 23 / 28
  43. 43. ual-logo Hybrid reasoning Assumptions Value of attribute G is needed Only value of attribute C is known Attribute F is set to be in/out A B C D B D E E F E G SBK+GJN (AGH-UST) Indect 5 August 2015 23 / 28
  44. 44. ual-logo Hybrid reasoning Assumptions Value of attribute G is needed Only value of attribute C is known Attribute F is set to be in/out (->) E(?) B 1 2 3 4 5 No. (?) D E = a E = b E = c E = d E = e P(E=d | evidence) = 0.6 P(E=c | evidence) = 0.1 P(E=b | evidence) = 0.0 P(E=a | evidence) = 0.1 P(E=e | evidence) = 0.2 SBK+GJN (AGH-UST) Indect 5 August 2015 23 / 28
  45. 45. ual-logo Hybrid reasoning Assumptions Value of attribute G is needed Only value of attribute C is known Attribute F is set to be in/out A B C D B D E E F E G SBK+GJN (AGH-UST) Indect 5 August 2015 23 / 28
  46. 46. ual-logo Hybrid reasoning Assumptions Value of attribute G is needed Only value of attribute C is known Attribute F is set to be in/out A B C D B D E E F E G SBK+GJN (AGH-UST) Indect 5 August 2015 23 / 28
  47. 47. ual-logo Outline 1 Introduction 2 Previous works 3 Proposed solution 4 Probabilistic interpretation of XTT2 rules 5 Probabilistic reasoning in XTT2 models 6 Implementation 7 Summary and future work SBK+GJN (AGH-UST) Indect 5 August 2015 24 / 28
  48. 48. ual-logo Prototype Implementation Components HeaRTDroid for deterministic reasoning and training set preparation Translator XTT2 to BN WEKA Prototype reasoner that combines HeaRTDroid and WEKA HeaRTDroidWeka XTT2 Model Translator XTT2 to BN Hybrid Reasoner States SBK+GJN (AGH-UST) Indect 5 August 2015 25 / 28
  49. 49. ual-logo Outline 1 Introduction 2 Previous works 3 Proposed solution 4 Probabilistic interpretation of XTT2 rules 5 Probabilistic reasoning in XTT2 models 6 Implementation 7 Summary and future work SBK+GJN (AGH-UST) Indect 5 August 2015 26 / 28
  50. 50. ual-logo Summary and future work Summary We provided probabilistic interpretation of XTT2 knowledge representation We proposed a hybrid inference algorithm We implemented prototype reasoner that binds HeaRTDroid, XTT2 and Bayesian network representation of XTT2 into one hybrid reasoner Future works Make the reasoner part of HeaRTDroid Evaluate the hybrid reasoning on the real-life use case SBK+GJN (AGH-UST) Indect 5 August 2015 27 / 28
  51. 51. ual-logo Thank you for your attention! Do you have any questions? RuleML 2015 http://geist.agh.edu.pl SBK+GJN (AGH-UST) Indect 5 August 2015 28 / 28

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