The dynamic nature of Intelligent Environments (IE’s) present a challenging problem when attempting to model or learn a model of such environments. By their very nature, IE’s are infused with complexity, unreliability and uncertainty due to a combination of sensor noise and the human element. As a result of this, the quantity, type and availability of data to model such environments is a major issue. Each situation is contextually different and constantly changing. To model each application, training data must be gained that is within the same feature space and has the same distribution, however this is often highly costly and time consuming. There can even be occurrences were a complete lack of target labelled data occurs. It is within these situations that our study is focussed. Within this research we propose a framework to dynamically model IE’s through the use of data sets from differing feature spaces and domains. The framework is constructed using a Fuzzy Transfer Learning process. This presentation is taken from the winning entry at the Leicester British Computer Society Postgraduate Research Competition 2012.

1. Fuzzy Transfer Learning for Intelligent Environments
Jethro Shell, Student Member, IEEE
March 28, 2012
Centre for Computational
Intelligence
De Montfort University
Leicester, United Kingdom
Email: JethroS@dmu.ac.uk

2. Intelligent Environments
What is an Intelligent Environment?
Applications that use sensors to model and make decisions about their
surroundings.
Constructed using a large number of varying types of sensor ranging from
temperature sensors to Passive Infra-red (PIR) Sensor.
Multiple applications: Environmental[3], Healthcare[6], Domestic[4], Military[1],
etc.
What are the issues?
IE’s require models to make decisions but each environment is
different:contextually, spatially and temporally.
Sensor construction and application domains produce uncertain and dynamic
environments.
Collecting of data is expensive in terms of time, resource and analysis.
Majority of learning processes require source and target data to come from the
same feature and domain space.

3. Simple Intelligent Environment Example
Flat A Flat B
*⊕ ⊗ * *
*⊕
Bedroom
Bathroom
⊗ Lounge
⊗
Lounge / Kitchen
*
⊗ ⊗
*⊕
* ⊗
*
Bedroom
Bathroom Hall Kitchen
⊗ ⊗
*
⊕
- Temperature Sensor
- Occupancy Sensor
⊗ - Heating Activation Sensor
A simple Ambient Intelligent Assisted Living environment.
Flat A is occupied by a single individual with data (Occupancy, Temperature,
and Heating Activation) monitored during March.
Flat B is occupied by a couple with data (Occupancy and Temperature)
monitored during October and November.

4. Fuzzy Transfer Learning framework
Propose a Fuzzy Transfer Learning (FuzzyTL) framework.
Addresses the issue of the lack of knowledge in target contexts by using a
combination of fuzzy logic and transfer learning.
Fuzzy logic allows for the incorporation of approximation and uncertainty within
the environment.
Transfer Learning is a humanistic style approach using knowledge from one task
to assist another.
Source Task
Input Output
Fuzzy Rules
Ad Hoc Transferable
Labelled Data Data Driven FIS
Learning
Fuzzy Sets
Target Task
Transferable
FIS
Input Output
Unlabelled Predictive
Processing Value
Data
Context
Adapation

5. Fuzzy Ad Hoc Data Driven System
Fuzzy Ad Hoc Data Driven Learning
Automated process for producing rules from numerical labelled data.
Computationally swift and low resource use.
Construction based upon a Fuzzy Inference System (FIS).
Characterised by short time requirement of iterative processes for rule
production[2].
Outline of FIS
Fuzzy Sets
Data Input Defuzzification Data Output
Fuzzy Rules
m(t) m(o) m(h)
VL L M H VH VL L M H VH VL L M H VH
1.0 1.0 1.0
0.0 t 0.0 o 0.0 h
Temperature (t) Occupancy (o) Heating (h)
IF Temperature is Low AND Occupancy is High THEN Heating is High

6. Fuzzy Ad Hoc Data Driven System
The ad hoc system is made up of four stages:
Construction of fuzzy sets
Basis of the process is to construct fuzzy sets and rules from numerical data.
Fuzzy sets are created by segmenting each variable domain equally into fuzzy
regions by 2N + 1.
Each variable can contain differing quantities of regions.
Min and max values of the variable are used to define the domain.
m(t)
VL L M H VH
1.0
0.0 Temperature (t)
tmin tmax

7. Fuzzy Ad Hoc Data Driven System
Construction of fuzzy rules
Each instance of data is used to construct a rule.
The maximum degree of membership of each feature is used to form the rule.
This is carried out for each input and output feature.
m(t)
VL L M H VH
1.0
0.0 t1 t2 Temperature (t)
tmin tmax
m(o)
VL L M H VH
1.0
0.0 o1 o2 Occupancy (o)
omin omax
m(h)
VL L M H VH
1.0
0.0 Heating (h)
h1 h2
hmin hmax
(t1 , o1 , h1 ) ⇒ [t1 (0.65 in VL), o1 (0.7 in M); h1 (0.55 in L)] ⇒ Rule 1
(t2 , o2 , h2 ) ⇒ [t2 (0.8 in H), o2 (1.0 in M); h2 (0.8 in H)] ⇒ Rule 2

8. Fuzzy Ad Hoc Data Driven System
Rule base reduction
The initial production of rules is equal to the size of the dataset.
Conflicts and additional complexity are produced as a result.
Predominantly this is unmanageable and requires reduction.
To reduce the rule base a degree of the maximum product (d) is assigned to
each rule.
Maximum product of membership
This can be represented as:
n m
d= Pij (xi )
i =1 j=1
n is the number of partitions.
m is the number of rules.
P representing the partitions.
x equals inputs.
Each rule with equal antecedent conditions are compared.
Those with the greatest degree remain within the rule base.

9. Fuzzy Ad Hoc Data Driven System
Defuzzification
Mapping of crisp input value to crisp output value.
Many methods are available to achieve defuzzification.
Within this implementation a centroid method was used.
Centroid defuzzification
This can be represented as:
K i ¯i
i =1 mo i y
z= K i
i =1 mo i
mo i denotes the degree of output using a product t-norm.
y i is the centre of the fuzzy region.
¯
K is number of fuzzy rules in the rule base.

10. Transfer Process
Transfer Learning utilises a humanistic style of knowledge exchange.
Crisp unlabelled data is taken from target task as an input.
Transferable Fuzzy Inference System (FIS) provides the basis for the
knowledge base and decision making.
Previously constructed fuzzy rules and fuzzy sets are used as the basis of
the model.
Classification output is produced using previous related knowledge.

11. Context Adaptation
To create an effective classification system, FIS needs to be adapted.
Domains of the fuzzy sets are adapted in order to cope with the
contextual changes.
Assessment is made of the whole target data set to gain new minimum
and maximum of each feature.
Each feature fuzzy domain is separately adapted taking into account the
minimum and maximum values.
The domain of each feature is compressed, expanded or shifted relating to
the new values.
m(t) m(t)
VL L M H VH VL L M H VH
1.0 1.0
0.0 Temperature (t) 0.0 Temperature (t)
old new old new
tmin tmax tmin tmin tmax tmax
An example of temperature fuzzy sets with adaptive shift.

12. Application to an intelligent environment
FuzzyTL was applied to Intel Berkeley Research Laboratory[5] sensor
network.
Wireless Sensor Network (WSN) consists of temperature, humidity, light
and residual power data sensors.
Spatial and temporal contextual changes were tested using differing
sensors on different days.
Sensors 7, 9, 12 , 24 , 34 , 42 and 51 where examined across seven days
using time and light to predict temperature.

13. Application to an intelligent environment
Baseline measurement of predictive values using comparison of the same sensor.
Sensor to Sensor Classification and Predictive NRMSE of Temperature
1
Sensor to Sensor Classification and Predictive NRMSE of Temperature
0.8
NRMSE of Comparison of Temperature Values (˚C)
0.6
0.4
0.2
0
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50
Sensor Number

14. Application to an intelligent environment
1
Comparison of Spatial Temporal Value and NRMSE of Temperature
0.8
NRMSE of Comparison of Temperature Values (˚C)
0.6
0.4
0.2
0
0 10 20 30 40 50
Spatial Temporal Combined Value (ST)
Spatial Temporal Combined value is represented as:
ST = (xi − yi )2 + (m + 1)
where x and y are points in two dimensional space in metres, m is number of days
separating the feature domains.

15. Conclusion and Future Work
Conclusion
Constructed a novel fuzzy transfer learning framework applicable to the issues
within Intelligent Environments.
Addresses the issue of modelling environments in contextually different
situations.
Used combined fuzzy and adaptive approach that absorbs uncertainty and
dynamic nature.
Utilised transfer of knowledge to enhance the learning of target tasks from
source tasks.
Applied methodology to a real world dataset to gain results.
Future Work
Greater work on impact of contextual information.
Further investigation into relatedness of features.
The establishing of limited / reduced information learning.
Increased diversity of applications, currently applying framework to task
recognition using eye gaze technology.

16. Bibliography
Links
Fuzzy Logic:An Introduction http://tinyurl.com/fuzzylogicvideo
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