Smart Home Technologies
Data Mining and Prediction
Objectives of Data Mining and
Prediction



Large amounts of sensor data have to
be “interpreted” to acquire knowledge
about tasks that occur in the
environment
Patterns in the data can be used to
predict future events
Knowledge of tasks facilitates the
automation of task components to
improve the inhabitants’ experience
Data Mining and Prediction

Data Mining attempts to extract patterns
from the available data



Associative patterns
What data attributes occur together ?
Classification
What indicates a given category ?
Temporal patterns
What sequences of events occur frequently ?
Example Patterns

Associative pattern
When Bob is in the living room he likes to watch TV
and eat popcorn with the light turned off.

Classification
Action movie fans like to watch Terminator, drink
beer, and have pizza.

Sequential patterns
After coming out of the bedroom in the morning,
Bob turns off the bedroom lights, then goes to the
kitchen where he makes coffee, and then leaves the
house.
Data Mining and Prediction

Prediction attempts to form patterns that
permit it to predict the next event(s)
given the available input data.

Deterministic predictions
If Bob leaves the bedroom before 7:00 am on a
workday, then he will make coffee in the kitchen.

Probabilistic sequence models
If Bob turns on the TV in the evening then he will
80% of the time go to the kitchen to make popcorn.
Objective of Prediction in
Intelligent Environments




Anticipate inhabitant actions
Detect unusual occurrences (anomalies)
Predict the right course of actions
Provide information for decision making

Automate repetitive tasks
e.g.: prepare coffee in the morning, turn on lights

Eliminate unnecessary steps, improve sequences
e.g.: determine if will likely rain based on weather forecast
and external sensors to decide if to water the lawn.
What to Predict

Behavior of the Inhabitants




Location
Tasks / goals
Actions
Behavior of the Environment


Device behavior (e.g. heating, AC)
Interactions
Example: Location Prediction



Where will Bob go next?
Locationt+1 = f(x)
Input data x:



Locationt, Locationt-1, …
Time, date, day of the week
Sensor data
Example: Location Prediction
Time
Date
Day
Locationt
Locationt+1
6:30
02/25
Monday
Bedroom
Bathroom
7:00
02/25
Monday
Bathroom
Kitchen
7:30
02/25
Monday
Kitchen
Garage
17:30
02/25
Monday
Garage
Kitchen
18:00
02/25
Monday
Kitchen
Bedroom
18:10
02/25
Monday
Bedroom
Living room
22:00
02/25
Monday
Living room Bathroom
22:10
02/25
Monday
Bathroom
Bedroom
6:30
02/26
Tuesday
Bedroom
Bathroom
Example: Location Prediction

Learned pattern

If Day = Monday…Friday
& Time > 0600
& Time < 0700
& Locationt = Bedroom
Then Locationt+1 = Bathroom
Prediction Techniques

Classification-Based Approaches





Nearest Neighbor
Neural Networks
Bayesian Classifiers
Decision Trees
Sequential Behavior Modeling


Hidden Markov Models
Temporal Belief Networks
Classification-Based Prediction

Problem

Input: State of the environment

Attributes of the current state
inhabitant location, device status, etc.


Output: Concept description


Attributes of previous states
Concept indicates next event
Prediction has to be applicable to future
examples
Instance-Based Prediction:
Nearest Neighbor


Use previous instances as a model for future
instances
Prediction for the current instance is chosen
as the classification of the most similar
previously observed instance.


Instances with correct classifications (predictions)
(xi,f(xi)) are stored
Given a new instance xq, the prediction is derived
as the one of the most similar instance xk:
f(xq) = f(xk)
Example: Location Prediction
Time
Date
Day
Locationt
Locationt+1
6:30
02/25
Monday
Bedroom
Bathroom
7:00
02/25
Monday
Bathroom
Kitchen
7:30
02/25
Monday
Kitchen
Garage
17:30
02/25
Monday
Garage
Kitchen
18:00
02/25
Monday
Kitchen
Bedroom
18:10
02/25
Monday
Bedroom
Living room
22:00
02/25
Monday
Living room Bathroom
22:10
02/25
Monday
Bathroom
Bedroom
6:30
02/26
Tuesday
Bedroom
Bathroom
Nearest Neighbor Example:
Inhabitant Location

Training Instances (with concept):
((Bedroom, 6:30), Bathroom), ((Bathroom, 7:00), Kitchen),
((Kitchen, 7:30), Garage), ((Garage, 17:30), Kitchen), …

Similarity Metric:
d((location1, time1), (location2, time2)) =
1000*(location1  location2) + | time1 – time2 |

Query Instance:
xq = (Bedroom, 6:20)

Nearest Neighbor:
xk = (Bedroom, 6:30)

Prediction f(xk):
Bathroom
d(xk, xq) = 10
Nearest Neighbor


Training instances and similarity metric form
regions where a concept (prediction) applies:
Uncertain information and incorrect training
instances lead to incorrect classifications
k-Nearest Neighbor

Instead of using the most similar instance,
use the average of the k most similar
instances


Given query xq, estimate concept (prediction)
using majority of k nearest neighbors
Or, estimate concept by establishing the concept
with the highest sum of inverse distances:
1
w f  ik , f
( x i )  f d ( xi , x q )
k-Nearest Neighbor Example

TV viewing preferences
Time
19:30
21:00
19:00
12:00
20:00
Date
02/25
02/25
02/26
02/27
02/27
Day
Thursday
Thursday
Friday
Saturday
Saturday
Channel
27
33
11
21
8
Genre
Reality
News
News
Action
News
Title
Cops
News
News
Terminator I
News
…
…
…
…
…
…
Time
13:30
22:00
20:00
22:00
Date
03/20
03/20
03/21
03/22
Day
Sunday
Sunday
Monday
Tuesday
Channel
13
4
8
13
Genre
Reality
News
News
Documentary
Title
Antiques Roadshow
News
60 Minutes
Nova
…
…
…
…
…
…

Distance Function?


What are the important attributes ?
How can they be compared ?
k-Nearest Neighbor Example

Distance function example:




Most important matching attribute: Show name
Second most important attribute: Time
Third most important attribute: Genre
Fourth most important attribute: Channel
d ( xi , x j ) 
Time
16:30
21:00
20:00
…

# (titlei ,k : titlei ,k  title j ,l )
0.5 * (# showsi  # shows j )
Date
04/20
04/21
04/22
…
Day
Wednesday
Thursday
Friday
…
Channel
13
33
8
…
 0.5 *
# (timesi ,k :| timesi ,k  times j ,l | 1hr )
0.5 * (# showsi  # shows j )
Genre
Documentary
News
News
…
Does he/she like to watch Nova ?
Title
WW II Planes
News
60 Minutes
…
 ...
Nearest Neighbor

Advantages




Fast training (just store instances)
Complex target functions
No loss of information
Problems



Slow at query time (have to evaluate all instances)
Sensitive to correct choice of similarity metric
Easily fooled by irrelevant attributes
Decision Trees

Use training instances to build a sequence of
evaluations that permits to determine the
correct category (prediction)
If Bob is in the Bedroom then
if the time is between 6:00 and 7:00 then
Bob will go to the Bathroom
else


Sequence of evaluations are represented as a
tree where leaves are labeled with the
category
Decision Tree Induction

Algorithm (main loop)
1.
2.
3.
4.
5.
A = best attribute for next node
Assign A as attribute for node
For each value of A, create descendant
node
Sort training examples to descendants
If training examples perfectly classified,
then Stop, else iterate over descendants
Decision Tree Induction

Best attribute based on informationtheoretic concept of entropy

Choose the attribute that reduces the
entropy (~uncertainty) most
A1
v1
?
A2
v2
v1
v2
?
B
K
Bathroom (25) Bathroom (25)
Kitchen (25)
Kitchen (25)
Bathroom (50) Bathroom (0)
Kitchen (0)
Kitchen (50)
Decision Tree Example:
Inhabitant Location
Day
M…F
Sat
Time > 6:00
yes
no
Time < 7:00
yes
Locationt
…
Bathroom
Bedroom
no
Locationt
…
Living Room
Bedroom
Sun
Example: Location Prediction
Time
Date
Day
Locationt
Locationt+1
6:30
02/25
Monday
Bedroom
Bathroom
7:00
02/25
Monday
Bathroom
Kitchen
7:30
02/25
Monday
Kitchen
Garage
17:30
02/25
Monday
Garage
Kitchen
18:00
02/25
Monday
Kitchen
Bedroom
18:10
02/25
Monday
Bedroom
Living room
22:00
02/25
Monday
Living room Bathroom
22:10
02/25
Monday
Bathroom
Bedroom
6:30
02/26
Tuesday
Bedroom
Bathroom
Decision Trees

Advantages




Understandable rules
Fast learning and prediction
Lower memory requirements
Problems



Replication problem (each category requires
multiple branches)
Limited rule representation (attributes are
assumed to be locally independent)
Numeric attributes can lead to large branching
factors
Artificial Neural Networks


Use a numeric function to calculate the
correct category. The function is learned from
the repeated presentation of the set of
training instances where each attribute value
is translated into a number.
Neural networks are motivated by the
functioning of neurons in the brain.

Functions are computed in a distributed fashion by
a large number of simple computational units
Neural Networks
Computer vs. Human Brain
Computer
Human Brain
Computational units
1 CPU, 108 gates
1011 neurons
Storage units
1010 bits RAM,
1012 bits disk
1011 neurons,
1014 synapses
Cycle time
10-9 sec
10-3 sec
Bandwidth
109 bits/sec
1014 bits/sec
Neuron updates / sec 106
1014
Artificial Neurons

Artificial neurons are a much simplified
computational model of neurons


Output:
a
i
 g (  w j a j  wth)
j
A function is learned by adjusting the weights wj
Artificial Neuron

Activation functions
Perceptrons

Perceptrons use a single unit with a threshold
function to distinguish two categories
Perceptron Learning

Weights are updated based on the treaining
instances (x(i), f(x(i))) presented.
w j  w j   ( f ( x )  o) x j
(i )


(i )
Adjusts the weights in order to move the output
closer to the desired target concept.
Learning rate  determines how fast to adjust the
weights (too slow will require many training steps,
too fast will prevent learning).
Limitation of Perceptrons

Learns only linearly-separable functions

E.g. XOR can not be learned
Feed forward Networks with
Sigmoid Units

Networks of units with sigmoid activation
functions can learn arbitrary functions
Feed forward Networks with
Sigmoid Units

General Networks permit arbitrary statebased categories (predictions) to be learned
Learning in Multi-Layer Networks:
Error Back-Propagation



As in Perceptrons, differences between the output of
the network and the target concept are propagated
back to the input weights.
Output errors for hidden units are computed based
on the propagated errors for the inputs of the output
units.
Weight updates correspond to gradient descent on
the output error function in weight space.
w j,l  w j,l  
( j (
f (x
w
(i )
k
j ,l
2
)  ok )
Neural Network Examples

Prediction





Predict steering commands in cars
Modeling of device behavior
Face and object recognition
Pose estimation
Decision and Control



Heating and AC control
Light control
Automated vehicles
Neural Network Example:
Prediction of Lighting


University of Colorado Adaptive Home [DLRM94]
Neural network learns to predict the light level after a
set of lights are changed
 Input:




The current light device levels (7 inputs)
The current light sensor levels (4 inputs)
The new light device levels (7 inputs)
Output:

The new light sensor levels (4 outputs)
[DLRM94] Dodier, R. H., Lukianow, D., Ries, J., & Mozer, M. C. (1994).
A comparison of neural net and conventional techniques for lighting control.
Applied Mathematics and Computer Science, 4, 447-462.
Neural Networks

Advantages



General purpose learner (can learn arbitrary
categories)
Fast prediction
Problems



All inputs have to be translated into numeric
inputs
Slow training
Learning might result in a local optimum
Bayes Classifier

Use Bayesian probabilities to determine the
most likely next event for the given instance
given all the training data.
f ( x)  arg max P( f | x)
f F
P( x | f ) P( f )
P( f | x ) 
P( x )

Conditional probabilities are determined from the
training data.
Naive Bayes Classifier

Bayes classifier required estimating P(x|f) for
all x and f by counting occurrences in the
training data.


Generally too complex for large systems
Naive Bayes classifier assumes that
attributes are statistically independent
x  (a1 , a2 ,, an )
P(a1 , a2 ,..., an | f )   P(ai | f )
i
f ( x )  arg max P( f ) P(ai | f )
f F
i
Bayes Classifier

Advantages




Yields optimal prediction (given the assumptions)
Can handle discrete or numeric attribute values
Naive Bayes classifier easy to compute
Problems


Optimal Bayes classifier computationally
intractable
Naive Bayes assumption usually violated
Bayesian Networks

Bayesian networks explicitly represent the
dependence and independence of various
attributes.




Attributes are modeled as nodes in a network and
links represent conditional probabilities.
Network forms a causal model of the attributes
Prediction can be included as an additional
node.
Probabilities in Bayesian networks can be
calculated efficiently using analytical or
statistical inference techniques.
Bayesian Networks Example:
Location Prediction

All state attributes are represented as nodes.

Nodes can include attributes that are not
observable.
Day
Time
Get ready
Room
P(Bathroom | R, Gr)
Gr
Prediction
R Bedroom
Kitchen
True
0.8
0.1
False
0.2
0.0
Bayesian Networks

Advantages





Efficient inference mechanism
Readable structure
For many problems relatively easy to design by
hand
Mechanisms for learning network structure exist
Problems


Building network automatically is complex
Does not handle sequence information
Sequential Behavior Prediction

Problem

Input: A sequence of states or events

States can be represented by their attributes
inhabitant location, device status, etc.

Events can be raw observations
Sensor readings, inhabitant input, etc.


Output: Predicted next event
Model of behavior has to be built based on
past instances and be usable for future
predictions.
Sequence Prediction Techniques

String matching algorithms




Deterministic best match
Probabilistic matching
Markov Models

Markov Chains

Hidden Markov Models
Dynamic Belief Networks
String-Based Prediction

Use the string of previous events or states to
find a part that matches the current history.


Prediction is either the event that followed the
best (longest) matching string or the most likely
event to follow strings partially matching the
history.
Issues:


How to determine quality of match ?
How can such a predictor be represented
efficiently if the previous event string is long ?
Example System: IPAM


Predict UNIX commands issued by a user
Calculate p(xt,xt-1) based on frequency




[DH98]
Update current p(Predicted, xt-1) by 
Update current p(Observed, xt-1) by 1- 
Weight more recent events more heavily
Data


77 users, 2-6 months, >168,000 commands
Accuracy less than 40% for one guess, but better
than Naïve Bayes Classifier
[DH98] B. D. Davison and H. Hirsh. Probabilistic Online Action Prediction. Intelligent
Environments: Papers from the AAAI 1998 Spring Symposium, Technical Report
SS-98-02, pp. 148-154: AAAI Press.
Example System: ONISI

[GP00]
Look for historical state/action sequences that
match immediate history and determine the
quality of the predictions from these sequences

In state s at time t, compute lt(s,a)


Average length of the k longest sequences ending in a
In state s, compute f(s,a)
Frequency of action a executed from state s
lt ( s , a )
f ( s, a )
 (1   )
Rank predictions using Rt (s, a)  
i lt (s, ai)
i f (s, ai)


[GP00] Peter Gorniak and David Poole, Predicting Future User Actions by Observing
Unmodified Applications, Seventeenth National Conference on Artificial Intelligence
(AAAI-2000), August 2000.
Onisi Example


[GP00]
k=3, for action a3 there are only two matches
of length 1 and 2, so lt(s3,a3) = (0+1+2)/3 = 1
If =0.9, the sum of averaged lengths for all
actions is 5, a3 has occurred 50 times in s3, and
s3 is visited 100 times, then
Rt(s3,a3) = 0.9*1/5 + 0.1*50/100 = 0.18+0.05 = 0.23
Example Sequence Predictors

Advantages



Permits predictions based on sequence of events
Simple learning mechanism
Problems



Relatively ad hoc weighting of sequence matches
Limited prediction capabilities
Large overhead for long past state/action
sequences
Markov Chain Prediction

Use the string of previous events or states to
create a model of the event generating
process.



Models are probabilistic and can be constructed
from the observed behavior of the system
Prediction is the most event that is most likely to
be generated by the model.
Issues:

What form should the model take ?
 String-based models
 State-based models
Example System: Active LeZi

Assumptions:



[GC03]
Event sequences are fairly repeatable
Generated by deterministic source
Construct model as parse tree of possible
event sequences


Nodes are events with associated frequencies
Model constructed using LZ78 text compression
algorithm
[DH98] K. Gopalratnam and D. J. Cook, Active LeZi: An Incremental Parsing
Algorithm for Device Usage Prediction in the Smart Home, In
Proceedings of the Florida Artificial Intelligence Research Symposium, 2003.
Text Compression: LZ78

Parses string x1 , x2 , …. xi into c(i)
substrings w1 , w2 , …. wc(i) that form the set
of phrases used for compression


Each prefix of a phrase wj is also a phrase wi in
the set used for compression
Example:


input aaababbbbbaabccddcbaaaa
yields phrases a,aa,b,ab,bb,bba,abc,c,d,dc,ba,aaa
Active LeZi

Represent compression phrases as a parse
tree with frequency statistics

E.g.: aaababbbbbaabccddcbaaaa
Prediction in Active LeZi

Calculate the probability for each possible
event

To calculate the probability, transitions across
phrase boundaries have to be considered





Slide window across the input sequence
Length k equal to longest phrase seen so far
Gather statistics on all possible contexts
Order k-1 Markov model
Output event with greatest probability across
all contexts as prediction
Example: Probability of a

Order 2


2/5 times that aa appears
5/10 times that a appears
Order 0

10/23 total symbols
Blended probability is
2 2 5
2  10 
    
5 5 10 10  23 
Order 1




Probability of escaping to
lower order = frequency of
null endings
Active LeZi Example: Prediction
on Simulated MavHome Data
Data simulates a single inhabitant interacting
with the devices in the home



Repetitive behavior patterns are embedded in the data
(e.g. morning routine )
Time is ignored in the prediction
Only device interactions are recorded
ALZ Performance II - Typical Scenarios with noise
100
90
80
70
60
50
40
30
20
10
0
% Prediction
Accuracy

0
500
1000
1500
Number Training Instances
2000
Active LeZi

Advantages




Permits predictions based on sequence of events
Does not require the construction of states
Permits probabilistic predictions
Problems


Tree can become very large (long prediction
times)
Nonoptimal predictions if the tree is not
sufficiently deep
Markov Chain Models

Markov chain models represent the event
generating process probabilistically.


Markov models can be described by a tuple
<S, T> representing states and transition
probabilities.
Markov assumption: The current state contains
all information about the past that is necessary
to predict the probability of the next state.
P(xt+1|xt, xt-1, …, x0) = P(xt+1 | xt)


Transitions correspond to events that occurred
in the environment (inhabitant actions, etc)
Prediction of next state (and event)
Markov Chain Example

Example states:


S = {(Room, Time, Day, Previous Room)}
Transition probabilities can be calculated from
training data by counting occurrences
# of times xi followed x j
P( xi | x j ) 
# of times x j ocurred
x2
x3
x6
x1
x4
x5
Markov Models

Advantages




Permits probabilistic predictions
Transition probabilities are easy to learn
Representation is easy to interpret
Problems




State space has to have Markov property
State space selection is not automatic
States might have to include previous information
State attributes might not be observable
Partially Observable MMs

Partially Observable Markov Models extend
Markov models by permitting states to be
only partially observable.

Systems can be represented by a tuple
<S, T, O, V> where <S, T> is a Markov model
and O, V are mapping observations about the
state to probabilities of a given state



O = {oi} is the set of observations
V: V(x, o) = P(o | x)
To determine a prediction the probability of
being in any given state is computed
Partially Observable MMs

Prediction is the most likely next state given
the information about the current state (i.e.
the current belief state):

Belief state B is a probability distribution over the
state space:
B = ((x1, P(x1)), …, (xn, P(xn))
t 1
P ( xi ) 
P(o | xi )
t
P
 ( x j ) P( xi | x j )

x
Prediction of the next state:
j

^
x  arg max  P( x j ) P( xi | x j )
xi
xj
Hidden Markov Models

Hidden Markov Models (HMM) provide
mechanisms to learn the Markov Model
<S, T> underlying a POMM from the
sequence of observations.
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Baum-Welch algorithm learns transition and
observation probabilities as well as the state
space (only the number of states has to be
given)
Model learned is the one that is most likely to
explain the observed training sequences
Hidden Markov Model Example

Tossing a balanced coin starting with a
biased coin that always starts heads:
Partially Observable MMs

Advantages

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
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Permits optimal predictions
HMM provide algorithms to learn the model
In HMM, Markovian state space description has
not to be known
Problems


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State space can be enormous
Learning of HMM is generally very complex
Computation of belief state is computationally
expensive
Example Location Prediction Task

Environment and observations:
[0, 1, 0, 2, 4, 5, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 4, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 4, 5, 4,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6]
[0, 1, 0, 2, 4, 5, 4, 6, 4, 3, 4, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 4, 5, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 4, 3, 4, 2, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2]
[0, 1, 0, 2, 4, 5, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 4, 3, 4, 2,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
[0, 1, 0, 2, 0, 2, 0, 2, 4, 5, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 4, 3, 4, 2, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 4, 5, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6]
[0, 1, 0, 2, 0, 2, 4, 5, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 4, 3, 4,
2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 4, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6]
[4, 3, 4, 2, 0, 1, 0, 0, 0, 1, 2, 4, 5, 4, 6, 6, 4, 3, 4, 2, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 4, 5, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 4, 3, 4, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 4, 5, 4, 6, 6]
Neural Network Predictor

Example network and training data

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Data has to be divided into training instances
Inputs represent current and 4 past locations
# Input training pattern 1:
66666
# Output training pattern 1:
1.000
# Input training pattern 2:
66434
# Output training pattern 2:
0.333
# Input training pattern 3:
66666
# Output training pattern 3:
1.000
# Input training pattern 4:
66666
# Output training pattern 4:
1.000
# Input training pattern 5:
66666
# Output training pattern 5:
1.000
Neural Network Predictor

Learning performance depends on:

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Network topology
Input representation
Learning rate
Hidden Markov Model Example

Input representation and learned HMM:

Initial and final HMM model
Conclusions

Prediction is important in intelligent
environments


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Different prediction algorithms have different
strength and weaknesses:

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Captures repetitive patterns (activities)
Helps automating activities (But: only tells what will
happen next; not what the system should do next)
Select a prediction approach that is suitable for the
particular problem.
There is no “best” prediction approach
Optimal prediction is a very hard problem and
is not yet solved.