intelligent environments

Intelligent Environments 1

Intelligent Environments

Computer Science and Engineering

University of Texas at Arlington


Prediction forIntelligent Environments Motivation Techniques Issues


Motivation An intelligent environment

acquires and applies knowledge about you and your surroundings in order to improve your experience. “acquires” prediction “applies” decision making


What to Predict Inhabitant behavior

Location Task Action

Environment behavior Modeling devices Interactions


Example Where will Bob go next? Locationt+1 = f(…) Independent variables

Locationt, Locationt-1, … Time, date, day of the week Sensor data Context

Bob’s task


Example (cont.)Time Date Day Locationt Locationt+1

0630 02/25 Monday Bedroom Bathroom

0700 02/25 Monday Bathroom Kitchen

0730 02/25 Monday Kitchen Garage

1730 02/25 Monday Garage Kitchen

1800 02/25 Monday Kitchen Bedroom

1810 02/25 Monday Bedroom Living room

2200 02/25 Monday Living room

Bathroom

2210 02/25 Monday Bathroom Bedroom

0630 02/26 Tuesday Bedroom Bathroom


Example Learned pattern

If Day = Monday…Friday& Time > 0600& Time < 0700& Locationt = Bedroom

Then Locationt+1 = Bathroom


Prediction Techniques Regression Neural network Nearest neighbor Bayesian classifier Decision tree induction Others


Linear Regression

n

ii

n

iii

xx

yyxxm

1

2

1

)(

))((

bmxy

xmyb

x y

1 3

2 5

3 7

4 9


Multiple Regression

n independent variables Find bi

System of n equations and n unknowns

nnxbxbxbby ...22110


Regression Pros

Fast, analytical solution Confidence intervals

y = a ± b with C% confidence Piecewise linear and nonlinear regression

Cons Must choose model beforehand

Linear, quadratic, … Numeric variables


Neural Networks


Neural Networks 10-105 synapses per neuron Synapses propagate

electrochemical signals Number, placement and strength

of connections changes over time (learning?)

Massively parallel


Computer vs. Human BrainComputer 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


Computer vs. Human Brain

“The Age of Spiritual Machines,” Kurzweil.


Artificial Neuron


Artificial Neuron Activation functions


Perceptron


Perceptron Learning


Perceptron Learns only linearly-separable

functions


Sigmoid Unit


Multilayer Network ofSigmoid Units


Error Back-Propagation Errors at output layer propagated

back to hidden layers Error proportional to link weights

and activation Gradient descent in weight space


NN for Face Recognition

90% accurate learning head pose for 20 different people.


Neural Networks Pros

General purpose learner Fast prediction

Cons Best for numeric inputs Slow training Local optima


Nearest Neighbor Just store training data (xi,f(xi)) Given query xq, estimate using

nearest neighbor xk: f(xq) = f(xk) k nearest neighbor

Given query xq, estimate using majority (mean) of k nearest neighbors


Nearest Neighbor


Nearest Neighbor Pros

Fast training Complex target functions No loss of information

Cons Slow at query time Easily fooled by irrelevant attributes


Bayes Classifier Recall Bob example

D = training data h = sample rule

)(

)()|()|(

DP

hPhDPDhP

)|(maxarg DhPhHh

best


Naive Bayes Classifier

Naive Bayes assumption

Naive Bayes classifier

),...,,(

)()|,...,,(maxarg

),...,,|(maxarg

21

21

21

n

jjn

Yybest

njYy

best

aaaP

yPyaaaPy

aaayPy

j

j

i

jijn yaPyaaaP )|()|,...,,( 21

ijij

YyNB yaPyPy

j

)|()(maxarg

y representsBob’s location


Bayes Classifier Pros

Optimal Discrete or numeric attribute values Naive Bayes easy to compute

Cons Bayes classifier computationally

intractable Naive Bayes assumption usually violated


Decision Tree Induction

Day

Time > 0600

Locationt

Time < 0700

Bathroom

M…F

yes

yes

Bedroom …

no

no

SatSun


Decision Tree Induction Algorithm (main loop)

1. A = best attribute for next node2. Assign A as attribute for node3. For each value of A, create

descendant node4. Sort training examples to descendants5. If training examples perfectly

classified, then Stop, else iterate over descendants


Decision Tree Induction Best attribute Based on information-theoretic

concept of entropy Choose attribute reducing entropy

(~uncertainty) from parent to descendant nodes

A1 A2

Bathroom (0)Kitchen (50)



Bathroom (25)Kitchen (25)? ? B K

v2v1 v1 v2


Decision Tree Induction Pros

Understandable rules Fast learning and prediction

Cons Replication problem Limited rule representation


Other Prediction Methods Hidden Markov models Radial basis functions Support vector machines Genetic algorithms Relational learning


Prediction Issues Representation of data and

patterns Relevance of data Sensor fusion Amount of data


Prediction Issues Evaluation

Accuracy False positives vs. false negatives

Concept drift Time-series prediction Distributed learning

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