intelligent environments
DESCRIPTION
Intelligent Environments. Computer Science and Engineering University of Texas at Arlington. Prediction for Intelligent Environments. Motivation Techniques Issues. Motivation. - PowerPoint PPT PresentationTRANSCRIPT
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Intelligent Environments 1
Intelligent Environments
Computer Science and Engineering
University of Texas at Arlington
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Intelligent Environments 2
Prediction forIntelligent Environments Motivation Techniques Issues
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Intelligent Environments 3
Motivation An intelligent environment
acquires and applies knowledge about you and your surroundings in order to improve your experience. “acquires” prediction “applies” decision making
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Intelligent Environments 4
What to Predict Inhabitant behavior
Location Task Action
Environment behavior Modeling devices Interactions
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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
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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
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Example Learned pattern
If Day = Monday…Friday& Time > 0600& Time < 0700& Locationt = Bedroom
Then Locationt+1 = Bathroom
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Intelligent Environments 8
Prediction Techniques Regression Neural network Nearest neighbor Bayesian classifier Decision tree induction Others
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Intelligent Environments 9
Linear Regression
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Intelligent Environments 10
Multiple Regression
n independent variables Find bi
System of n equations and n unknowns
nnxbxbxbby ...22110
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Intelligent Environments 11
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
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Intelligent Environments 12
Neural Networks
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Neural Networks 10-105 synapses per neuron Synapses propagate
electrochemical signals Number, placement and strength
of connections changes over time (learning?)
Massively parallel
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Intelligent Environments 14
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
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Intelligent Environments 15
Computer vs. Human Brain
“The Age of Spiritual Machines,” Kurzweil.
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Intelligent Environments 16
Artificial Neuron
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Intelligent Environments 17
Artificial Neuron Activation functions
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Intelligent Environments 18
Perceptron
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Intelligent Environments 19
Perceptron Learning
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Intelligent Environments 20
Perceptron Learns only linearly-separable
functions
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Intelligent Environments 21
Sigmoid Unit
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Intelligent Environments 22
Multilayer Network ofSigmoid Units
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Intelligent Environments 23
Error Back-Propagation Errors at output layer propagated
back to hidden layers Error proportional to link weights
and activation Gradient descent in weight space
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Intelligent Environments 24
NN for Face Recognition
90% accurate learning head pose for 20 different people.
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Neural Networks Pros
General purpose learner Fast prediction
Cons Best for numeric inputs Slow training Local optima
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Intelligent Environments 26
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
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Intelligent Environments 27
Nearest Neighbor
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Intelligent Environments 28
Nearest Neighbor Pros
Fast training Complex target functions No loss of information
Cons Slow at query time Easily fooled by irrelevant attributes
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Intelligent Environments 29
Bayes Classifier Recall Bob example
D = training data h = sample rule
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best
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Intelligent Environments 30
Naive Bayes Classifier
Naive Bayes assumption
Naive Bayes classifier
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y representsBob’s location
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Intelligent Environments 31
Bayes Classifier Pros
Optimal Discrete or numeric attribute values Naive Bayes easy to compute
Cons Bayes classifier computationally
intractable Naive Bayes assumption usually violated
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Intelligent Environments 32
Decision Tree Induction
Day
Time > 0600
Locationt
Time < 0700
Bathroom
M…F
yes
yes
Bedroom …
no
no
SatSun
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Intelligent Environments 33
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
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Intelligent Environments 34
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 (50)Kitchen (0)
Bathroom (25)Kitchen (25)
Bathroom (25)Kitchen (25)? ? B K
v2v1 v1 v2
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Decision Tree Induction Pros
Understandable rules Fast learning and prediction
Cons Replication problem Limited rule representation
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Intelligent Environments 36
Other Prediction Methods Hidden Markov models Radial basis functions Support vector machines Genetic algorithms Relational learning
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Intelligent Environments 37
Prediction Issues Representation of data and
patterns Relevance of data Sensor fusion Amount of data
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Intelligent Environments 38
Prediction Issues Evaluation
Accuracy False positives vs. false negatives
Concept drift Time-series prediction Distributed learning