Strategies for Reducing the Complexity of Symbolic Models for Activity Recognition
Kristina Yordanova Chair of Mobile Multimedia Information Systems Department of Computer Science University of Rostock
http://mmis.informatik.uni-rostock.de email:
[email protected]
11 September 2014
University of Rostock
Kristina Yordanova
Strategies for Reducing the Complexity of Symbolic Models for AR
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Assisting the user
Kristina Yordanova
Strategies for Reducing the Complexity of Symbolic Models for AR
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Recognising the user activities
Coffee machine mat sensor
Door mat sensor
Printer mat sensor
1 0 0 0 0 0 - enter 0 0 0 0 0 0 - walk 0 0 0 0 0 0 - walk 0 1 0 0 0 0 - print 0 1 0 0 0 0 - print
Office room Coffee jar mat sensor
train the model the model relies on training data
Kristina Yordanova
Water tap mat sensor
Paper stack mat sensor
action walk Pre: person at start position Effect: person at end position
generate the model: potential advantages sequences that do not appear often situations with unavailable training data
Strategies for Reducing the Complexity of Symbolic Models for AR
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Probabilistic symbolic models
rely on the idea of Bayesian filtering
ż ppxt |y1:t ´1 q “ ppxt |y1:t q “
ppxt |xt ´1 q ppyt |xt q ppx0 q
Kristina Yordanova
X
ppxt |xt ´1 qppxt ´1 |y1:t ´1 qdx
ppyt |xt qppxt |y1:t ´1 q ppyt |y1:t ´1 q
prediction
correction
system model observation model prior density
Strategies for Reducing the Complexity of Symbolic Models for AR
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Probabilistic symbolic models: system model
ppxt |xt ´1 q “
ř
at PA p pxt |at , xxt ´1 qp pat |dt , xt ´1 qp pdt |xt ´1 q dt PD
ppxt |at , xxt ´1 q
causal model
ppat |dt , xt ´1 q
action selection
ppdt |xt ´1 q
duration model
Kristina Yordanova
system model
Strategies for Reducing the Complexity of Symbolic Models for AR
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Elements needed for building the system model
causal model
action selection
precondition-effect rules
Kristina Yordanova
goal distance, action weights, cognitive heuristics
action durations
probability distribution
Strategies for Reducing the Complexity of Symbolic Models for AR
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Problems with symbolic models 9 rules can generate models with thousands of states difficult to find problems difficult to achieve high performance
o
o Kristina Yordanova
Strategies for Reducing the Complexity of Symbolic Models for AR
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Outline
1
Introduction
2
Reducing the model complexity
Problem: how to find the actual execution path? o
o by introducing better action selection heuristics Kristina Yordanova
Strategies for Reducing the Complexity of Symbolic Models for AR
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Problem: how to find the actual execution path? o
o by reducing the model complexity on symbolic level Kristina Yordanova
Strategies for Reducing the Complexity of Symbolic Models for AR
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Reducing the model complexity on symbolic level
difficult for inexperienced designers to build models with small size 8 modelling patterns for reducing the model complexity in the action’s preconditions and effects locks abstract reusable actions repeating behaviour macro structures action synchronisation type hierarchy combining objects phases Detailed description of the different patterns can be found in Kristina Yordanova: Modelling Toolkit for Computational Causal Behaviour Models, Methods for Engineering Symbolic Human Behaviour Models for Activity Recognition, PhD Thesis, 2014.
Kristina Yordanova
Strategies for Reducing the Complexity of Symbolic Models for AR
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Pattern: phases Name: Phases Intent: reduces the model complexity Applicability: in problems with high number of elements in the environment Implementation: Actions can be executed only in certain parts of the graph by defining execution phases that activate or block certain actions Structure: init
goal
Sample code (:action activate-phase :precondition (and (phase-1) (not (can-do-something)) ) :effect (and (not (phase-1)) (phase-2) (can-do-something) ) )
Known uses: cooking task with a lot of objects involved Kristina Yordanova
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Reducing the model complexity: results 1e+08
Number of plans intuitive patterns
1e+04 1 user
2 users
1e+00
1e+00
1e+04
1e+08
Number of states intuitive patterns
3 users
1 user
2 users
3 users
Average accuracy
0.0
0.2
0.4
0.6
0.8
intuitive patterns
Kristina Yordanova
1 user
2 users
3 users
Strategies for Reducing the Complexity of Symbolic Models for AR
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Reducing the model complexity: results 3500 2500
intuitive patterns
0
500
1e+02 1e+00
Number of plans
1500
1e+04
1e+06
Number of states intuitive patterns
meeting
cooking
meeting
cooking
Average accuracy
0.0
0.2
0.4
0.6
0.8
intuitive patterns
Kristina Yordanova
meeting
cooking
Strategies for Reducing the Complexity of Symbolic Models for AR
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Summary: reducing the model complexity
need for mechanisms for reducing the model complexity 8 modelling patterns provide mechanisms for reducing the model complexity reducing the model complexity increases the model performance
Kristina Yordanova
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Discussion
Thank you for your attention!
Kristina Yordanova
[email protected] Chair of Mobile Multimedia Information Systems Department of Computer Science University of Rostock
http://mmis.informatik.uni-rostock.de
Kristina Yordanova
Strategies for Reducing the Complexity of Symbolic Models for AR
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