Physical models are important but difficult to build for adaptive cyber-physical systems (CPS). A single modeling formalism is not enough, and formalisms should be selected based on their expressiveness, supported analyses, and the relevant engineering expertise. Physical models should be obtained in a way that maximizes their value, analytical power, and fragility. Both theory-driven and data-driven approaches have tradeoffs. Finally, physical models should be treated as first-class entities in adaptation and be continuously adapted based on their predicted value and computational cost at runtime.

1. Ivan Ruchkin, Selva Samuel, Bradley Schmerl,
Amanda Rico, and David Garlan
Institute for Software Research, Carnegie Mellon University

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 CPS operate in uncertain contexts
 Need to adapt to unanticipated situations

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System & environment
under adaptation
Adaptation with models
Phenomena

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System & environment
under adaptation
Adaptation with physical models
Physical phenomena

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NUC
(computer)
Kinect
(sensor)
Base
(actuator
& battery)

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9.  Abstractions of physical objects and
interactions
 Beyond simple discrete models
 Objects may be in the system, in the environment,
or on the border
 Example: power model forTurtleBot
 How much does each task consume?
 How much power is left given current voltage?
 How long does it take to charge?
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10.  Software models guide state-of-the-art
adaptive systems
 Physical models are often implicit or assumed
 In CPS, we need both software and physical
models!
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11. 1. Selecting modeling formalism
2. Obtaining physical models
3. Using physical models in adaptation
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12. 1. Selecting modeling formalism
2. Obtaining physical models
3. Using physical models in adaptation
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13.  Many formalisms and tools are available for
modeling CPS
 Differential equations, signal flow graphs, automata
 Position: no single formalism is enough to
model adaptive CPS; we need to embrace their
multiplicity
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14.  Evaluate individual formalisms
 Expressiveness
▪ Linear/non-linear, continuous/discrete, classes of
functions (polynomials, transcendental functions, etc.)
 Types of analyses supported
▪ Trade-off between expressiveness and computing cost
 Engineering expertise
▪ Novices: higher effort and lower quality
 We need approaches to integrate formalisms!
 Difficult problem, outside of talk’s scope
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15.  We chose a linear real-valued regression model
 Continuous changes in parameters
 Easily embeddable into other models
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P(v, t) = Av + Bt + C
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time (s)
power(wh)

16. 1. Selecting modeling formalism
2. Obtaining physical models
3. Using physical models in adaptation
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17.  Goal: maximize value of each model
 Analytical power: strength of predictions and
explanations
 Fragility: amount of rework to accommodate future
changes
 Computational cost: amount of processing needed
for analysis
 Position: the way we build physical models
affects their value.We need more guidance!
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18.  Theory-driven
 Physical theory
dictates first principles
 Calibrate with data
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 Data-driven
 Collect data first
 Then create
abstractions from it
time (s)
power(wh)

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 We chose to use data-driven approach
 Low expertise with theory-driven models
 Ok with low-precision far-horizon predictions
 The model is fragile: hard to change

20. 1. Selecting modeling formalism
2. Obtaining physical models
3. Using physical models in adaptation
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21.  Software models in adaptation are used for:
 State estimation and prediction
 Triggering adaptive changes
 Choosing adaptive strategy
 + Continuous improvement of models themselves
 Position: physical models should also be
treated as first-class entities in adaptation
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22.  Clear representation
 Either separate models or explicit embedding
 Easier change and reuse
 Coordinated use with cyber models
 Estimation, prediction, choice
 Models themselves should be adapted
 Model value & cost should be the guiding factors
 Need to reason about model value at run time!
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23. Physical models in adaptive CPS are
important and difficult to build
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Challenge Position
Selecting modeling
formalism
Embrace multiplicity; use formalisms
based on expressiveness, analyses, and
expertise.
Obtaining physical models Model value should the guiding factor.
More guidance is needed to connect
model- building and model value.
Using physical models in
adaptation
Physical models should be treated as first-
class entities and adapted based on their
value at run time.