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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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 for TurtleBot

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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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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1. Selecting modeling formalism

2. Obtaining physical models

3. Using physical models in adaptation

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1. Selecting modeling formalism

2. Obtaining physical models

3. Using physical models in adaptation

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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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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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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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1. Selecting modeling formalism

2. Obtaining physical models

3. Using physical models in adaptation

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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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Theory-driven

Physical theory dictates first principles

Calibrate with data

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Data-driven

Collect data first

Then create abstractions from it

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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

1. Selecting modeling formalism

2. Obtaining physical models

3. Using physical models in adaptation

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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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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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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.