ML-Aided Simulation: A Conceptual Framework for Integrating Simulation Models with Machine Learning

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ML-Aided Simulation: A Conceptual Framework for Integrating Simulation Models with Machine Learning Paper presented at ACM 2018 Conference on Principles of Advanced Discrete Simulation (PADS) https://dl.acm.org/citation.cfm?id=3200933 Authors: Mahmoud Elbattah and Owen Molloy National University of Ireland Galway

Data & Analytics

ACM 2018 Conference on Principles of Advanced Discrete Simulation (PADS)
ML-Aided Simulation:A Conceptual Framework for
Integrating Simulation Models with Machine Learning
Mahmoud Elbattah, Owen Molloy

ACM 2018 Conference on Principles of Advanced Discrete Simulation (PADS)
Integrating M&S with ML: Why, When, How?
2
Image Source:
Robinson, S. (2004). Simulation: The Practice of Model Development and Use. Chichester: Wiley.

ACM 2018 Conference on Principles of Advanced Discrete Simulation (PADS)
Basic View on Systems & Simulations
3
Source: Zeigler, B.P. and Sarjoughian, H.S., 2012. Guide to modeling and simulation of systems of systems.
Springer Science & Business Media.

ACM 2018 Conference on Principles of Advanced Discrete Simulation (PADS)
Motivational Questions
• How simulation models can learn about changes in the actual
system behaviour with minimal human input?
• Is it possible to integrate simulation models with ML models to
enable that learning process to happen in an automated
manner? If so, how?
• Can the integration with ML lead to a higher level of confidence
in simulations, given by a more measurable accuracy of ML
models?
4

ACM 2018 Conference on Principles of Advanced Discrete Simulation (PADS)
Key Idea 1:
Learning to Predict the System Behaviour
5

ACM 2018 Conference on Principles of Advanced Discrete Simulation (PADS)
Key Idea 2:
Identify Predictable Influential Variables
6
• Influential Variable: A variable that has a significant influence
on the system behaviour with respect to the question(s) of
interest, whereas the variation of that variable can lead to a
change in policy, strategy, or decision-making.

ACM 2018 Conference on Principles of Advanced Discrete Simulation (PADS)
Key Idea 2:
Identify Predictable Influential Variables (cont’d)
7

ACM 2018 Conference on Principles of Advanced Discrete Simulation (PADS)
Key Idea 2:
Identify Predictable Influential Variables (cont’d)
8

ACM 2018 Conference on Principles of Advanced Discrete Simulation (PADS)
Key Idea 3:
Incremental Learning = Adaptive Behaviour
9
A Basic Feedback Loop 1
Feedback Loops Aided by ML.
Source: Forrester, J.W., 1968. Principles of Systems, Text and Workbook, Wright-Allen Press-US.

ACM 2018 Conference on Principles of Advanced Discrete Simulation (PADS)
Key Idea 3:
Incremental Learning = Adaptive Behaviour (cont’d)
10

ACM 2018 Conference on Principles of Advanced Discrete Simulation (PADS)
How This Can Be Useful?
• The power of learning from data is that the entire process can
be automated with minimal, or without, involvement of human
input.
• This can be useful for modeling dynamic systems that exist in
rapidly changing environments (Concept Drift).
• Realising “self-adaptive” simulation models that can adapt their
behaviour based on ML predictions.
• May help reduce the epistemic uncertainty1 attributed to the
subjective interpretation of system knowledge.
• Works effectively in situations where the system behaviour can
be largely described and learned by examples.
11
Source: Oberkampf, W.L., DeLand, S.M., Rutherford, B.M., Diegert, K.V. and Alvin, K.F., 2002. Error and uncertainty in modeling
and simulation. Reliability Engineering & System Safety, 75(3), pp.333-357.

ACM 2018 Conference on Principles of Advanced Discrete Simulation (PADS)
Further Directions: More Complex ML for More
Complex Systems
12

ACM 2018 Conference on Principles of Advanced Discrete Simulation (PADS)
Closing Thought
• Machine Learning: The subfield of computer science that
gives computers the ability to learn without being explicitly
programmed (Arthur Samuel 1959).
• ML-Aided Simulations: Simulation models given the
ability to adapt to new knowledge without being explicitly
informed by modellers.
13
Source: Samuel, A.L., 1959. Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and
Development, 3(3), pp.210-229.

ACM 2018 Conference on Principles of Advanced Discrete Simulation (PADS)
THANK YOU!
Mahmoud Elbattah
mahmoud.elbattah@nuigalway.ie

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