Sensitivity Analysis for Building Adaptive Robotic Software

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P. Jamshidi, M. Velez, C. Kästner, N. Siegmund, and P. Kawthekar. Transfer learning for improving model predictions in highly configurable software. Int’l Symp. Software Engineering for Adaptive and Self-Managing Systems (SEAMS), 2017.

Technologie

SENSITIVITY ANALYSIS FOR
BUILDING ADAPTIVE ROBOTIC SOFTWARE
Pooyan Jamshidi, Miguel Velez and Christian Kästner
INTENT DISCOVERY: SENSITIVITY ANALYSIS FOR CONFIGURATION OPTIMIZATION
REDUCING COSTS WITH TRANSFER LEARNING
USE CASES
Systematic System Evolution
To automate or guide intelligent design choices.
Runtime Adaptation
To enable runtime adaptation of software configurations
to maintain quality of performance under dynamic
conditions (changing environment, goals, and tasks).
Performance Debugging
To guide robot software developers to identify potential
bugs causing low quality of performance.
RESULTS
Motivation:
Robotic software expose configurable parameters.
These tunable parameters affect performance of robots.
This can be leveraged to optimize performance.
Source Response Target Response
Transfer learning combines:
Lots of data gathered cheaply from
the simulator
With much less data gathered
expensively from the target robot
To make better predictions overall
PUBLICATIONS
P. Jamshidi, M. Velez, C. Kästner, N. Siegmund, and P. Kawthekar. Transfer
learning for improving model predictions in highly conﬁgurable software. Int’l
Symp. Software Engineering for Adaptive and Self-Managing Systems
(SEAMS), 2017.
P. Kawthekar and C. Kästner. Sensitivity analysis for building evolving &
adaptive robotic software, Workshop on Autonomous Mobile Service Robots
(WSF), 2016.
Predictive
Model
Learn
Model
Measure
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Source
Target
Simulator (Gazebo) Robot (TurtleBot)
Predict
Performance
Predictions
Adaptation
Use for
analysis
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(a) (b)
(c) (d)
Prediction without transfer learning
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Prediction with transfer learning
Using only a few real data points to
predict yields poor results across
configuration space
Using transfer learning to combine the
few real data points with lots of
approximate data yields a good model
Machine
Learning
Conﬁguration
Parameters
Design of
Experiment
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ents
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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

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Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida. In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization. In this session, participants gained answers to the following questions: - What is a Green Information Management (IM) Strategy, and why should you have one? - How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication? - How can an organization use insights into their data to influence employee behavior for IM? - How can you reap additional benefits from content reduction that go beyond Green IM?

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Sensitivity Analysis for Building Adaptive Robotic Software

1. SENSITIVITY ANALYSIS FOR BUILDING ADAPTIVE ROBOTIC SOFTWARE Pooyan Jamshidi, Miguel Velez and Christian Kästner INTENT DISCOVERY: SENSITIVITY ANALYSIS FOR CONFIGURATION OPTIMIZATION REDUCING COSTS WITH TRANSFER LEARNING USE CASES Systematic System Evolution To automate or guide intelligent design choices. Runtime Adaptation To enable runtime adaptation of software configurations to maintain quality of performance under dynamic conditions (changing environment, goals, and tasks). Performance Debugging To guide robot software developers to identify potential bugs causing low quality of performance. RESULTS Motivation: Robotic software expose configurable parameters. These tunable parameters affect performance of robots. This can be leveraged to optimize performance. Source Response Target Response Transfer learning combines: Lots of data gathered cheaply from the simulator With much less data gathered expensively from the target robot To make better predictions overall PUBLICATIONS P. Jamshidi, M. Velez, C. Kästner, N. Siegmund, and P. Kawthekar. Transfer learning for improving model predictions in highly configurable software. Int’l Symp. Software Engineering for Adaptive and Self-Managing Systems (SEAMS), 2017. P. Kawthekar and C. Kästner. Sensitivity analysis for building evolving & adaptive robotic software, Workshop on Autonomous Mobile Service Robots (WSF), 2016. Predictive Model Learn Model Measure Measure Data Source Target Simulator (Gazebo) Robot (TurtleBot) Predict Performance Predictions Adaptation Use for analysis 5 10 15 20 25 number of particles 5 10 15 20 25 numberofrefinements 0 5 10 15 20 25 5 10 15 20 25 number of particles 5 10 15 20 25 numberofrefinements 0 5 10 15 20 25 5 10 15 20 25 number of particles 5 10 15 20 25 numberofrefinements 0 5 10 15 20 25 CPU usage [%] CPU usage [%] (a) (b) (c) (d) Prediction without transfer learning 5 10 15 20 25 5 10 15 20 25 10 15 20 25 Prediction with transfer learning Using only a few real data points to predict yields poor results across configuration space Using transfer learning to combine the few real data points with lots of approximate data yields a good model Machine Learning Configuration Parameters Design of Experiment Configuration Space Predictive Model Sensitivity Analysis DataMeasurem ents Configuration Space Data Accuracy Energy CPU 0 5 10 15 20 25 30 35 mean CPU utilization 0 500 1000 1500 2000 2500 3000 3500 numberofconfigurations 5 10 15 20 25 number of particles 5 10 15 20 25 numberofrefinements 6 8 10 12 14 16 18 20 22 24 26 5 10 15 20 25 number of particles 5 10 15 20 25 numberofrefinements 5 10 15 20 25 30 35 40 45 CPU Localisation Error

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