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34 results on '"Castillo-Effen, Mauricio"'

1. Software Engineering for Robotics: Future Research Directions; Report from the 2023 Workshop on Software Engineering for Robotics

Author

Goues, Claire Le, Elbaum, Sebastian, Anthony, David, Celik, Z. Berkay, Castillo-Effen, Mauricio, Correll, Nikolaus, Jamshidi, Pooyan, Quigley, Morgan, Tabor, Trenton, and Zhu, Qi

Subjects
Computer Science - Robotics, Computer Science - Software Engineering
Abstract

Robots are experiencing a revolution as they permeate many aspects of our daily lives, from performing house maintenance to infrastructure inspection, from efficiently warehousing goods to autonomous vehicles, and more. This technical progress and its impact are astounding. This revolution, however, is outstripping the capabilities of existing software development processes, techniques, and tools, which largely have remained unchanged for decades. These capabilities are ill-suited to handling the challenges unique to robotics software such as dealing with a wide diversity of domains, heterogeneous hardware, programmed and learned components, complex physical environments captured and modeled with uncertainty, emergent behaviors that include human interactions, and scalability demands that span across multiple dimensions. Looking ahead to the need to develop software for robots that are ever more ubiquitous, autonomous, and reliant on complex adaptive components, hardware, and data, motivated an NSF-sponsored community workshop on the subject of Software Engineering for Robotics, held in Detroit, Michigan in October 2023. The goal of the workshop was to bring together thought leaders across robotics and software engineering to coalesce a community, and identify key problems in the area of SE for robotics that that community should aim to solve over the next 5 years. This report serves to summarize the motivation, activities, and findings of that workshop, in particular by articulating the challenges unique to robot software, and identifying a vision for fruitful near-term research directions to tackle them., Comment: 16 pages

Published
2024

2. Part-X: A Family of Stochastic Algorithms for Search-Based Test Generation with Probabilistic Guarantees

Author

Pedrielli, Giulia, Khandait, Tanmay, Chotaliya, Surdeep, Thibeault, Quinn, Huang, Hao, Castillo-Effen, Mauricio, and Fainekos, Georgios

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Data Structures and Algorithms, Electrical Engineering and Systems Science - Systems and Control
Abstract

Requirements driven search-based testing (also known as falsification) has proven to be a practical and effective method for discovering erroneous behaviors in Cyber-Physical Systems. Despite the constant improvements on the performance and applicability of falsification methods, they all share a common characteristic. Namely, they are best-effort methods which do not provide any guarantees on the absence of erroneous behaviors (falsifiers) when the testing budget is exhausted. The absence of finite time guarantees is a major limitation which prevents falsification methods from being utilized in certification procedures. In this paper, we address the finite-time guarantees problem by developing a new stochastic algorithm. Our proposed algorithm not only estimates (bounds) the probability that falsifying behaviors exist, but also it identifies the regions where these falsifying behaviors may occur. We demonstrate the applicability of our approach on standard benchmark functions from the optimization literature and on the F16 benchmark problem., Comment: 25 pages, 7 Figures

Published
2021

3. The Association for the Advancement of Artificial Intelligence 2020 Workshop Program

Author

Bang, Grace, Barash, Guy, Bea, Ryan, Cali, Jacques, Castillo-Effen, Mauricio, Chen, Xin Cynthia, Chhaya, Niyati, Dhoopar, Rohan, Dumancic, Sebastijan, Espinoza, Huascar, Farchi, Eitan, Fioretto, Ferdinando, Fuentetaja, Raquel, Galley, Michel, Geib, Christopher, Hemandez-Orallo, Jose, Huang, Xiaowei, Keren, Sarah, Kim, Seokhwan, Jaidka, Kokil, Liu, Xiaomo, Lu, Tyler, Ma, Zhiqiang, Mallah, Richard, McDermid, John, Michalowski, Martin, Mirsky, Reuth, Heigeartaigh, Sean O., Ramachandran, Deepak, Segovia-Aguas, Javier, Shaban-Nejad, Arash, Shehory, Onn, Shwartz, Vered, Srivastava, Siddharth, Talamadupula, Kartik, Tang, Jian, Zhang, Dell, and Zhang, Jian

Subjects
Artificial intelligence -- Conferences, meetings and seminars, Scientific societies -- Conferences, meetings and seminars, Artificial intelligence, Business, Association for the Advancement of Artificial Intelligence -- Conferences, meetings and seminars
Abstract

* The Association for the Advancement of Artificial Intelligence 2020 Workshop Program included twenty-three workshops covering a wide range of topics in artificial intelligence. This report contains the required reports, [...]

Published
2020

4. SafeAI 2023, Artificial Intelligence Safety 2023 : Proceedings of the Workshop on Artificial Intelligence Safety 2023 (SafeAI 2023), co-located with the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023)

Author

Pedroza, Gabriel, Huang, Xiaowei, Chen, Xin Cynthia, Theodorou, Andreas, Hernández-Orallo, José, Castillo-Effen, Mauricio, Mallah, Richard, McDermid, John, Pedroza, Gabriel, Huang, Xiaowei, Chen, Xin Cynthia, Theodorou, Andreas, Hernández-Orallo, José, Castillo-Effen, Mauricio, Mallah, Richard, and McDermid, John

Abstract

We summarize the AAAI-2023 Workshop on Artificial Intelligence Safety (SafeAI-2023), held at the 37th AAAI Conference on Artificial Intelligence on February 13-14, 2023 in Washington DC, USA.

Published
2023

5. The IJCAI-23 joint workshop on artificial intelligence safety and safe reinforcement learning (AISafety-SafeRL2023)

Author

Pedroza, Gabriel, Chen, Xin Cynthia, Hernández-Orallo, José, Huang, Xiaowei, Theodorou, Andreas, Matragkas, Nikolaos, Espinoza, Huascar, Mallah, Richard, McDermid, John, Castillo-Effen, Mauricio, Bossens, David, Koenighofer, Bettina, Tschiatschek, Sebastian, Liu, Anqi, Pedroza, Gabriel, Chen, Xin Cynthia, Hernández-Orallo, José, Huang, Xiaowei, Theodorou, Andreas, Matragkas, Nikolaos, Espinoza, Huascar, Mallah, Richard, McDermid, John, Castillo-Effen, Mauricio, Bossens, David, Koenighofer, Bettina, Tschiatschek, Sebastian, and Liu, Anqi

Abstract

We summarize the IJCAI-23 Joint Workshop on Artificial Intelligence Safety and Safe Reinforcement Learning (AISafety-SafeRL2023)1, held at the 32nd International Joint Conference on Artificial Intelligence (IJCAI-23) on August 21-22, 2023 in Macau, China., Conference: IJCAI-23 Joint Workshop on Artificial Intelligence Safety and Safe Reinforcement Learning (AISafety-SafeRL 2023), Macau, China, August 21-22, 2023.

Published
2023

7. Part-X: A Family of Stochastic Algorithms for Search-Based Test Generation With Probabilistic Guarantees

Author

Pedrielli, Giulia, Khandait, Tanmay, Cao, Yumeng, Thibeault, Quinn, Huang, Hao, Castillo-Effen, Mauricio, and Fainekos, Georgios

Abstract

Requirements driven search-based testing (also known as falsification) has proven to be a practical and effective method for discovering erroneous behaviors in Cyber-Physical Systems. Despite the constant improvements on the performance and applicability of falsification methods, they all share a common characteristic. Namely, they are best-effort methods which do not provide any guarantees on the absence of erroneous behaviors (falsifiers) when the testing budget is exhausted. The absence of finite time guarantees is a major limitation which prevents falsification methods from being utilized in certification procedures. In this paper, we address the finite-time guarantees problem by developing a new stochastic algorithm. Our proposed algorithm not only estimates (bounds) the probability that falsifying behaviors exist, but also identifies the regions where these falsifying behaviors may occur. We demonstrate the applicability of our approach on standard benchmark functions from the optimization literature and on the F16 benchmark problem. Note to Practitioners—The safety assurance problem for Cyber-Physical Systems (CPS) remains an open challenge. To demonstrate functional safety, practitioners must collect evidence that establishes that a system performs as expected under certain assumptions. The expected system behavior is typically captured through functional correctness requirements. In the case of CPS, evidence typically takes the form of test cases that are executed both on a model of the system and/or on the actual system. One of the challenges in producing such evidence is how to automatically generate test cases which are representative of the infinite execution space of CPS. Search-based test generation (SBTG) is a class of methods that can automatically generate test cases for CPS while being guided by the functional requirements. As SBTG methods try to discover test cases that invalidate, i.e., falsify, the requirements, they also collect validating, i.e., satisfying, test cases that can be used as evidence. This work introduces a method that can assess whether enough test cases have been executed given a finite testing budget. The sufficiency of the test suite is assessed by computing the probability that invalidating system behaviors may exist but have not yet been discovered. The practitioner can then adjust the number of test cases generated until a desired degree of confidence on the probability is achieved. Hence, our method not only works as an automated test case generation algorithm, but also as a method that provides formal functional performance guarantees on the system. Future directions will investigate extensions of our method to stochastic CPS.

Published
2024
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13. Flight critical software and systems development using ASSERT™

Author

Siu, Kit, primary, Moitra, Abha, additional, Durling, Michael, additional, Crapo, Andy, additional, Li, Meng, additional, Yu, Han, additional, Herencia-Zapana, Heber, additional, Castillo-Effen, Mauricio, additional, Sen, Shiraj, additional, McMillan, Craig, additional, Russell, Daniel, additional, Roy, Sundeep, additional, and Manolios, Panagiotis, additional

Published
2017
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16. Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence.

Author

Barash, Guy, Castillo-Effen, Mauricio, Chhaya, Niyati, Clark, Peter, Espinoza, Huáscar, Farchi, Eitan, Geib, Christopher, Gundersen, Odd Erik, hÉigeartaig, Seán Ó, Hernández-Orallo, José, Chiori Hori, Xiaowei Huang, Jaidka, Kokil, Kapanipathi, Pavan, Keren, Sarah, Kim, Seokhwan, Lanctot, Marc, Lange, Danny, Martinez, David, and Mattar, Marwan

Subjects
ARTIFICIAL intelligence, NATURAL language processing, DEEP learning, HUMAN activity recognition, REINFORCEMENT learning, RECOMMENDER systems, CYBER intelligence (Computer security)
Abstract

The workshop program of the Association for the Advancement of Artificial Intelligence’s 33rd Conference on Artificial Intelligence (AAAI-19) was held in Honolulu, Hawaii, on Sunday and Monday, January 27–28, 2019. There were fifteen workshops in the program: Affective Content Analysis: Modeling Affect-in-Action, Agile Robotics for Industrial Automation Competition, Artificial Intelligence for Cyber Security, Artificial Intelligence Safety, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Games and Simulations for Artificial Intelligence, Health Intelligence, Knowledge Extraction from Games, Network Interpretability for Deep Learning, Plan, Activity, and Intent Recognition, Reasoning and Learning for Human-Machine Dialogues, Reasoning for Complex Question Answering, Recommender Systems Meet Natural Language Processing, Reinforcement Learning in Games, and Reproducible AI. This report contains brief summaries of the all the workshops that were held [ABSTRACT FROM AUTHOR]

Published
2019
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33. A Practical Overview of Safety Concerns and Mitigation Methods for Visual Deep Learning Algorithms

Author

Saeed Bakhshi Germi, Esa Rahtu, Pedroza, Gabriel, Hernández-Orallo, José, Chen, Xin Cynthia, Huang, Xiaowei, Espinoza, Huáscar, Castillo-Effen, Mauricio, McDermid, John, Mallah, Richard, Ó hÉigeartaigh, Seán, Tampere University, and Computing Sciences

Subjects
113 Computer and information sciences
Abstract

This paper proposes a practical list of safety concerns and mitigation methods for visual deep learning algorithms. The growing success of deep learning algorithms in solving non-linear and complex problems has recently attracted the attention of safety-critical applications. While the state-of-the-art methods achieve high performance in synthetic and real-case scenarios, it is impossible to verify/validate their reliability based on currently available safety standards. Recent works try to solve the issue by providing a list of safety concerns and mitigation methods in generic machine learning algorithms from the standards’ perspective. However, these solutions are either vague, and non-practical when dealing with deep learning methods in real-case scenarios, or they are shallow and fail to address all potential safety concerns. This paper provides an in-depth look at the underlying cause of faults in a visual deep learning algorithm to find a practical and complete safety concern list with potential state-of-the-art mitigation strategies. publishedVersion

34. Time for AI (Ethics) maturity model is now

Author

Vakkuri Ville, Marianna Jantunen, Halme Erika, Kemell Kai-Kristian, Nguyen-Duc Anh, Mikkonen Tommi, Abrahamsson Pekka, Department of Computer Science, Empirical Software Engineering research group, Doctoral Programme in Computer Science, Espinoza, Huáscar, McDermid, John, Huang, Xiaowei, Castillo-Effen, Mauricio, Chen, Xin Cynthia, Hernandez-Orallo, José, OhEigeartaigh, Seán, and Mallah, Richard

Subjects
FOS: Computer and information sciences, järjestelmäsuunnittelu, Computer Science - Computers and Society, toimintaohjeet, ComputingMethodologies_PATTERNRECOGNITION, Computers and Society (cs.CY), tekoäly, etiikka, eettisyys, ohjelmistokehitys, 113 Computer and information sciences, GeneralLiterature_MISCELLANEOUS
Abstract

Publisher Copyright: Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). There appears to be a common agreement that ethical concerns are of high importance when it comes to systems equipped with some sort of Artificial Intelligence (AI). Demands for ethical AI are declared from all directions. As a response, in recent years, public bodies, governments, and universities have rushed in to provide a set of principles to be considered when AI based systems are designed and used. We have learned, however, that high-level principles do not turn easily into actionable advice for practitioners. Hence, also companies are publishing their own ethical guidelines to guide their AI development. This paper argues that AI software is still software and needs to be approached from the software development perspective. The software engineering paradigm has introduced maturity model thinking, which provides a roadmap for companies to improve their performance from the selected viewpoints known as the key capabilities. We want to voice out a call for action for the development of a maturity model for AI software. We wish to discuss whether the focus should be on AI ethics or, more broadly, the quality of an AI system, called a maturity model for the development of AI systems.

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