Neural network pruning is a technique of network compression by removing weights of lower importance from an optimized neural network. Often, pruned networks are compared in terms of accuracy, which is realized in terms of rewards for Deep Reinforcement Learning (DRL) networks. However, networks that estimate control actions for safety-critical tasks, must also adhere to safety requirements along with obtaining rewards. We propose a methodology to iteratively refine the weights of a pruned neural network such that we get a sparse high-performance network without significant side effects on safety.

Safety Aware Neural Pruning for Deep Reinforcement Learning

Increased dependence on networked, software-based control has escalated the vulnerabilities of Cyber-Physical Systems (CPSs). Detection and monitoring components developed leveraging dynamical systems theory are often employed as lightweight security measures for protecting such safety-critical CPSs against false data injection attacks. However, existing approaches do not correlate attack scenarios with parameters of detection systems. In this work, we propose real-time attack detection and mitigation strategies for safety-critical CPSs. A Reinforcement Learning (RL) based framework is presented which adaptively sets the parameters of such detectors based on experience learned from attack scenarios. The detection system is provided with a suitable training environment to learn how to maximize the detection rate while minimizing false alarms. Along with the objective of attack detection, our framework also attempts to preserve system performance by judiciously choosing control actions based on the operating region. Our proposed method i) mathematically establishes a detection strategy for fast and accurate FDI attack detection, ii) correlates the key parameters of the detection system by learning from attack scenarios, and iii) incorporates a real-time attack mitigation strategy that uses formally synthesized fast controllers, thus creating an end-to-end defense against FDI attacks for safety-critical CPSs. We evaluate our proposed method using well-known safety-critical CPS examples.

Catch Me If You Learn: Real-Time Attack Detection and Mitigation in Learning Enabled CPS

Safe Reinforcement Learning (SRL) algorithms aim to learn a policy that maximizes the reward while satisfying the safety constraints. One of the challenges in SRL is that it is often difficult to balance the two objectives of reward maximization and safety constraint satisfaction. Existing algorithms utilize constraint optimization techniques like penalty-based, barrier penalty-based, and Lagrangian-based dual or primal policy optimizations methods. However, they suffer from training oscillations and approximation errors, which impact the overall learning objectives.

This paper proposes the Permeable Penalty Barrier-based Policy Optimization (P2BPO) algorithm that addresses this issue by allowing a small fraction of penalty beyond the penalty barrier, and a parameter is used to control this permeability. In addition, an adaptive penalty parameter is used instead of a constant one, which is initialized with a low value and increased gradually as the agent violates the safety constraints. We have also provided a theoretical proof of the proposed method's performance guarantee bound, which ensures that P2BPO can learn a policy satisfying the safety constraints with high probability while achieving a higher expected reward. Furthermore, we compare P2BPO with other SRL algorithms on various SRL tasks and demonstrate that it achieves better rewards while adhering to the constraints.

P2BPO: Permeable Penalty Barrier-Based Policy Optimization for Safe RL

Student Abstract 3-min Presentation Contest Session 1

lightning talk

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

**Session chair:** Martina Maggio

- 25 min: [**(Outstanding Paper) Concurrent Order Dispatch for Instant Delivery with Time-Constrained Actor-Critic Reinforcement Learning**](https://conferences.computer.org/rtsspub/pdfs/RTSS2021-23beZleJJDdiozCiQ9rzXD/280200a176/280200a176.pdf), *Baoshen Guo, Shuai Wang, Yi Ding, Guang Wang, Suining He, Desheng Zhang and Tian He*
- 25 min: [**Keep Fresh: Real-time Data Retrieval with Speed Adaptation in Mobile Cyber-Physical Systems**](https://conferences.computer.org/rtsspub/pdfs/RTSS2021-23beZleJJDdiozCiQ9rzXD/280200a304/280200a304.pdf), *Chenchen Fu, Xiaoxing Qiu, Zelin Yun, Song Han, Weiwei Wu and Jason Xue*
- 25 min: [**Catch Me If You Learn: Real-Time Attack Detection and Mitigation in Learning Enabled CPS**](https://conferences.computer.org/rtsspub/pdfs/RTSS2021-23beZleJJDdiozCiQ9rzXD/280200a136/280200a136.pdf), *Ipsita Koley, Sunandan Adhikary and Soumyajit Dey*
- 25 min: [**Composite Resource Scheduling for Networked Control Systems**](https://conferences.computer.org/rtsspub/pdfs/RTSS2021-23beZleJJDdiozCiQ9rzXD/280200a162/280200a162.pdf), *Peng Wu, Chenchen Fu, Tianyu Wang, Minming Li, Yingchao Zhao, Jason Xue and Song Han*



4.12 - Cyber-Physical Systems

technical paper

 **RTSS 2021** has successfully concluded. Hope you have enjoyed it, and look forward to seeing you next year in person.
    Announcement for the Awards:
 * Best Paper Award goes to: Monte Carlo Response-Time Analysis, Sergey Bozhko, Georg von der Brüggen, and Björn Brandenburg
 * Best Student Paper Award goes to: Automatic Energy-Hotspot Detection and Elimination in Real-Time Deeply Embedded Systems, Mohsen Shekarisaz, Lothar Thiele, and Mehdi Kargahi.
 * Best Solution Award for Industry Session goes to: Toward Real-Time Guaranteed Scheduling for Autonomous Driving Systems. Jinghao Sun, Tianyi Wang, Kailu Duan, Bin Lu, Jiankang Ren, Zhishan Guo, and Guozhen Tan.



IEEE RTSS 2021

The IEEE Real-Time Systems Symposium (RTSS) is the premier conference in the field of real-time systems, and is a venue for researchers and practitioners to showcase innovations covering all aspects of real-time systems, including theory, design, analysis, implementation, evaluation, and experience. 

Safe, Robust and Responsible AI

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

Soumyajit Dey

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SHORT BIO

Presentations

Safety Aware Neural Pruning for Deep Reinforcement Learning

Catch Me If You Learn: Real-Time Attack Detection and Mitigation in Learning Enabled CPS

P2BPO: Permeable Penalty Barrier-Based Policy Optimization for Safe RL

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