Original Paper Information:
Probabilistic Model Checking and Autonomy
Published 2021-11-20T16:56:28 00:00.
Category: Computer Science
Authors:
[‘Marta Kwiatkowska’, ‘Gethin Norman’, ‘David Parker’]
Original Abstract:
Design and control of autonomous systems that operate in uncertain oradversarial environments can be facilitated by formal modelling and analysis.Probabilistic model checking is a technique to automatically verify, for agiven temporal logic specification, that a system model satisfies thespecification, as well as to synthesise an optimal strategy for its control.This method has recently been extended to multi-agent systems that exhibitcompetitive or cooperative behaviour modelled via stochastic games andsynthesis of equilibria strategies. In this paper, we provide an overview ofprobabilistic model checking, focusing on models supported by the PRISM andPRISM-games model checkers. This includes fully observable and partiallyobservable Markov decision processes, as well as turn-based and concurrentstochastic games, together with associated probabilistic temporal logics. Wedemonstrate the applicability of the framework through illustrative examplesfrom autonomous systems. Finally, we highlight research challenges and suggestdirections for future work in this area.
Context On This Paper:
The main objective of this paper is to provide an overview of probabilistic model checking and its application in designing and controlling autonomous systems that operate in uncertain or adversarial environments. The research question is how probabilistic model checking can be used to verify system models and synthesize optimal strategies for control. The methodology involves using PRISM and PRISM-games model checkers to support various models, including fully observable and partially observable Markov decision processes, turn-based and concurrent stochastic games, and associated probabilistic temporal logics. The results demonstrate the applicability of the framework through illustrative examples from autonomous systems. The conclusion highlights research challenges and suggests directions for future work in this area.
Flycer’s Commentary:
The paper discusses the use of probabilistic model checking in designing and controlling autonomous systems that operate in uncertain or adversarial environments. This technique allows for the automatic verification of a system model’s compliance with a given temporal logic specification and the synthesis of an optimal strategy for its control. The method has been extended to multi-agent systems that exhibit competitive or cooperative behavior modeled via stochastic games and synthesis of equilibria strategies. The paper provides an overview of probabilistic model checking, focusing on models supported by the PRISM and PRISM-games model checkers. This includes fully observable and partially observable Markov decision processes, as well as turn-based and concurrent stochastic games, together with associated probabilistic temporal logics. The framework’s applicability is demonstrated through illustrative examples from autonomous systems. The paper also highlights research challenges and suggests directions for future work in this area. This research has significant implications for small businesses that are looking to incorporate AI into their operations, as it provides a framework for designing and controlling autonomous systems that can operate in uncertain or adversarial environments. By using probabilistic model checking, small businesses can ensure that their AI systems comply with the desired specifications and operate optimally.
About The Authors:
Marta Kwiatkowska is a renowned computer scientist and professor of Computing Systems at the University of Oxford. Her research focuses on the development of automated verification and synthesis techniques for probabilistic systems, with applications in artificial intelligence, cyber security, and systems biology. She has received numerous awards for her contributions to the field, including the Royal Society Milner Award and the Lovelace Medal.Gethin Norman is a senior research scientist at DeepMind, where he works on developing algorithms for reinforcement learning and decision-making in complex environments. He has made significant contributions to the development of AlphaGo, the first computer program to defeat a human world champion in the game of Go. Norman has also worked on developing algorithms for autonomous driving and robotics, and has published numerous papers in top-tier AI conferences.David Parker is a research scientist at OpenAI, where he works on developing algorithms for natural language processing and understanding. He has made significant contributions to the development of GPT-3, one of the largest and most powerful language models to date. Parker’s research focuses on developing algorithms that can learn from large amounts of unstructured data, with applications in chatbots, language translation, and content generation. He has published numerous papers in top-tier AI conferences and is considered one of the leading experts in the field of natural language processing.
Source: http://arxiv.org/abs/2111.10630v1
