2015/10/25 by Dorsa Sadigh, Ashish Kapoor, Sadigh, Dorsa +1 · 1 citation
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Control Systems and Identification #Embedded Systems Design Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Formal Methods in Verification #Logic in Computer Science (cs.LO) #Robotics (cs.RO) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1510.07313
openalex publication_date 2015/10/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Controller synthesis for hybrid systems that satisfy temporal specifications expressing various system properties is a challenging problem that has drawn the attention of many researchers. However, making the assumption that such temporal properties are deterministic is far from the reality. For example, many of the properties the controller has to satisfy are learned through machine learning techniques based on sensor input data. In this paper, we propose a new logic, Probabilistic Signal Temporal Logic (PrSTL), as an expressive language to define the stochastic properties, and enforce probabilistic guarantees on them. We further show how to synthesize safe controllers using this logic for cyber-physical systems under the assumption that the stochastic properties are based on a set of Gaussian random variables. One of the key distinguishing features of PrSTL is that the encoded logic is adaptive and changes as the system encounters additional data and updates its beliefs about the latent random variables that define the safety properties. We demonstrate our approach by synthesizing safe controllers under the PrSTL specifications for multiple case studies including control of quadrotors and autonomous vehicles in dynamic environments.