2015/10/28 by Chanyeol Yoo, Yoo, Chanyeol, Călin Belta +1 · 1 citation
Computer Science · #AI-based Problem Solving and Planning #FOS: Computer and information sciences #FOS: Electrical engineering #Formal Methods in Verification #Logic in Computer Science (cs.LO) #Logic, Reasoning, and Knowledge #Robotics (cs.RO) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1510.08474
openalex publication_date 2015/10/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Autonomous agents often operate in uncertain environments where their decisions are made based on beliefs over states of targets. We are interested in controller synthesis for complex tasks defined over belief spaces. Designing such controllers is challenging due to computational complexity and the lack of expressivity of existing specification languages. In this paper, we propose a probabilistic extension to signal temporal logic (STL) that expresses tasks over continuous belief spaces. We present an efficient synthesis algorithm to find a control input that maximises the probability of satisfying a given task. We validate our algorithm through simulations of an unmanned aerial vehicle deployed for surveillance and search missions.