The synthesis of logical controllers that guarantee desired specifications is a central problem in the design of cyber-physical systems (CPS). In practice, such guarantees rely on assumptions about how the controller interacts with its environment. These assumptions restrict environment behavior to make synthesis feasible, but in existing approaches they are often overly restrictive, leading to conservative designs and limiting the range of behaviors that systems can safely accommodate. This thesis rethinks the role of assumptions in logical controller synthesis by emphasizing their permissiveness---the ability to capture a wide range of admissible environment behaviors. We study permissive assumptions in two key settings: (a) interactions among multiple discrete components in distributed systems, and (b) interactions between high-level logical controllers and low-level physical dynamics in hybrid systems. In both settings, we develop theoretical and algorithmic foundations for computing and exploiting permissive assumptions to enable new design paradigms for logical controller synthesis. For distributed systems, we define permissiveness as capturing all cooperative behaviors of other components that enable a controller to satisfy its specification. We present an algorithm for computing such assumptions in monolithic systems and extend it to distributed systems via a negotiation-based framework that iteratively constructs permissive assume-guarantee contracts for each component. These contracts enable decentralized synthesis and are applied to human-robot interaction, allowing robots to cooperate with humans whenever possible and request cooperation only when necessary. For hybrid systems, we utilize permissive assumptions on the plant model---the abstract representation of physical dynamics---to address three key challenges. To enable seamless adaptation of controllers to changing logical contexts, i. e. , changes in high-level goals or tasks, we introduce a novel synthesis framework that utilizes persistent live groups, a class of assumptions capturing liveness properties of continuous dynamics. To improve scalability to large or uncertain plant models, we develop universal controllers where decisions are conditioned on branching-time assumptions called prophecies, which are learned from representative models and efficiently verified at runtime on unseen plant models. Finally, to enhance robustness under uncertainty or partial violations of assumptions on the plant model, we introduce a robust semantics for branching-time temporal logics, enabling formal reasoning about controller behavior under such violations. Overall, this work enables correctness-by-construction synthesis while avoiding unnecessary conservatism, resulting in CPS that are more robust, scalable, and responsive.
Satya Prakash Nayak (Thu,) studied this question.