Decomposing GR(1) Games with Singleton Liveness Guarantees for Efficient Synthesis

Temporal logic based synthesis approaches are often used to find trajectories that are correct-by-construction for tasks in systems with complex behavior. Some examples of such tasks include synchronization for multi-agent hybrid systems, reactive motion planning for robots. However, the scalability of such approaches is of concern and at times a bottleneck when transitioning from theory to practice. In this paper, we identify a class of problems in the GR(1) fragment of linear-time temporal logic (LTL) where the synthesis problem allows for a decomposition that enables easy parallelization. This decomposition also reduces the alternation depth, resulting in more efficient synthesis. A multi-agent robot gridworld example with coordination tasks is presented to demonstrate the application of the developed ideas and also to perform empirical analysis for benchmarking the decomposition-based synthesis approach

Low-Effort Specification Debugging and Analysis

Reactive synthesis deals with the automated construction of implementations of reactive systems from their specifications. To make the approach feasible in practice, systems engineers need effective and efficient means of debugging these specifications. In this paper, we provide techniques for report-based specification debugging, wherein salient properties of a specification are analyzed, and the result presented to the user in the form of a report. This provides a low-effort way to debug specifications, complementing high-effort techniques including the simulation of synthesized implementations. We demonstrate the usefulness of our report-based specification debugging toolkit by providing examples in the context of generalized reactivity(1) synthesis.Comment: In Proceedings SYNT 2014, arXiv:1407.493

Robust Model Predictive Control for Signal Temporal Logic Synthesis

Most automated systems operate in uncertain or adversarial conditions, and have to be capable of reliably reacting to changes in the environment. The focus of this paper is on automatically synthesizing reactive controllers for cyber-physical systems subject to signal temporal logic (STL) specifications. We build on recent work that encodes STL specifications as mixed integer linear constraints on the variables of a discrete-time model of the system and environment dynamics. To obtain a reactive controller, we present solutions to the worst-case model predictive control (MPC) problem using a suite of mixed integer linear programming techniques. We demonstrate the comparative effectiveness of several existing worst-case MPC techniques, when applied to the problem of control subject to temporal logic specifications; our empirical results emphasize the need to develop specialized solutions for this domain

Abstractions and sensor design in partial-information, reactive controller synthesis

Automated synthesis of reactive control protocols from temporal logic specifications has recently attracted considerable attention in various applications in, for example, robotic motion planning, network management, and hardware design. An implicit and often unrealistic assumption in this past work is the availability of complete and precise sensing information during the execution of the controllers. In this paper, we use an abstraction procedure for systems with partial observation and propose a formalism to investigate effects of limitations in sensing. The abstraction procedure enables the existing synthesis methods with partial observation to be applicable and efficient for systems with infinite (or finite but large number of) states. This formalism enables us to systematically discover sensing modalities necessary in order to render the underlying synthesis problems feasible. We use counterexamples, which witness unrealizability potentially due to the limitations in sensing and the coarseness in the abstract system, and interpolation-based techniques to refine the model and the sensing modalities, i.e., to identify new sensors to be included, in such synthesis problems. We demonstrate the method on examples from robotic motion planning.Comment: 9 pages, 4 figures, Accepted at American Control Conference 201

Correct-by-synthesis reinforcement learning with temporal logic constraints

We consider a problem on the synthesis of reactive controllers that optimize some a priori unknown performance criterion while interacting with an uncontrolled environment such that the system satisfies a given temporal logic specification. We decouple the problem into two subproblems. First, we extract a (maximally) permissive strategy for the system, which encodes multiple (possibly all) ways in which the system can react to the adversarial environment and satisfy the specifications. Then, we quantify the a priori unknown performance criterion as a (still unknown) reward function and compute an optimal strategy for the system within the operating envelope allowed by the permissive strategy by using the so-called maximin-Q learning algorithm. We establish both correctness (with respect to the temporal logic specifications) and optimality (with respect to the a priori unknown performance criterion) of this two-step technique for a fragment of temporal logic specifications. For specifications beyond this fragment, correctness can still be preserved, but the learned strategy may be sub-optimal. We present an algorithm to the overall problem, and demonstrate its use and computational requirements on a set of robot motion planning examples.Comment: 8 pages, 3 figures, 2 tables, submitted to IROS 201