2,923 research outputs found
Temporal Logic Control for Stochastic Linear Systems using Abstraction Refinement of Probabilistic Games
We consider the problem of computing the set of initial states of a dynamical
system such that there exists a control strategy to ensure that the
trajectories satisfy a temporal logic specification with probability 1
(almost-surely). We focus on discrete-time, stochastic linear dynamics and
specifications given as formulas of the Generalized Reactivity(1) fragment of
Linear Temporal Logic over linear predicates in the states of the system. We
propose a solution based on iterative abstraction-refinement, and turn-based
2-player probabilistic games. While the theoretical guarantee of our algorithm
after any finite number of iterations is only a partial solution, we show that
if our algorithm terminates, then the result is the set of satisfying initial
states. Moreover, for any (partial) solution our algorithm synthesizes witness
control strategies to ensure almost-sure satisfaction of the temporal logic
specification. We demonstrate our approach on an illustrative case study.Comment: Technical report accompanying HSCC'15 pape
Conditionally Optimal Algorithms for Generalized B\"uchi Games
Games on graphs provide the appropriate framework to study several central
problems in computer science, such as the verification and synthesis of
reactive systems. One of the most basic objectives for games on graphs is the
liveness (or B\"uchi) objective that given a target set of vertices requires
that some vertex in the target set is visited infinitely often. We study
generalized B\"uchi objectives (i.e., conjunction of liveness objectives), and
implications between two generalized B\"uchi objectives (known as GR(1)
objectives), that arise in numerous applications in computer-aided
verification. We present improved algorithms and conditional super-linear lower
bounds based on widely believed assumptions about the complexity of (A1)
combinatorial Boolean matrix multiplication and (A2) CNF-SAT. We consider graph
games with vertices, edges, and generalized B\"uchi objectives with
conjunctions. First, we present an algorithm with running time , improving the previously known and worst-case bounds. Our algorithm is optimal for dense graphs under (A1).
Second, we show that the basic algorithm for the problem is optimal for sparse
graphs when the target sets have constant size under (A2). Finally, we consider
GR(1) objectives, with conjunctions in the antecedent and
conjunctions in the consequent, and present an -time algorithm, improving the previously known -time algorithm for
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
A multi-paradigm language for reactive synthesis
This paper proposes a language for describing reactive synthesis problems
that integrates imperative and declarative elements. The semantics is defined
in terms of two-player turn-based infinite games with full information.
Currently, synthesis tools accept linear temporal logic (LTL) as input, but
this description is less structured and does not facilitate the expression of
sequential constraints. This motivates the use of a structured programming
language to specify synthesis problems. Transition systems and guarded commands
serve as imperative constructs, expressed in a syntax based on that of the
modeling language Promela. The syntax allows defining which player controls
data and control flow, and separating a program into assumptions and
guarantees. These notions are necessary for input to game solvers. The
integration of imperative and declarative paradigms allows using the paradigm
that is most appropriate for expressing each requirement. The declarative part
is expressed in the LTL fragment of generalized reactivity(1), which admits
efficient synthesis algorithms, extended with past LTL. The implementation
translates Promela to input for the Slugs synthesizer and is written in Python.
The AMBA AHB bus case study is revisited and synthesized efficiently,
identifying the need to reorder binary decision diagrams during strategy
construction, in order to prevent the exponential blowup observed in previous
work.Comment: In Proceedings SYNT 2015, arXiv:1602.0078
How to Handle Assumptions in Synthesis
The increased interest in reactive synthesis over the last decade has led to
many improved solutions but also to many new questions. In this paper, we
discuss the question of how to deal with assumptions on environment behavior.
We present four goals that we think should be met and review several different
possibilities that have been proposed. We argue that each of them falls short
in at least one aspect.Comment: In Proceedings SYNT 2014, arXiv:1407.493
Synthesis of Switching Protocols from Temporal Logic Specifications
We propose formal means for synthesizing switching protocols that determine the sequence in which the modes of a switched system are activated to satisfy certain high-level specifications in linear temporal logic. The synthesized protocols are robust against exogenous disturbances on the continuous dynamics. Two types of finite transition systems, namely under- and over-approximations, that abstract the behavior of the underlying continuous dynamics are defined. In particular, we show that the discrete synthesis problem for an under-approximation can be formulated as a model checking problem, whereas that for an over-approximation can be transformed into a two-player game. Both of these formulations are amenable to efficient, off-the-shelf software tools. By construction, existence of a discrete switching strategy for the discrete synthesis problem guarantees the existence of a continuous switching protocol for the continuous synthesis problem, which can be implemented at the continuous level to ensure the correctness of the nonlinear switched system. Moreover, the proposed framework can be straightforwardly extended to accommodate specifications that require reacting to possibly adversarial external events. Finally, these results are illustrated using three examples from different application domains
Reactivity in decision-form games
In this paper we introduce the reactivity in decision-form games. The concept of reactivity allows us to give a natural concept of rationalizable solution for decision-form games: the solubility by elimination of sub- reactive strategies. This concept of solubility is less demanding than the concept of solubility by elimination of non-reactive strategies (introduced by the author and already studied and applied to economic games). In the work we define the concept of super-reactivity, the preorder of re- activity and, after a characterization of super-reactivity, we are induced to give the concepts of maximal-reactivity and sub-reactivity; the latter definition permits to introduce the iterated elimination of sub-reactive strategies and the solubility of a decision-form game by iterated elimina- tion of sub-reactive strategies. In the paper several examples are devel- oped. Moreover, in the case of normal-form games, the relation between reactivity and dominance is completely revealed.Decision form games; reactivity; dominance
Fully Observable Non-deterministic Planning as Assumption-Based Reactive Synthesis
We contribute to recent efforts in relating two approaches to automatic synthesis, namely, automated planning and discrete reactive synthesis. First, we develop a declarative characterization of the standard “fairness” assumption on environments in non-deterministic planning, and show that strong-cyclic plans are correct solution concepts for fair environments. This complements, and arguably completes, the existing foundational work on non-deterministic planning, which focuses on characterizing (and computing) plans enjoying special “structural” properties, namely loopy but closed policy structures. Second, we provide an encoding suitable for reactive synthesis that avoids the naive exponential state space blowup. To do so, special care has to be taken to specify the fairness assumption on the environment in a succinct manner.Fil: D'ippolito, Nicolás Roque. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Rodriguez, Natalia. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación; ArgentinaFil: Sardina, Sebastian. RMIT University; Australi
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
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