111,898 research outputs found
Sound and Automated Synthesis of Digital Stabilizing Controllers for Continuous Plants
Modern control is implemented with digital microcontrollers, embedded within
a dynamical plant that represents physical components. We present a new
algorithm based on counter-example guided inductive synthesis that automates
the design of digital controllers that are correct by construction. The
synthesis result is sound with respect to the complete range of approximations,
including time discretization, quantization effects, and finite-precision
arithmetic and its rounding errors. We have implemented our new algorithm in a
tool called DSSynth, and are able to automatically generate stable controllers
for a set of intricate plant models taken from the literature within minutes.Comment: 10 page
Blending Inductive and Deductive Processes in the English/Language Arts Classroom
This article attempts to demonstrate how the inductive and deductive processing modes function together. Educational models associated with an inductive learning process provide a great opportunity for students to assess their accountability in the learning process. However, the lessons gleaned from such an inductive approach can be more insight-provoking when a synthesis of (or at least access to) deductive processing occurs. The topic is presented in two parts: The first part constitutes a review of the inductive/deductive dynamic through research, study, and theory across multiple learning contexts. The second part presents a qualitative study and data examples for the purposes of theoretically and practically applying various deductive/inductive processes to an English/Language Arts context
Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis
Even with impressive advances in automated formal methods, certain problems
in system verification and synthesis remain challenging. Examples include the
verification of quantitative properties of software involving constraints on
timing and energy consumption, and the automatic synthesis of systems from
specifications. The major challenges include environment modeling,
incompleteness in specifications, and the complexity of underlying decision
problems.
This position paper proposes sciduction, an approach to tackle these
challenges by integrating inductive inference, deductive reasoning, and
structure hypotheses. Deductive reasoning, which leads from general rules or
concepts to conclusions about specific problem instances, includes techniques
such as logical inference and constraint solving. Inductive inference, which
generalizes from specific instances to yield a concept, includes algorithmic
learning from examples. Structure hypotheses are used to define the class of
artifacts, such as invariants or program fragments, generated during
verification or synthesis. Sciduction constrains inductive and deductive
reasoning using structure hypotheses, and actively combines inductive and
deductive reasoning: for instance, deductive techniques generate examples for
learning, and inductive reasoning is used to guide the deductive engines.
We illustrate this approach with three applications: (i) timing analysis of
software; (ii) synthesis of loop-free programs, and (iii) controller synthesis
for hybrid systems. Some future applications are also discussed
Validity-Guided Synthesis of Reactive Systems from Assume-Guarantee Contracts
Automated synthesis of reactive systems from specifications has been a topic
of research for decades. Recently, a variety of approaches have been proposed
to extend synthesis of reactive systems from proposi- tional specifications
towards specifications over rich theories. We propose a novel, completely
automated approach to program synthesis which reduces the problem to deciding
the validity of a set of forall-exists formulas. In spirit of IC3 / PDR, our
problem space is recursively refined by blocking out regions of unsafe states,
aiming to discover a fixpoint that describes safe reactions. If such a fixpoint
is found, we construct a witness that is directly translated into an
implementation. We implemented the algorithm on top of the JKind model checker,
and exercised it against contracts written using the Lustre specification
language. Experimental results show how the new algorithm outperforms JKinds
already existing synthesis procedure based on k-induction and addresses
soundness issues in the k-inductive approach with respect to unrealizable
results.Comment: 18 pages, 5 figures, 2 table
Invariant Synthesis for Incomplete Verification Engines
We propose a framework for synthesizing inductive invariants for incomplete
verification engines, which soundly reduce logical problems in undecidable
theories to decidable theories. Our framework is based on the counter-example
guided inductive synthesis principle (CEGIS) and allows verification engines to
communicate non-provability information to guide invariant synthesis. We show
precisely how the verification engine can compute such non-provability
information and how to build effective learning algorithms when invariants are
expressed as Boolean combinations of a fixed set of predicates. Moreover, we
evaluate our framework in two verification settings, one in which verification
engines need to handle quantified formulas and one in which verification
engines have to reason about heap properties expressed in an expressive but
undecidable separation logic. Our experiments show that our invariant synthesis
framework based on non-provability information can both effectively synthesize
inductive invariants and adequately strengthen contracts across a large suite
of programs
Synthesis equivalence of triples
This working paper describes a framework for compositional supervisor synthesis, which is applicable to all discrete event systems modelled as a set of deterministic automata. Compositional synthesis exploits the modular structure of the input model, and therefore works best for models consisting of a large number of small automata. State-space explosion is mitigated by the use of abstraction to simplify individual components, and the property of synthesis equivalence guarantees that the final synthesis result is the same as it would have been for the non-abstracted model. The working paper describes synthesis equivalent abstractions and shows their use in an algorithm to compute supervisors efficiently. The algorithm has been implemented in the DES software tool Supremica and successfully computes modular supervisors, even for systems with more than 1014 reachable states, in less than 30 seconds
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