1,001 research outputs found
Interpolant-Based Transition Relation Approximation
In predicate abstraction, exact image computation is problematic, requiring
in the worst case an exponential number of calls to a decision procedure. For
this reason, software model checkers typically use a weak approximation of the
image. This can result in a failure to prove a property, even given an adequate
set of predicates. We present an interpolant-based method for strengthening the
abstract transition relation in case of such failures. This approach guarantees
convergence given an adequate set of predicates, without requiring an exact
image computation. We show empirically that the method converges more rapidly
than an earlier method based on counterexample analysis.Comment: Conference Version at CAV 2005. 17 Pages, 9 Figure
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
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
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Oracle-Guided Design and Analysis of Learning-Based Cyber-Physical Systems
We are in world where autonomous systems, such as self-driving cars, surgical robots, robotic manipulators are becoming a reality. Such systems are considered \textit{safety-critical} since they interact with humans on a regular basis. Hence, before such systems can be integrated into our day to day life, we need to guarantee their safety. Recent success in machine learning (ML) and artificial intelligence (AI) has led to an increase in their use in real world robotic systems. For example, complex perception modules in self-driving cars and deep reinforcement learning controllers in robotic manipulators. Although powerful, they introduce an additional level of complexity when it comes to the formal analysis of autonomous systems. In this thesis, such systems are designated as Learning-Based Cyber-Physical Systems~(LB-CPS). In this thesis, we take inspiration from the Oracle-Guided Inductive Synthesis~(OGIS) paradigm to develop frameworks which can aid in achieving formal guarantees in different stages of an autonomous system design and analysis pipeline. Furthermore, we show that to guarantee the safety of LB-CPS, the design (synthesis) and analysis (verification) must consider feedback from the other. We consider five important parts of the design and analysis process and show a strong coupling among them, namely (i) Robust Control Synthesis from High Level Safety Specifications; (ii) Diagnosis and Repair of Safety Requirements for Control Synthesis; (iii) Counter-example Guided Data Augmentation for training high-accuracy ML models; (iv) Simulation-Guided Falsification and Verification against Adversarial Environments; and (v) Bridging Model and Real-World Gap. Finally, we introduce a software toolkit \verifai{} for the design and analysis of AI based systems, which was developed to provide a common formal platform to implement design and analysis frameworks for LB-CPS
Model-based compositional verification approaches and tools development for cyber-physical systems
The model-based design for embedded real-time systems utilizes the veriable reusable components and proper architectures, to deal with the verification scalability problem caused by state-explosion. In this thesis, we address verification approaches for both low-level individual component correctness and high-level system correctness, which are equally important under this scheme. Three prototype tools are developed, implementing our approaches and algorithms accordingly.
For the component-level design-time verification, we developed a symbolic verifier, LhaVrf, for the reachability verification of concurrent linear hybrid systems (LHA). It is unique in translating a hybrid automaton into a transition system that preserves the discrete transition structure, possesses no continuous dynamics, and preserves reachability of discrete states. Afterward, model-checking is interleaved in the counterexample fragment based specification relaxation framework. We next present a simulation-based bounded-horizon reachability analysis approach for the reachability verification of systems modeled by hybrid automata (HA) on a run-time basis. This framework applies a dynamic, on-the-fly, repartition-based error propagation control method with the mild requirement of Lipschitz continuity on the continuous dynamics. The novel features allow state-triggered discrete jumps and provide eventually constant over-approximation error bound for incremental stable dynamics. The above approaches are implemented in our prototype verifier called HS3V. Once the component properties are established, the next thing is to establish the system-level properties through compositional verication. We present our work on the role and integration of quantier elimination (QE) for property composition and verication. In our approach, we derive in a single step, the strongest system property from the given component properties for both time-independent and time-dependent scenarios. The system initial condition can also be composed, which, alongside the strongest system property, are used to verify a postulated system property through induction. The above approaches are implemented in our prototype tool called ReLIC
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