In a variety of applications that encompass adversarial behavior, there is clearly interest in deceiving an adversary about one’s objectives or, alternatively, making it difficult for the adversary to predict one’s strategy to achieve those objectives. In this talk, I will outline recent work on formalizing the notions of deception and unpredictability within control systems. Namely, I will discuss an approach which encodes deception and deceptive strategies through introducing a belief space for an adversary, as well as a belief-induced reward objective. Such a framework makes it possible to consider design of optimal deceptive strategies within the setting of optimal control, where lack of knowledge about the adversary translates into a need to develop robust optimal control policies, or policies based on partial observations. On the other hand, we relate unpredictability of an agent to the total Shannon entropy of the paths that an agent may take to reach its objective, and show that, within the context of Markov decision processes, maximal unpredictability of an agent is achieved through following a policy that results in a maximal total entropy of the induced Markov chain. In parallel with the development of the theory of deception and unpredictability, I will illustrate the introduced notions using a variety of situations that naturally involve adversarial behavior, and show that the policies which are deceptive or generate maximal unpredictability in the sense of theoretical definitions indeed also follow the natural intuition behind these notions.
Recursive algebraic data types (term algebras, ADTs) are one of the
most well-studied theories in logic, and find application in
contexts including functional programming, modelling languages,
proof assistants, and verification. At this point, several
state-of-the-art theorem provers and SMT solvers include tailor-made
decision procedures for ADTs, and version 2.6 of the SMT-LIB
standard includes support for ADTs. We study a relatively simple
approach to decide satisfiability of ADT constraints, the reduction
of ADT constraints to equisatisfiable constraints over uninterpreted
functions (EUF) and linear integer arithmetic (LIA). We show that
the reduction approach gives rise to both decision and Craig
interpolation procedures in ADTs. As an extension, we then consider
ADTs with size constraints, and give a precise characterisation of
the ADTs for which reduction combined with incremental unfolding is
a decision procedure.
A promising technique for the formal verification of
embedded and cyber-physical systems is flow-pipe construction, which
creates a sequence of regions covering all reachable states over time.
Flow-pipe construction methods can check whether specifications are
met for all states, rather than just testing using a finite and
incomplete set of simulation traces. A fundamental challenge when
using flow-pipe construction on high-dimensional systems is the cost
of geometric operations, such as intersection and convex hull. In this
talk, we address this challenge by showing that it is often possible
to remove the need to perform high-dimensional geometric operations by
combining two model transformations, direct time-triggered conversion
and dynamics scaling. Further, we discuss how to make the
overapproximation error in the conversion arbitrarily small. Finally,
we show that our transformation-based approach enables the analysis of
a drivetrain system with up to 51 dimensions.
Bio: Sergiy Bogomolov is on the faculty of the Australian National
University where he leads the Dependable Cyber Physical Systems Lab.
Prior to joining ANU Sergiy was a Postdoctoral Researcher in the
Institute of Science and Technology Austria. Sergiy is broadly
interested in algorithms and techniques to support design and
development workflow of trustworthy and resilient autonomous systems.
For this purpose, he uses and develops techniques on the interface of
cyber-physical systems verification and AI planning. His Ph.D. and
M.Sc. degrees are from the University of Freiburg, Germany.
Increasingly, autonomous systems’ behavior is driven by machine learning, and in particular by (deep) neural networks. One crucial question then is how to effectively test such systems given that they don’t have (complete) specifications or even source code corresponding to some of their critical behavior. Further, adding even more complexity to an already difficult problem, many of these autonomous systems are usually integrated into larger systems, thus possibly leading to undesirable feature interactions. This talk will report on recent work done to address these intricate problems and will attempt to better define the challenges ahead. Examples from the automotive domain will be used to illustrate the main points.
Lionel C. Briand is professor in software verification and validation at the SnT centre for Security, Reliability, and Trust, University of Luxembourg, where he is also the vice-director of the centre. He is currently running multiple collaborative research projects with companies in the automotive, satellite, financial, and legal domains. Lionel has held various engineering, academic, and leading research positions in five other countries before that.
Lionel was elevated to the grade of IEEE Fellow in 2010 for his work on the testing of object-oriented systems. He was granted the IEEE Computer Society Harlan Mills award and the IEEE Reliability Society engineer-of-the-year award for his work on model-based verification and testing, respectively in 2012 and 2013. He received an ERC Advanced grant in 2016 — on the topic of modelling and testing cyber-physical systems — which is the most prestigious individual research grant in the European Union. His research interests include: software testing and verification, model-driven software development, search-based software engineering, and empirical software engineering.
Automata learning is a technique that has successfully been applied in verification, with the automaton type
varying depending on the application domain. Adaptations of automata learning algorithms for increasingly
complex types of automata have to be developed from scratch because there was no abstract theory offering
guidelines. This makes it hard to devise such algorithms, and it obscures their correctness proofs.
We introduce a simple category-theoretic formalism that provides an appropriately abstract foundation for
studying automata learning. Furthermore, our framework establishes formal relations between algorithms for
learning, testing, and minimization. We illustrate its generality with two examples: deterministic and weighted
The notion of comparison between system runs is fundamental in formal verification. This concept is implicitly present in the verification of qualitative systems, and is more pronounced in the verification of quantitative systems. In this work, we identify a novel mode of comparison in quantitative systems: the online comparison of the aggregate values of two sequences of quantitative weights. This notion is embodied by comparator automata (comparators, in short), a new class of automata that read two infinite sequences of weights synchronously and relate their aggregate values.
We show that comparators that are finite-state and accept by the Büchi condition lead to generic algorithms for a number of well-studied problems, including the quantitative inclusion and winning strategies in quantitative graph games with incomplete information, as well as related non-decision problems, such as obtaining a finite representation of all counterexamples in the quantitative inclusion problem.
We study comparators for two aggregate functions: discounted-sum and limit-average. We prove that the discounted-sum comparator is omega-regular for all integral discount factors.
Not every aggregate function, however, has an omega-regular comparator. Specifically, we show that the language of sequence-pairs for which limit-average aggregates exist is neither omega-regular nor omega-context-free. Given this result, we introduce the notion of prefix-average as a relaxation of limit-average aggregation, and show that it admits omega-context-free comparators.
In this talk, I will investigate the expressive power and the algorithmic properties of weighted expressions, which define functions from finite words to integers. First, I will consider a slight extension of an expression formalism, introduced by Chatterjee. et. al. in the context of infinite words (called Mean-Payoff expressions), by which to combine values given by unambiguous (max,+)-automata, using Presburger arithmetic. Important decision problems such as emptiness, universality and comparison are PSpace-Complete for these expressions. I will then investigate the extension of these expressions with Kleene star. This allows to iterate an expression over smaller fragments of the input word, and to combine the results by taking their iterated sum. Unfortunately, the decision problems turn out to be undecidable. So, the goal of this talk is to highlights a still expressive class of expression and sketch is decidability especially by considering a new class of automata: Weighted chop automata.
In this talk we present token ﬂow based synthesis of Petri nets from labelled prime event structures (LPES). For this purpose we use unfolding semantics based on token ﬂows.
First, given a ﬁnite LPES, it is shown how to synthesize a Petri net with acyclic behavior, such that the unfolding of the synthesized net preserves common preﬁxes and concurrency of runs of the LPES. The partial language of this unfolding is the minimal partial language of an unfolding of a Petri net, which includes the partial language of LPES. This result extend the class of non-sequential behaviour,for which Petri nets can be synthesized, because in comparison to a partial language, an LPES enables to deﬁne which common history of runs should be preserved in the synthesized net.
Second, given an inﬁnite LPES represented by some ﬁnite preﬁx equipped with a cutting context and cut-off events it is shown how to synthesize a bounded Petri net, such that the unfolding of the synthesized net preserves common preﬁxes and concurrency of runs of the LPES. The partial language of this unfolding is the minimal partial language of an unfolding of a Petri net, which includes the partial language of LPES. This result extends the class of non-sequential behaviour, for which Petri nets can be synthesized, because ﬁnite representations of inﬁnite LPES by a ﬁnite preﬁx equipped with a cutting context and cut-off events are more expressive than ﬁnite representations of inﬁnite partial languages by terms.
Parity games are deceptively simple two-player games on directed graphs
labeled with numbers.
Parity games have important practical applications in formal
verification and synthesis, especially to solve the model-checking problem
of the modal mu-calculus. They are also interesting from the theory
perspective, because they are widely believed to admit a polynomial
solution, but so far no such algorithm is known. In recent years, a number
of new algorithms and improvements to existing algorithms have been
In this talk, we introduce parity games in an accessible way and discuss
why they are so interesting. We present various solutions that have been
proposed over the years. We also present a comprehensive empirical evaluation
of modern parity game algorithms and solvers, both on real world benchmarks
and randomly generated games.