In spite of the COVID-19 outbreak, we are glad to host BCTCS 2020 starting today, even if virtually. For more information on invited and contributed talks, check the official BCTCS 2020 website.
Today Swansea is hosting the first virtual WADT, and you are gently invited to participate.
The virtual WADT is part of the 25th International Workshop on Algebraic Development Techniques 2020, which hopefully will still happen as a physical meeting in autumn this year. The algebraic approach to system specification encompasses many aspects of the formal design of software systems. Originally born as a formal method for reasoning about abstract data types, it now covers new specification frameworks and programming paradigms (such as object-oriented, aspect oriented, agent-oriented, logic and higher-order functional programming) as well as a wide range of application areas (including information systems, concurrent, distributed and mobile systems).
The workshop takes place under the auspices of IFIP WG 1.3.
Please see below for the programme.
Due to COVID-19 outbreak across the world, we need to move our theory seminars online to ZOOM. We are staying on track with the schedule and Yoriyuki Yamagata will give his talk tomorrow at 2pm.
Topic: Falsification of Cyber-Physical Systems Using Deep Reinforcement Learning.
Abstract: “Falsification” is a method to find a system input or parameter (counter-example) which causes a behavior violating a given specification (usually given by metric or signal temporal logic). Because the correctness of a complex CPS is difficult to be proven, falsification is more practical approach than full verification. A counter-example found by falsification can be used for debugging and testing. Failure of falsification does not generally mean the correctness of the system, but suggests it in some degree. “Robustness guided falsification” is an approach of falsification. “Robustness” is a numerical measure of how robustly a formula holds. If robustness becomes negative, the formula is false. Therefore, minimizing robustness can lead falsification of a formula.
In this talk, we introduce a method to recast robustness guided falsification to a “reinforcement learning problem”. Reinforcement learning is a machine learning technique in which an agent finds a law of an interacting environment and maximizes a reward. We implement our method using “deep reinforcement leaning”, in which deep neural networks are used, and present a case study to explore its effectiveness. (This work is a collaboration with Shuang Liu, Takumi Akazaki, Yihai Duan, Jianye Hao)
We are looking forward to welcome Paul Schafer, who will be visiting Swansea University on 18 to 23 January.
Abstract: Imaginary cubes are three-dimensional objects with square projections in three orthogonal ways just as a cube has. How many different kinds of imaginary cubes can you imagine? In this talk we show that there are 16 kinds of minimal convex imaginary cubes which includes regular tetrahedron, cuboctahedron, and two objects that we call H and T. As we will explain, H and T have a lot of beautiful mathematical properties related to tiling, fractal, and higher-dimensional geometry, and based on these properties, the speaker has designed a puzzle, constructed three-dimensional math-art objects, and used them for educations at various levels from elemental school to graduate schools. In this talk, I will explain mathematics of imaginary cubes and show the activities I have been engaged in. I will carry a couple of copies of the puzzle and some of the math-art objects so that the audience can enjoy them while I am staying in Swansea.
Following the Summer School, we are really proud to host the 2nd Proof Society Workshop. The workshop was an opportunity to listen to a lot of interesting invited and contributed talks on proof theory and various areas of its application:
Adam Wyner: Computational Law – The Case of Autonomous Vehicles
Yong Cheng: Exploring the incompleteness phenomenon
Matthias Baaz: Towards a Proof Theory for Henkin Quantifiers
Sonia Marin: On cut-elimination for non-wellfounded proofs: the case of PDL
Gilles Dowek: Logical frameworks, reverse mathematics, and formal proofs translation
Benjamin Ralph: What is a combinatorial proof system?
William Stirton: Ordinal assignments correlated with notions of reduction
Oliver Kullmann: Practical proof theory: practical versions of Extended Resolution
Anton Setzer and Ulrich Berger on behalf of Ralph Matthes: Martin Hofmann’s case for non-strictly positive data types – reloaded
Laura Crosilla: Philosophy of mathematics and proof theory
Takako Nemoto: Recursion Theory in Constructive Mathematics
Arno Pauly: Combinatorial principles equivalent to weak induction
Antonina Kolokolova: The proof complexity of reasoning over richer domains
Joost Joosten: The reduction property revisited
Helmut Schwichtenberg: Computational content of proofs
Thanks to all the speaker and participants and we hope to see you all again soon.
Big thanks to all the speakers and the participants for joining our Summer School. We hope to see you again during the future events by the Proof Society.
We are very glad to welcome all the participants of the 2nd International Summer School on Proof Theory.
The first day began with a lecture on Universal Proof Theory by Rosalie Iemhoff, followed by Wolfram Pohlers‘ talk on Ordinal Analysis, Bounded Arithmetic lecture from our own Arnold Beckmann, introduction to Proof Mining by Paulo Oliva, Paola Bruscoli’s talk on Structural Proof Theory and Anton Setzer’s lecture on MLTT.
We were lucky with both the lovely weather and the fact that Bay Campus is located right at the seafront, so the evening brought a nice treat for everyone in a form of a BBQ at the beach. Big thanks to Arnold, Faron for their grilling and Ulrich, Rosalie, Monika, Arved, Aled, Anton, Olga and everyone else who helped with the organisation.
Federico Cerutti from Cardiff University is visiting us this Thursday. He will give a talk on Probabilistic Logic Programming with Beta-Distributed Random Variables.
Abstract: We enable aProbLog—a probabilistic logical programming approach—to reason in presence of uncertain probabilities represented as Beta-distributed random variables. We achieve the same performance of state-of-the-art algorithms for highly specified and engineered domains, while simultaneously we maintain the flexibility offered by aProbLog in handling complex relational domains.
Our motivation is that faithfully capturing the distribution of probabilities is necessary to compute an expected utility for effective decision making under uncertainty: unfortunately, these probability distributions can be highly uncertain due to sparse data. To understand and accurately manipulate such probability distributions we need a well-defined theoretical framework that is provided by the Beta distribution, which specifies a distribution of probabilities representing all the possible values of a probability when the exact value is unknown.