This week (9.09-11.09) the Seventeenth International Conference on Computability and Complexity in Analysis (CCA) is taking place virtually via Microsoft Teams (originally planned for Bologna, Italy).
Arno Pauly is among the invited speakers. He will give a talk on Weinrauch complexity this Friday.
A number of Swansea theory group members are attending the CCC 2020 (Continuity, Computability, Constructivity – From Logic to Algorithms) conference, taking place from 31 August to 4 September via Zoom. Their work will be presented in the following talks:
CCC conference series are linked to the CID project that Swansea University actively participates in the recent years. This year the conference features talks by a number of invited speakers:
Association Computability in Europe
Continuing the strong connections between the Swansea Theoretical Computer Science group and the Association Computability in Europe, Arno Pauly just joined the executive committee of the association. Arnold Beckmann is also involved in the CiE governance, serving on its council.
Our PhD student Tonicha Crook attended her first CIE conference and gave an interesting talk on The Weihrauch degree of finding Nash Equilibria in multiplayer games.
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.
25th INTERNATIONAL WORKSHOP ON ALGEBRAIC DEVELOPMENT TECHNIQUES 2020
Our colleague Arno Pauly is on the Programme Committee of the Computability Theory and its Applications seminar series, which currently is taking place virtually.
The seminar now has a YouTube channel where you can find the recordings of past talks you may have missed. The information on future talks, the timings of those, and the links to the videos are all available on the webpage here:
CTA Seminar – Computability
The goal of this endeavor is to run a seminar on the platform Zoom on a weekly basis, perhaps with alternating time slots each of which covers at least three out of four of Europe, North America, Asia, and New Zealand/Australia.
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)
Senior Researcher, Software Analytics Research Group, Information Technology Reseaerch Institute, AIST