Alexei Lisitsa, Mateo Salles, Alexei Vernitski
We report on our success on solving the problem of untangling braids up to length 20 and width 4. We use feed-forward neural networks in the framework of reinforcement learning to train the agent to choose Reidemeister moves to untangle braids in the minimal number of moves.
Yinzheng Zhong, Alexei Lisitsa
In this paper, we introduce the transition-based feature generator (TFGen) technique, which reads general activity data with attributes and generates step-by-step generated data. The activity data may consist of network activity from packets, system calls from processes or classified activity from surveillance cameras. TFGen processes data online and will generate data with encoded historical data for each incoming activity with high computational efficiency. The input activities may concurrently originate from distinct traces or channels. The technique aims to address issues such as domain-independent applicability, the ability to discover global process structures, the encoding of time-series data, and online processing capability.
Alexei Lisitsa, Mateo Salles, Alexei Vernitski
We use machine learning to classify examples of braids (or flat braids) as trivial or non-trivial. Our ML takes form of supervised learning using neural networks (multilayer perceptrons). When they achieve good results in classification, we are able to interpret their structure as mathematical conjectures and then prove these conjectures as theorems. As a result, we find new convenient invariants of braids, including a complete invariant of flat braids.
Oliver Dippel, Alexei Lisitsa, Bei Peng
Transformers have demonstrated exceptional in-context learning (ICL) capabilities, enabling applications across natural language processing, computer vision, and sequential decision-making. In reinforcement learning, ICL reframes learning as a supervised problem, facilitating task adaptation without parameter updates. Building on prior work leveraging transformers for sequential decision-making, we propose Heuristic Transformer (HT), an in-context reinforcement learning (ICRL) approach that augments the in-context dataset with a belief distribution over rewards to achieve better decision-making. Using a variational auto-encoder (VAE), a low-dimensional stochastic variable is learned to represent the posterior distribution over rewards, which is incorporated alongside an in-context dataset and query states as prompt to the transformer policy. We assess the performance of HT across the Darkroom, Miniworld, and MuJoCo environments, showing that it consistently surpasses comparable baselines in terms of both effectiveness and generalization. Our method presents a promising direction to bridge the gap between belief-based augmentations and transformer-based decision-making.
Manolis Pitsikalis, Thanh-Toan Do, Alexei Lisitsa et al.
The shipping industry is an important component of the global trade and economy, however in order to ensure law compliance and safety it needs to be monitored. In this paper, we present a novel Ship Type classification model that combines vessel transmitted data from the Automatic Identification System, with vessel imagery. The main components of our approach are the Faster R-CNN Deep Neural Network and a Neuro-Fuzzy system with IF-THEN rules. We evaluate our model using real world data and showcase the advantages of this combination while also compare it with other methods. Results show that our model can increase prediction scores by up to 15.4\% when compared with the next best model we considered, while also maintaining a level of explainability as opposed to common black box approaches.
Abdullah Khan, Alexei Vernitski, Alexei Lisitsa
We use reinforcement learning to tackle the problem of untangling braids. We experiment with braids with 2 and 3 strands. Two competing players learn to tangle and untangle a braid. We interface the braid untangling problem with the OpenAI Gym environment, a widely used way of connecting agents to reinforcement learning problems. The results provide evidence that the more we train the system, the better the untangling player gets at untangling braids. At the same time, our tangling player produces good examples of tangled braids.
Alexei Lisitsa, Andrei P. Nemytykh
The previous VPT 2020 workshop was organized in honour of Professor Alberto Pettorossi on the occasion of his academic retirement from Università di Roma Tor Vergata. Due to the pandemic the VPT 2020 meeting was cancelled but its proceeding have already appeared in the EPTCS 320 volume. The joint VPT-20-21 event has subsumed the original programme of VPT 2020 and provided an opportunity to meet and celebrate the achievements of Professor Alberto Pettorossi; its programme was further expanded with the newly submitted presentations for VPT 2021. The aim of the VPT workshop series is to provide a forum where people from the areas of program transformation and program verification can fruitfully exchange ideas and gain a deeper understanding of the interactions between those two fields.
Manolis Pitsikalis, Alexei Lisitsa, Shan Luo
Event definitions in Complex Event Processing systems are constrained by the expressiveness of each system's language. Some systems allow the definition of instantaneous complex events, while others allow the definition of durative complex events. While there are exceptions that offer both options, they often lack of intervals relations such as those specified by the Allen's interval algebra. In this paper, we propose a new logic based temporal phenomena definition language, specifically tailored for Complex Event Processing, that allows the representation of both instantaneous and durative phenomena and the temporal relations between them. Moreover, we demonstrate the expressiveness of our proposed language by employing a maritime use case where we define maritime events of interest. Finally, we analyse the execution semantics of our proposed language for stream processing and introduce the `Phenesthe' implementation prototype.
Marie Farrell, Rafael C. Cardoso, Louise A. Dennis et al.
Ensuring that autonomous space robot control software behaves as it should is crucial, particularly as software failure in space often equates to mission failure and could potentially endanger nearby astronauts and costly equipment. To minimise mission failure caused by software errors, we can utilise a variety of tools and techniques to verify that the software behaves as intended. In particular, distinct nodes in a robotic system often require different verification techniques to ensure that they behave as expected. This paper introduces a method for integrating the various verification techniques that are applied to robotic software, via a First-Order Logic (FOL) specification that captures each node's assumptions and guarantees. These FOL specifications are then used to guide the verification of the individual nodes, be it by testing or the use of a formal method. We also outline a way of measuring our confidence in the verification of the entire system in terms of the verification techniques used.
Ge Chu, Alexei Lisitsa
Penetration testing (or pentesting) is one of the widely used and important methodologies to assess the security of computer systems and networks. Traditional pentesting relies on the domain expert knowledge and requires considerable human effort all of which incurs a high cost. The automation can significantly improve the efficiency, availability and lower the cost of penetration testing. Existing approaches to the automation include those which map vulnerability scanner results to the corresponding exploit tools, and those addressing the pentesting as a planning problem expressed in terms of attack graphs. Due to mainly non-interactive processing, such solutions can deal effectively only with static and simple targets. In this paper, we propose an automated penetration testing approach based on the belief-desire-intention (BDI) agent model, which is central in the research on agent-based processing in that it deals interactively with dynamic, uncertain and complex environments. Penetration testing actions are defined as a series of BDI plans and the BDI reasoning cycle is used to represent the penetration testing process. The model is extensible and new plans can be added, once they have been elicited from the human experts. We report on the results of testing of proof of concept BDI-based penetration testing tool in the simulated environment.
Alexei Lisitsa, Andrei P. Nemytykh, Maurizio Proietti
This volume contains the proceedings of the Fifth International Workshop on Verification and Program Transformation (VPT 2017). The workshop took place in Uppsala, Sweden, on April 29th, 2017, affiliated with the European Joint Conferences on Theory and Practice of Software (ETAPS). The aim of the VPT workshop series is to provide a forum where people from the areas of program transformation and program verification can fruitfully exchange ideas and gain a deeper understanding of the interactions between those two fields. Seven papers were presented at the workshop. Additionally, three invited talks were given by Javier Esparza (Technische Universität München, Germany), Manuel Hermenegildo (IMDEA Software Institute, Madrid, Spain), and Alexey Khoroshilov (Linux Verification Center, ISPRAS, Moscow, Russia).
Geoff Hamilton, Alexei Lisitsa, Andrei P. Nemytykh
This volume contains the revised versions of papers presented at the Fourth International Workshop on Verification and Program Transformation (VPT 2016) on April 2, 2016 in Eindhoven, The Netherlands. The workshop is an event of the European Joint Conferences on Theory and Practice of Software (ETAPS 2016). The aim of the VPT workshops is to provide a forum where people from the area of program transformation and the area of program verification can fruitfully exchange ideas and gain a deeper understanding of the interactions between those two fields. The research papers which have been recently published in those fields, show that the interactions are very beneficial and, indeed, go both ways. In one direction, methods and tools developed in the field of program transformation, such as partial deduction, partial evaluation, fold/unfold transformations, and supercompilation, have all been applied with success for the verification of systems, and in particular, the verification of infinite state and parameterized systems. In the other direction, methods developed in program verification, such as model checking, abstract interpretation, SAT and SMT solving, and automated theorem proving, have been used to enhance program transformation techniques, thereby making these techniques more powerful and useful in practice.
Alexei Lisitsa, Andrei P. Nemytykh, Alberto Pettorossi
This volume contains the papers selected among those which were presented at the 3rd International Workshop on Verification and Program Transformation (VPT 2015) held in London, UK, on April 11th, 2015. Previous editions of the Workshop were held at Saint-Petersburg (Russia) in 2013, and Vienna (Austria) in 2014. Those papers show that methods and tools developed in the field of program transformation such as partial evaluation and fold/unfold transformations, and supercompilation, can be applied in the verification of software systems. They also show how some program verification methods, such as model checking techniques, abstract interpretation, SAT and SMT solving, and automated theorem proving, can be used to enhance program transformation techniques, thereby making these techniques more powerful and useful in practice.