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Catch Me If You Can: Improving Adversaries in Cyber-Security With Q-Learning Algorithms 如果你能抓住我:用q -学习算法提高网络安全中的对手
International Conference on Agents and Artificial Intelligence Pub Date : 2023-02-07 DOI: 10.5220/0011684500003393
Arti Bandhana, Ondrej Lukás, S. García, T. Kroupa
{"title":"Catch Me If You Can: Improving Adversaries in Cyber-Security With Q-Learning Algorithms","authors":"Arti Bandhana, Ondrej Lukás, S. García, T. Kroupa","doi":"10.5220/0011684500003393","DOIUrl":"https://doi.org/10.5220/0011684500003393","url":null,"abstract":"The ongoing rise in cyberattacks and the lack of skilled professionals in the cybersecurity domain to combat these attacks show the need for automated tools capable of detecting an attack with good performance. Attackers disguise their actions and launch attacks that consist of multiple actions, which are difficult to detect. Therefore, improving defensive tools requires their calibration against a well-trained attacker. In this work, we propose a model of an attacking agent and environment and evaluate its performance using basic Q-Learning, Naive Q-learning, and DoubleQ-Learning, all of which are variants of Q-Learning. The attacking agent is trained with the goal of exfiltrating data whereby all the hosts in the network have a non-zero detection probability. Results show that the DoubleQ-Learning agent has the best overall performance rate by successfully achieving the goal in $70%$ of the interactions.","PeriodicalId":174978,"journal":{"name":"International Conference on Agents and Artificial Intelligence","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116755665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Towards Modelling and Verification of Social Explainable AI 面向社会可解释AI的建模与验证
International Conference on Agents and Artificial Intelligence Pub Date : 2023-02-02 DOI: 10.48550/arXiv.2302.01063
Damian Kurpiewski, W. Jamroga, Teofil Sidoruk
{"title":"Towards Modelling and Verification of Social Explainable AI","authors":"Damian Kurpiewski, W. Jamroga, Teofil Sidoruk","doi":"10.48550/arXiv.2302.01063","DOIUrl":"https://doi.org/10.48550/arXiv.2302.01063","url":null,"abstract":"Social Explainable AI (SAI) is a new direction in artificial intelligence that emphasises decentralisation, transparency, social context, and focus on the human users. SAI research is still at an early stage. Consequently, it concentrates on delivering the intended functionalities, but largely ignores the possibility of unwelcome behaviours due to malicious or erroneous activity. We propose that, in order to capture the breadth of relevant aspects, one can use models and logics of strategic ability, that have been developed in multi-agent systems. Using the STV model checker, we take the first step towards the formal modelling and verification of SAI environments, in particular of their resistance to various types of attacks by compromised AI modules.","PeriodicalId":174978,"journal":{"name":"International Conference on Agents and Artificial Intelligence","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125940177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Can We Use Probing to Better Understand Fine-tuning and Knowledge Distillation of the BERT NLU? 我们可以使用探测来更好地理解BERT NLU的微调和知识蒸馏吗?
International Conference on Agents and Artificial Intelligence Pub Date : 2023-01-27 DOI: 10.48550/arXiv.2301.11688
Jakub Ho'scilowicz, Marcin Sowanski, Piotr Czubowski, A. Janicki
{"title":"Can We Use Probing to Better Understand Fine-tuning and Knowledge Distillation of the BERT NLU?","authors":"Jakub Ho'scilowicz, Marcin Sowanski, Piotr Czubowski, A. Janicki","doi":"10.48550/arXiv.2301.11688","DOIUrl":"https://doi.org/10.48550/arXiv.2301.11688","url":null,"abstract":"In this article, we use probing to investigate phenomena that occur during fine-tuning and knowledge distillation of a BERT-based natural language understanding (NLU) model. Our ultimate purpose was to use probing to better understand practical production problems and consequently to build better NLU models. We designed experiments to see how fine-tuning changes the linguistic capabilities of BERT, what the optimal size of the fine-tuning dataset is, and what amount of information is contained in a distilled NLU based on a tiny Transformer. The results of the experiments show that the probing paradigm in its current form is not well suited to answer such questions. Structural, Edge and Conditional probes do not take into account how easy it is to decode probed information. Consequently, we conclude that quantification of information decodability is critical for many practical applications of the probing paradigm.","PeriodicalId":174978,"journal":{"name":"International Conference on Agents and Artificial Intelligence","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121251481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Ontology-based Solution for Building an Intelligent Searching System on Traffic Law Documents 基于本体的交通法规文书智能检索系统构建方案
International Conference on Agents and Artificial Intelligence Pub Date : 2023-01-26 DOI: 10.48550/arXiv.2301.11252
Vuong T. Pham, H. Nguyen, T. Le, Binh Duc Nguyen, Quoc Hung Ngo
{"title":"Ontology-based Solution for Building an Intelligent Searching System on Traffic Law Documents","authors":"Vuong T. Pham, H. Nguyen, T. Le, Binh Duc Nguyen, Quoc Hung Ngo","doi":"10.48550/arXiv.2301.11252","DOIUrl":"https://doi.org/10.48550/arXiv.2301.11252","url":null,"abstract":"In this paper, an ontology-based approach is used to organize the knowledge base of legal documents in road traffic law. This knowledge model is built by the improvement of ontology Rela-model. In addition, several searching problems on traffic law are proposed and solved based on the legal knowledge base. The intelligent search system on Vietnam road traffic law is constructed by applying the method. The searching system can help users to find concepts and definitions in road traffic law. Moreover, it can also determine penalties and fines for violations in the traffic. The experiment results show that the system is efficient for users' typical searching and is emerging for usage in the real-world.","PeriodicalId":174978,"journal":{"name":"International Conference on Agents and Artificial Intelligence","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134365739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Learning to Participate through Trading of Reward Shares 通过奖励股交易学习参与
International Conference on Agents and Artificial Intelligence Pub Date : 2023-01-18 DOI: 10.48550/arXiv.2301.07416
M. Kölle, Tim Matheis, Philipp Altmann, Kyrill Schmid
{"title":"Learning to Participate through Trading of Reward Shares","authors":"M. Kölle, Tim Matheis, Philipp Altmann, Kyrill Schmid","doi":"10.48550/arXiv.2301.07416","DOIUrl":"https://doi.org/10.48550/arXiv.2301.07416","url":null,"abstract":"Enabling autonomous agents to act cooperatively is an important step to integrate artificial intelligence in our daily lives. While some methods seek to stimulate cooperation by letting agents give rewards to others, in this paper we propose a method inspired by the stock market, where agents have the opportunity to participate in other agents' returns by acquiring reward shares. Intuitively, an agent may learn to act according to the common interest when being directly affected by the other agents' rewards. The empirical results of the tested general-sum Markov games show that this mechanism promotes cooperative policies among independently trained agents in social dilemma situations. Moreover, as demonstrated in a temporally and spatially extended domain, participation can lead to the development of roles and the division of subtasks between the agents.","PeriodicalId":174978,"journal":{"name":"International Conference on Agents and Artificial Intelligence","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114752247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Compression of GPS Trajectories using Autoencoders 使用自动编码器的GPS轨迹压缩
International Conference on Agents and Artificial Intelligence Pub Date : 2023-01-18 DOI: 10.48550/arXiv.2301.07420
M. Kölle, Steffen Illium, Carsten Hahn, Lorenz Schauer, Johannes Hutter, Claudia Linnhoff-Popien
{"title":"Compression of GPS Trajectories using Autoencoders","authors":"M. Kölle, Steffen Illium, Carsten Hahn, Lorenz Schauer, Johannes Hutter, Claudia Linnhoff-Popien","doi":"10.48550/arXiv.2301.07420","DOIUrl":"https://doi.org/10.48550/arXiv.2301.07420","url":null,"abstract":"The ubiquitous availability of mobile devices capable of location tracking led to a significant rise in the collection of GPS data. Several compression methods have been developed in order to reduce the amount of storage needed while keeping the important information. In this paper, we present an lstm-autoencoder based approach in order to compress and reconstruct GPS trajectories, which is evaluated on both a gaming and real-world dataset. We consider various compression ratios and trajectory lengths. The performance is compared to other trajectory compression algorithms, i.e., Douglas-Peucker. Overall, the results indicate that our approach outperforms Douglas-Peucker significantly in terms of the discrete Fr'echet distance and dynamic time warping. Furthermore, by reconstructing every point lossy, the proposed methodology offers multiple advantages over traditional methods.","PeriodicalId":174978,"journal":{"name":"International Conference on Agents and Artificial Intelligence","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116118481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training 序列:使用序列量子增强训练实现可跟踪的量子机器学习
International Conference on Agents and Artificial Intelligence Pub Date : 2023-01-06 DOI: 10.5220/0011772400003393
Philipp Altmann, Leo Sünkel, Jonas Stein, Tobias Müller, Christoph Roch, Claudia Linnhoff-Popien
{"title":"SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training","authors":"Philipp Altmann, Leo Sünkel, Jonas Stein, Tobias Müller, Christoph Roch, Claudia Linnhoff-Popien","doi":"10.5220/0011772400003393","DOIUrl":"https://doi.org/10.5220/0011772400003393","url":null,"abstract":"Applying new computing paradigms like quantum computing to the field of machine learning has recently gained attention. However, as high-dimensional real-world applications are not yet feasible to be solved using purely quantum hardware, hybrid methods using both classical and quantum machine learning paradigms have been proposed. For instance, transfer learning methods have been shown to be successfully applicable to hybrid image classification tasks. Nevertheless, beneficial circuit architectures still need to be explored. Therefore, tracing the impact of the chosen circuit architecture and parameterization is crucial for the development of beneficially applicable hybrid methods. However, current methods include processes where both parts are trained concurrently, therefore not allowing for a strict separability of classical and quantum impact. Thus, those architectures might produce models that yield a superior prediction accuracy whilst employing the least possible quantum impact. To tackle this issue, we propose Sequential Quantum Enhanced Training (SEQUENT) an improved architecture and training process for the traceable application of quantum computing methods to hybrid machine learning. Furthermore, we provide formal evidence for the disadvantage of current methods and preliminary experimental results as a proof-of-concept for the applicability of SEQUENT.","PeriodicalId":174978,"journal":{"name":"International Conference on Agents and Artificial Intelligence","volume":"37 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124525038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Emotion-Cause Pair Extraction as Question Answering 情感-原因对抽取作为问答
International Conference on Agents and Artificial Intelligence Pub Date : 2023-01-05 DOI: 10.48550/arXiv.2301.01982
Huu-Hiep Nguyen, Minh-Tien Nguyen
{"title":"Emotion-Cause Pair Extraction as Question Answering","authors":"Huu-Hiep Nguyen, Minh-Tien Nguyen","doi":"10.48550/arXiv.2301.01982","DOIUrl":"https://doi.org/10.48550/arXiv.2301.01982","url":null,"abstract":"The task of Emotion-Cause Pair Extraction (ECPE) aims to extract all potential emotion-cause pairs of a document without any annotation of emotion or cause clauses. Previous approaches on ECPE have tried to improve conventional two-step processing schemes by using complex architectures for modeling emotion-cause interaction. In this paper, we cast the ECPE task to the question answering (QA) problem and propose simple yet effective BERT-based solutions to tackle it. Given a document, our Guided-QA model first predicts the best emotion clause using a fixed question. Then the predicted emotion is used as a question to predict the most potential cause for the emotion. We evaluate our model on a standard ECPE corpus. The experimental results show that despite its simplicity, our Guided-QA achieves promising results and is easy to reproduce. The code of Guided-QA is also provided.","PeriodicalId":174978,"journal":{"name":"International Conference on Agents and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130761472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Improving Convergence for Quantum Variational Classifiers using Weight Re-Mapping 利用权重映射改进量子变分分类器的收敛性
International Conference on Agents and Artificial Intelligence Pub Date : 2022-12-22 DOI: 10.48550/arXiv.2212.14807
M. Kölle, Alessandro Giovagnoli, Jonas Stein, M. Mansky, Julian Hager, Claudia Linnhoff-Popien
{"title":"Improving Convergence for Quantum Variational Classifiers using Weight Re-Mapping","authors":"M. Kölle, Alessandro Giovagnoli, Jonas Stein, M. Mansky, Julian Hager, Claudia Linnhoff-Popien","doi":"10.48550/arXiv.2212.14807","DOIUrl":"https://doi.org/10.48550/arXiv.2212.14807","url":null,"abstract":"In recent years, quantum machine learning has seen a substantial increase in the use of variational quantum circuits (VQCs). VQCs are inspired by artificial neural networks, which achieve extraordinary performance in a wide range of AI tasks as massively parameterized function approximators. VQCs have already demonstrated promising results, for example, in generalization and the requirement for fewer parameters to train, by utilizing the more robust algorithmic toolbox available in quantum computing. A VQCs' trainable parameters or weights are usually used as angles in rotational gates and current gradient-based training methods do not account for that. We introduce weight re-mapping for VQCs, to unambiguously map the weights to an interval of length $2pi$, drawing inspiration from traditional ML, where data rescaling, or normalization techniques have demonstrated tremendous benefits in many circumstances. We employ a set of five functions and evaluate them on the Iris and Wine datasets using variational classifiers as an example. Our experiments show that weight re-mapping can improve convergence in all tested settings. Additionally, we were able to demonstrate that weight re-mapping increased test accuracy for the Wine dataset by $10%$ over using unmodified weights.","PeriodicalId":174978,"journal":{"name":"International Conference on Agents and Artificial Intelligence","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133957598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
VoronoiPatches: Evaluating A New Data Augmentation Method VoronoiPatches:评估一种新的数据增强方法
International Conference on Agents and Artificial Intelligence Pub Date : 2022-12-20 DOI: 10.48550/arXiv.2212.10054
Steffen Illium, Gretchen Griffin, Michael Kolle, Maximilian Zorn, Jonas Nusslein, Claudia Linnhoff-Popien
{"title":"VoronoiPatches: Evaluating A New Data Augmentation Method","authors":"Steffen Illium, Gretchen Griffin, Michael Kolle, Maximilian Zorn, Jonas Nusslein, Claudia Linnhoff-Popien","doi":"10.48550/arXiv.2212.10054","DOIUrl":"https://doi.org/10.48550/arXiv.2212.10054","url":null,"abstract":"Overfitting is a problem in Convolutional Neural Networks (CNN) that causes poor generalization of models on unseen data. To remediate this problem, many new and diverse data augmentation methods (DA) have been proposed to supplement or generate more training data, and thereby increase its quality. In this work, we propose a new data augmentation algorithm: VoronoiPatches (VP). We primarily utilize non-linear recombination of information within an image, fragmenting and occluding small information patches. Unlike other DA methods, VP uses small convex polygon-shaped patches in a random layout to transport information around within an image. Sudden transitions created between patches and the original image can, optionally, be smoothed. In our experiments, VP outperformed current DA methods regarding model variance and overfitting tendencies. We demonstrate data augmentation utilizing non-linear re-combination of information within images, and non-orthogonal shapes and structures improves CNN model robustness on unseen data.","PeriodicalId":174978,"journal":{"name":"International Conference on Agents and Artificial Intelligence","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124289049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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