Reinforcement learning (RL) operating on attack graphs leveraging cyber terrain principles are used to develop reward and state associated with determination of surveillance detection routes (SDR). This work extends previous efforts on developing RL methods for path analysis within enterprise networks. This work focuses on building SDR where the routes focus on exploring the network services while trying to evade risk. RL is utilized to support the development of these routes by building a reward mechanism that would help in realization of these paths. The RL algorithm is modified to have a novel warm-up phase which decides in the initial exploration which areas of the network are safe to explore based on the rewards and penalty scale factor.

工程方法集中在传统的分解和重构概念上，这些概念依赖于分区组件的输入和输出，以允许组成后的组件级属性。但是，在人工智能（AI）中，通常期望系统会影响其环境，并通过环境影响自己。因此，目前尚不清楚AI系统的输入是否将独立于其输出，因此，是否可以将AI系统视为传统组件。本文认为，工程通用智能需要新的通用系统戒律，称为核心和外围，并探索其理论用途。使用抽象系统理论和必要品种定律详细阐述了新的戒律。通过使用呈现的材料，工程师可以更好地理解调节AI结果以满足利益相关者需求的总体特征，以及实施方案的一般系统性质如何挑战传统工程实践。

转移学习，多任务学习和元学习是关于跨学习任务的知识的概括，与一般智能密切相关。但是，在文献中，它们之间的形式，正式的系统差异并没有得到充实。缺乏系统级形式主义导致在协调相关的跨学科工程工作方面遇到困难。该手稿将转移学习，多任务学习和元学习形式化为抽象学习系统，与正式的最小主义摘要系统理论一致。此外，它使用提出的形式主义来将三个学习的概念从组成，等级和结构同态的角度联系起来。从投入输出系统方面很容易地描绘了发现，强调了划定传输，多任务和元学习之间正式的一般系统差异的便利性。

用于实现人为总体情报（AGI）的解决方案方法可能不包含适当模拟和表征AGI所需的形式主义。特别地，目前的学习方法将问题域和问题任务的概念作为基本的常见，但几乎没有明显，野外遇到的AGI将被辨别到一组域任务配对中。显然，系统中AGI的结果也不明显，可以在域和任务方面或作为其后果很好地表达。因此，对于学习的荟萃理论，在解决方案方法方面没有明确表达自己的实际和理论使用。一般系统理论提供了这样的元理论。这里，Mesarovician摘要系统理论被用作学习的超级结构。摘要制定了学习系统。随后的精制将学习系统的假设分层将学习系统的假设分解为层次结构，并考虑到学习理论的层次结构项目。卓越的梅萨维亚人摘要学习系统理论通过直接关注思想参与者，在这种情况下，在这种情况下，与当代关注有关思维的参与者解决问题的思考系统来说，通过专注于思维参与者来返回人工智能研究的创始动力。

但是，强化学习（RL）已应用于攻击渗透测试的攻击图，但是，受过训练的药物并不能反映现实，因为攻击图缺乏在战场的智能准备中通常捕获的操作细微差别（IPB），包括（网络）地形的概念。特别是，当前的实践构建攻击图专门使用常见漏洞评分系统（CVSS）及其组件。我们介绍了使用IPB概念在网络地形分析的障碍，接近途径，关键地形，观察和火场以及掩盖和隐藏的网络地形分析中构建攻击图的方法。我们在一个示例中演示了我们的方法，其中防火墙被视为障碍，并在（1）奖励空间和（2）状态动力学中表示。我们表明，地形分析可用于使现实主义攻击RL的图形。

Autonomous vehicles are being deployed with a spectrum of capability, extending from driver assistance features for the highway in personal vehicles (SAE Level 2+) to fully autonomous fleet ride sharing services operating in complex city environments (SAE Level 4+). This spectrum of autonomy often operates in different physical environments with different degrees of assumed driver in-the-loop oversight and hence have very different system and subsystem requirements. At the heart of SAE Level 2 to 5 systems is localization and mapping, which ranges from road determination for feature geofencing or high-level routing, through lane determination for advanced driver assistance, to where-in-lane positioning for full vehicle control. We assess localization and mapping requirements for different levels of autonomy and supported features. This work provides a framework for system decomposition, including the level of redundancy needed to achieve the target level of safety. We examine several representative autonomous and assistance features and make recommendations on positioning requirements as well map georeferencing and information integrity.

We present Azimuth, an open-source and easy-to-use tool to perform error analysis for text classification. Compared to other stages of the ML development cycle, such as model training and hyper-parameter tuning, the process and tooling for the error analysis stage are less mature. However, this stage is critical for the development of reliable and trustworthy AI systems. To make error analysis more systematic, we propose an approach comprising dataset analysis and model quality assessment, which Azimuth facilitates. We aim to help AI practitioners discover and address areas where the model does not generalize by leveraging and integrating a range of ML techniques, such as saliency maps, similarity, uncertainty, and behavioral analyses, all in one tool. Our code and documentation are available at github.com/servicenow/azimuth.

Science tests competing theories or models by evaluating the similarity of their predictions against observational experience. Thus, how we measure similarity fundamentally determines what we learn. In machine learning and scientific modeling, similarity metrics are used as objective functions. A classic example being mean squared error, which is the optimal measure of similarity when errors are normally distributed and independent and identically distributed (iid). In many cases, however, the error distribution is neither normal nor iid, so it is left to the scientist to determine an appropriate objective. Here, we review how information theory can guide that selection, then demonstrate the approach with a simple hydrologic model.

As Artificial and Robotic Systems are increasingly deployed and relied upon for real-world applications, it is important that they exhibit the ability to continually learn and adapt in dynamically-changing environments, becoming Lifelong Learning Machines. Continual/lifelong learning (LL) involves minimizing catastrophic forgetting of old tasks while maximizing a model's capability to learn new tasks. This paper addresses the challenging lifelong reinforcement learning (L2RL) setting. Pushing the state-of-the-art forward in L2RL and making L2RL useful for practical applications requires more than developing individual L2RL algorithms; it requires making progress at the systems-level, especially research into the non-trivial problem of how to integrate multiple L2RL algorithms into a common framework. In this paper, we introduce the Lifelong Reinforcement Learning Components Framework (L2RLCF), which standardizes L2RL systems and assimilates different continual learning components (each addressing different aspects of the lifelong learning problem) into a unified system. As an instantiation of L2RLCF, we develop a standard API allowing easy integration of novel lifelong learning components. We describe a case study that demonstrates how multiple independently-developed LL components can be integrated into a single realized system. We also introduce an evaluation environment in order to measure the effect of combining various system components. Our evaluation environment employs different LL scenarios (sequences of tasks) consisting of Starcraft-2 minigames and allows for the fair, comprehensive, and quantitative comparison of different combinations of components within a challenging common evaluation environment.

While machine learning models have achieved unprecedented success in real-world applications, they might make biased/unfair decisions for specific demographic groups and hence result in discriminative outcomes. Although research efforts have been devoted to measuring and mitigating bias, they mainly study bias from the result-oriented perspective while neglecting the bias encoded in the decision-making procedure. This results in their inability to capture procedure-oriented bias, which therefore limits the ability to have a fully debiasing method. Fortunately, with the rapid development of explainable machine learning, explanations for predictions are now available to gain insights into the procedure. In this work, we bridge the gap between fairness and explainability by presenting a novel perspective of procedure-oriented fairness based on explanations. We identify the procedure-based bias by measuring the gap of explanation quality between different groups with Ratio-based and Value-based Explanation Fairness. The new metrics further motivate us to design an optimization objective to mitigate the procedure-based bias where we observe that it will also mitigate bias from the prediction. Based on our designed optimization objective, we propose a Comprehensive Fairness Algorithm (CFA), which simultaneously fulfills multiple objectives - improving traditional fairness, satisfying explanation fairness, and maintaining the utility performance. Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed CFA and highlight the importance of considering fairness from the explainability perspective. Our code is publicly available at https://github.com/YuyingZhao/FairExplanations-CFA .