We present a model of sense-making that greatly facilitates the collaboration between an intelligent analyst and a knowledge-based agent. It is a general model grounded in the science of evidence and the scientific method of hypothesis generation and testing, where sense-making hypotheses that explain an observation are generated, relevant evidence is then discovered, and the hypotheses are tested based on the discovered evidence. We illustrate how the model enables an analyst to directly instruct the agent to understand situations involving the possible production of weapons (e.g., chemical warfare agents) and how the agent becomes increasingly more competent in understanding other situations from that domain (e.g., possible production of centrifuge-enriched uranium or of stealth fighter aircraft).

With the emerging opportunities of artificial intelligence (AI), learning and teaching may be supported in situ and in real-time for more efficient and valid solutions. Hence, AI have the potential to further revolutionise the integration of human and artificial intelligence and impact human and machine collaboration during learning and teaching (Seeber et al., 2020; Wesche & Sonderegger, 2019). The discourse around utilisation of AI in education shifted from being narrowly focused on automation-based tasks to augmentation of human capabilities linked to learning and teaching (Chatti et al., 2020). As such, AI systems are capable of analysing large datasets, including unstructured data, in real-time, and detect patterns or structures that can be used for intelligent human decision-making in learning and teaching situations (Baker, 2016). This special issue will address the reciprocal issues when augmenting human intelligence with machine intelligence in K-12 and higher education.

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Python is a general-purpose interpreted programming language that is used for web development, data analysis, and machine learning. Python programming is a perfect language for python enthusiasts to understand better. To help you understand concepts better. Here are the top 10 python programming books. Automating Boring Stuff with Python is a go-to book for all python lovers. Even though the title of the book sounds boring, the book is not at all so.

In this note, I develop my personal view on the scope and relevance of symbolic computation in software science. For this, I discuss the interaction and differences between symbolic computation, software science, automatic programming, mathematical knowledge management, artificial intelligence, algorithmic intelligence, numerical computation, and machine learning. In the discussion of these notions, I allow myself to refer also to papers (1982, 1985, 2001, 2003, 2013) of mine in which I expressed my views on these areas at early stages of some of these fields. It is a great joy to see that the SCSS (Symbolic Computation in Software Science) conference series, this year, experiences its 9th edition. A big Thank You to the organizers, referees, and contributors who kept the series going over the years! The series emerged from a couple of meetings of research groups in Austria, Japan, and Tunisia, including my Theorema Group at RISC, see the home pages of the SCSS series since 2006. In 2012, we decided to define "Symbolic Computation in Software Science" as the scope for our meetings and to establish them as an open conference series with this title. As always, when one puts two terms like "symbolic computation" and "software science" together, one is tempted to read the preposition in between - in our case "in" - as just a set-theoretic union. Pragmatically, this is reasonable if one does not want to embark on scrutinizing discussions. However, since I was one of the initiators of the SCSS series, let me take the opportunity to explain the intention behind SC in SS in this note. Also, this note, for me, is a kind of revision and summary of thoughts I had over the years on the subject of SCSS and related subjects.

In Bayesian learning of Gaussian graphical model structure, it is common to restrict attention to certain classes of graphs and approximate the posterior distribution by repeatedly moving from one graph to another, using MCMC or methods such as stochastic shotgun search (SSS). I give two corrected versions of an algorithm for non-decomposable graphs and discuss random graph distributions, in particular as prior distributions. The main topic of the thesis is Bayesian structure-learning with forests or trees. Restricting attention to these graphs can be justified using theorems on random graphs. I describe how to use the Chow$\unicode{x2013}$Liu algorithm and the Matrix Tree Theorem to find the MAP forest and certain quantities in the posterior distribution on trees. I give adapted versions of MCMC and SSS for approximating the posterior distribution for forests and trees, and systems for storing these graphs so that it is easy to choose moves to neighbouring graphs. Experiments show that SSS with trees does well when the true graph is a tree or sparse graph. SSS with trees or forests does better than SSS with decomposable graphs in certain cases. Graph priors improve detection of hubs but need large ranges of probabilities. MCMC on forests fails to mix well and MCMC on trees is slower than SSS. (For a longer abstract see the thesis.)

The Industry 4.0 paradigm has been characterized by greater connectivity between networks of digitalized manufacturing systems. The application of enabling technologies, including automation and cyber-physical systems, has supported smart manufacturing and decentralized decision making. The implications of Industry 4.0 technologies are significant, leading to reduced production time and cost, while improving product quality. The challenges include how to analyze, exchange, and securely manage the vast amounts of data generated between manufacturing systems. These challenges have spurred growth in research areas including additive manufacturing, Artificial Intelligence, collaborative robotics, digital manufacturing, Internet of Things, machine learning, Big Data analytics, virtual and augmented reality, as well as many others.

Landslides pose a serious risk to population, property, and environment in mountainous regions and even in flat areas worldwide. Landslides have caused massive casualties and significant losses and damage to property. In recent years, machine learning (ML) techniques, including deep learning methods, have increasingly been used to model complex landslides. Analyses so far have demonstrated promising predictive ability compared to traditional, deterministic solutions, and physical model testing. This Special Issue of Applied Sciences seeks to incorporate the latest developments in machine learning with respect to modeling and prediction of landslide susceptibility, including quantitative and qualitative assessments of the classification, volume (or area) and spatial distribution of landslides, as well as the velocity, intensity, and runout (and consequences) of existing or potential landsliding.

A special issue of Geophysical Prospecting is being planned on machine learning applications in geophysical exploration and monitoring. Artificial intelligence, and in particular its subdomain machine learning, has revolutionized many science and engineering disciplines during the past decade. In many domains such as image recognition, machine translation, and speech analysis, machine learning outperforms conventional techniques and has emerged as the method of choice. It is no surprise that recently geophysicists have also found great value in machine learning to automate workflows, extract valuable information from big data, and create new pathways in solving challenging computational problems. Despite this surge in interest, we are still in the early days of developing machine learning applications for subsurface resource exploration, and the geophysical community at large will benefit from a better understanding of the promise of machine learning in transforming industrial practices.

This trend also brings about a unique opportunity and good assurance for solving different critical problems in medical and healthcare systems as well as engineering applications of Artificial Intelligence (AI) and Operations Research (OR). However, such an assurance strongly depends on the extent to which researchers can discover useful patterns, find informative mechanisms underlying the fragmented and diverse data sets, as well as convert this knowledge into intelligent decisions. AI techniques have been recently studied and applied as promising tools for the development and application of intelligent systems in the healthcare context. AI-based systems can generally learn from data and evolve according to real-time changes and fluctuations by considering the indisputable uncertainty of health data and processes. Many attempts have been made so far that employ different techniques including, inter alia, Machine Learning (ML), neural networks, optimization, computational intelligence and human–machine interface.