Tactical Decision Games (TDGs) are military conflict scenarios presented both textually and graphically on a map. These scenarios provide a challenging domain for machine learning because they are open-ended, highly structured, and typically contain many details of varying relevance. We have developed a problem-solving component of an interactive companion system that proposes military tasks to solve TDG scenarios using a combination of analogical retrieval, mapping, and constraint propagation. We use this problem-solving component to explore analogical learning. In this paper, we describe the problems encountered in learning for this domain, and the methods we have developed to address these, such as partition constraints on analogical mapping correspondences and the use of incremental remapping to improve robustness. We present the results of learning experiments that show improvement in performance through the simple accumulation of examples, despite a weak domain theory.

With the increasing trend in the topic of migration in Europe, the public is now more engaged in expressing their opinions through various platforms such as Twitter. Understanding the online discourses is therefore essential to capture the public opinion. The goal of this study is the analysis of social media platform to quantify public attitudes towards migrations and the identification of different factors causing these attitudes. The tweets spanning from 2013 to Jul-2021 in the European countries which are hosts to immigrants are collected, pre-processed, and filtered using advanced topic modeling technique. BERT-based entity linking and sentiment analysis, and attention-based hate speech detection are performed to annotate the curated tweets. Moreover, the external databases are used to identify the potential social and economic factors causing negative attitudes of the people about migration. To further promote research in the interdisciplinary fields of social science and computer science, the outcomes are integrated into a Knowledge Base (KB), i.e., MigrationsKB which significantly extends the existing models to take into account the public attitudes towards migrations and the economic indicators. This KB is made public using FAIR principles, which can be queried through SPARQL endpoint. Data dumps are made available on Zenodo.

Relation linking is essential to enable question answering over knowledge bases. Although there are various efforts to improve relation linking performance, the current state-of-the-art methods do not achieve optimal results, therefore, negatively impacting the overall end-to-end question answering performance. In this work, we propose a novel approach for relation linking framing it as a generative problem facilitating the use of pre-trained sequence-to-sequence models. We extend such sequence-to-sequence models with the idea of infusing structured data from the target knowledge base, primarily to enable these models to handle the nuances of the knowledge base. Moreover, we train the model with the aim to generate a structured output consisting of a list of argument-relation pairs, enabling a knowledge validation step. We compared our method against the existing relation linking systems on four different datasets derived from DBpedia and Wikidata. Our method reports large improvements over the state-of-the-art while using a much simpler model that can be easily adapted to different knowledge bases.

The Stable Roommates problem with Ties and Incomplete lists (SRTI) is a matching problem characterized by the preferences of agents over other agents as roommates, where the preferences may have ties or be incomplete. SRTI asks for a matching that is stable and, sometimes, optimizes a domain-independent fairness criterion (e.g., Egalitarian). However, in real-world applications (e.g., assigning students as roommates at a dormitory), we usually consider a variety of domain-specific criteria depending on preferences over the habits and desires of the agents. With this motivation, we introduce a knowledge-based method to SRTI considering domain-specific knowledge, and investigate its real-world application for assigning students as roommates at a university dormitory. This paper is under consideration for acceptance in Theory and Practice of Logic Programming (TPLP).

As per some research, AI acts better than humans when it is about diagnosing diseases. You can notice that AI-based technologies are outperforming radiologists at identifying malignant tumors during clinical trials. After observing the excellent outcome, the clinicians believe that AI will replace human efforts in the medical field, but not immediately. In this article, you will get to know the potential of Artificial Intelligence and the significant obstacles to the swift execution of AI in healthcare. Machine Learning is perhaps the most common AI application that enables the system to learn the data and improve the real-time experience without any programming.

This paper presents two studies that use a machine learning expert system (MLES). One focuses on a system to advise to United States federal judges for regarding consistent federal criminal sentencing, based on both the federal sentencing guidelines and offender characteristics. The other study aims to develop a system that could prospectively assist the U.S. Patent and Trademark Office automate their patentability assessment process. Both studies use a machine learning-trained rule-fact expert system network to accept input variables for training and presentation and output a scaled variable that represents the system recommendation (e.g., the sentence length or the patentability assessment). This paper presents and compares the rule-fact networks that have been developed for these projects. It explains the decision-making process underlying the structures used for both networks and the pre-processing of data that was needed and performed. It also, through comparing the two systems, discusses how different methods can be used with the MLES system.

Expert systems have been used to enable computers to make recommendations and decisions. This paper presents the use of a machine learning trained expert system (MLES) for phishing site detection and fake news detection. Both topics share a similar goal: to design a rule-fact network that allows a computer to make explainable decisions like domain experts in each respective area. The phishing website detection study uses a MLES to detect potential phishing websites by analyzing site properties (like URL length and expiration time). The fake news detection study uses a MLES rule-fact network to gauge news story truthfulness based on factors such as emotion, the speaker's political affiliation status, and job. The two studies use different MLES network implementations, which are presented and compared herein. The fake news study utilized a more linear design while the phishing project utilized a more complex connection structure. Both networks' inputs are based on commonly available data sets.

The field of artificial intelligence (AI) is witnessing a recent upsurge in research, tools development, and deployment of applications. Multiple software companies are shifting their focus to developing intelligent systems; and many others are deploying AI paradigms to their existing processes. In parallel, the academic research community is injecting AI paradigms to provide solutions to traditional engineering problems. Similarly, AI has evidently been proved useful to software engineering (SE). When one observes the SE phases (requirements, design, development, testing, release, and maintenance), it becomes clear that multiple AI paradigms (such as neural networks, machine learning, knowledge-based systems, natural language processing) could be applied to improve the process and eliminate many of the major challenges that the SE field has been facing. This survey chapter is a review of the most commonplace methods of AI applied to SE. The review covers methods between years 1975-2017, for the requirements phase, 46 major AI-driven methods are found, 19 for design, 15 for development, 68 for testing, and 15 for release and maintenance. Furthermore, the purpose of this chapter is threefold; firstly, to answer the following questions: is there sufficient intelligence in the SE lifecycle? What does applying AI to SE entail? Secondly, to measure, formulize, and evaluate the overlap of SE phases and AI disciplines. Lastly, this chapter aims to provide serious questions to challenging the current conventional wisdom (i.e., status quo) of the state-of-the-art, craft a call for action, and to redefine the path forward.

The power system is undergoing rapid evolution with the roll-out of advanced metering infrastructure and local energy applications (e.g. electric vehicles) as well as the increasing penetration of intermittent renewable energy at both transmission and distribution level, which characterizes the peak load demand with stronger randomness and less predictability and therefore poses a threat to the power grid security. Since storing large quantities of electricity to satisfy load demand is neither economically nor environmentally friendly, effective peak demand management strategies and reliable peak load forecast methods become essential for optimizing the power system operations. To this end, this paper provides a timely and comprehensive overview of peak load demand forecast methods in the literature. To our best knowledge, this is the first comprehensive review on such topic. In this paper we first give a precise and unified problem definition of peak load demand forecast. Second, 139 papers on peak load forecast methods were systematically reviewed where methods were classified into different stages based on the timeline. Thirdly, a comparative analysis of peak load forecast methods are summarized and different optimizing methods to improve the forecast performance are discussed. The paper ends with a comprehensive summary of the reviewed papers and a discussion of potential future research directions.

Why does Covid-19 present itself more severe in some patients but not in others? The question has puzzled researchers and clinicians since the start of the pandemic, but now new research from the EPFL Blue Brain Project may have found a major clue to solving the mystery thanks to machine learning. Analyzing data extracted from 240,000 open access scientific papers, the findings of a paper published in Frontiers revealed the previously undiscovered roles elevated blood glucose levels have in the severity of Covid-19. What makes one person more at risk of developing severe Covid-19 than someone else? While it is widely accepted that elderly people are the most at-risk during the current pandemic, many young, seemingly healthy people have also been hospitalized by the disease.