The language of epistemic specifications and epistemic logic programs extends disjunctive logic programs under the stable model semantics with modal constructs called subjective literals. Using subjective literals, it is possible to check whether a regular literal is true in every or some stable models of the program, those models, in this context also called belief sets, being collected in a set called world view. This allows for representing, within the language, whether some proposition should be understood accordingly to the open or the closed world assumption. Several attempts for capturing the intuitions underlying the language by means of a formal semantics were given, resulting in a multitude of proposals that makes it difficult to understand the current state of the art. In this paper, we provide an overview of the inception of the field and the knowledge representation and reasoning tasks it is suitable for.

When the House of Representatives returns early from summer recess next week to vote on a blueprint for Democrats' eventual multi trillion-dollar spending bill, the Democratic majority will quickly have to resolve a high-stakes standoff. In the other: Nine House moderates, led by New Jersey Rep. Josh Gottheimer, co-chair of the bipartisan but not necessarily accurately named Problem Solvers Caucus. So which side would you put your money on? Which makes the most pressing question for our nation's lawmakers: What, precisely, is Gottheimer's endgame here? Gottheimer, a former speechwriter for Bill Clinton representing a swingy, but Democrat-trending, northern New Jersey district, was elected to Congress in 2016 and has regularly raised the ire of the left.

Pinterest has launched a new search feature that could make it easier for Black, Brown, Indigenous, Latinx and other POC users to find hair inspiration that would suit their hair types. The visual discovery website has introduced hair pattern search, it said, with BIPOC users in mind. This new feature uses computer vision-powered object detection to enable users to refine their searches by six different hair patterns: protective, coily, curly, wavy, straight and shaved/bald. Now, after users search for broader terms like "summer hairstyles," "glam hair" or "short hair," they'll find new hair pattern buttons that will narrow down the results. The feature is now live in the US, UK, Ireland, Canada, Australia and New Zealand on desktop, as well as on iOS and Android. It will roll out to more locations over the coming months.

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.

The STAR architecture was designed to test the value of the full Selective Tuning model of visual attention for complex real-world visuospatial tasks and behaviors. However, knowledge of how humans solve such tasks in 3D as active observers is lean. We thus devised a novel experimental setup and examined such behavior. We discovered that humans exhibit a variety of problem-solving strategies whose breadth and complexity are surprising and not easily handled by current methodologies. It is apparent that solution methods are dynamically composed by hypothesizing sequences of actions, testing them, and if they fail, trying different ones. The importance of active observation is striking as is the lack of any learning effect. These results inform our Cognitive Program representation of STAR extending its relevance to real-world tasks.

In the question answering(QA) task, multi-hop reasoning framework has been extensively studied in recent years to perform more efficient and interpretable answer reasoning on the Knowledge Graph(KG). However, multi-hop reasoning is inapplicable for answering n-ary fact questions due to its linear reasoning nature. We discover that there are two feasible improvements: 1) upgrade the basic reasoning unit from entity or relation to fact; and 2) upgrade the reasoning structure from chain to tree. Based on these, we propose a novel fact-tree reasoning framework, through transforming the question into a fact tree and performing iterative fact reasoning on it to predict the correct answer. Through a comprehensive evaluation on the n-ary fact KGQA dataset introduced by this work, we demonstrate that the proposed fact-tree reasoning framework has the desired advantage of high answer prediction accuracy. In addition, we also evaluate the fact-tree reasoning framework on two binary KGQA datasets and show that our approach also has a strong reasoning ability compared with several excellent baselines. This work has direct implications for exploring complex reasoning scenarios and provides a preliminary baseline approach.

The language of epistemic specifications and epistemic logic programs extends disjunctive logic programs under the stable model semantics with modal constructs called subjective literals. Using subjective literals, it is possible to check whether a regular literal is true in every or some stable models of the program, those models, in this context also called \emph{belief sets}, being collected in a set called world view. This allows for representing, within the language, whether some proposition should be understood accordingly to the open or the closed world assumption. Several attempts for capturing the intuitions underlying the language by means of a formal semantics were given, resulting in a multitude of proposals that makes it difficult to understand the current state of the art. In this paper, we provide an overview of the inception of the field and the knowledge representation and reasoning tasks it is suitable for. We also provide a detailed analysis of properties of proposed semantics, and an outlook of challenges to be tackled by future research in the area. Under consideration in Theory and Practice of Logic Programming (TPLP)

The merge-and-shrink framework has been introduced as a general approach for defining abstractions of large state spaces arising in domain-independent planning and related areas. The distinguishing characteristic of the merge-and-shrink approach is that it operates directly on the factored representation of state spaces, repeatedly modifying this representation through transformations such as shrinking (abstracting a factor of the representation), merging (combining two factors), label reduction (abstracting the way in which different factors interact), and pruning (removing states or transitions of a factor). We provide a novel view of the merge-and-shrink framework as a “toolbox” or “algebra” of transformations on factored transition systems, with the construction of abstractions as only one possible application. For each transformation, we study desirable properties such as conservativeness (overapproximating the original transition system), inducedness (absence of spurious states and transitions), and refinability (reconstruction of paths in the original transition system from the transformed one). We provide the first complete characterizations of the conditions under which these desirable properties can be achieved. We also provide the first full formal account of factored mappings, the mechanism used within the merge-and-shrink framework to establish the relationship between states in the original and transformed factored transition system. Unlike earlier attempts to develop a theory for merge-and-shrink, our approach is fully compositional: the properties of a sequence of transformations can be entirely understood by the properties of the individual transformations involved. This aspect is key to the use of merge-and-shrink as a general toolbox for transforming factored transition systems. New transformations can easily be added to our theory, with compositionality taking care of the seamless integration with the existing components. Similarly, new properties of transformations can be integrated into the theory by showing their compositionality and studying under which conditions they are satisfied by the building blocks of merge-and-shrink.

Children who learn computer science skills such as coding gain a multitude of benefits in other areas, including problem solving, creative thinking and mathematics, according to a new study by OKdo. For a new report titled Broader Benefits of Learning to Code, the global tech company gathered survey responses from almost 7,000 UK teachers and parents (with children aged 5-16), in which 96% of teachers claimed to have seen first-hand evidence of how computer science lessons can help to improve both hard and soft skills, as well as IT abilities, in children. Overall, eight in 10 (82%) of teachers said computer science education boosts pupils' problem solving capabilities. On top of this, two thirds (68%) agreed that it helps them develop expertise in mathematics, while six in 10 (60%) claimed that lessons in the subject also positively impacts creative thinking in young people. Over a third (35%) felt that teaching coding can boost children's organisational and time management skills, with 34% also feeling that participating in the subject can improve young people's ability to work as part of team.

To create a linked list and display the elements in a llinked list. First, we need to create a Node class to store the data and address of the next Node. After that create a LinkedList class with a head instance initialize as None. In LinkedList class, we have to define two methods one for placing the data in the linked list(create()) and another one to display the data of the link list (display()). In the first method(create()) we have to create a new Node and put the data into that node after that link to the previous node to the new node.