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.

We have to implement Queue using Linked List, In order to do that first we need to implement Linked List after that in the linked list we have to define two methods enqueue() and dequeue(). To implement a linked list you may prefer Python Program to Create a Linked List & Display the Elements in the List. In the enqueue() method we add a new node at the end of the linked list. In the dequeue() method we remove the node from the beginning of the linked list and return the data of the removed node. If there is no node then return'None' and print'Queue is empty'.

We introduce a novel logic-based system for reasoning over data streams, which relies on a framework enabling a tight, fine-tuned interaction between Apache Flink and the I^2-DLV system. The architecture allows to take advantage from both the powerful distributed stream processing capabilities of Flink and the incremental reasoning capabilities of I^2-DLV based on overgrounding techniques. Besides the system architecture, we illustrate the supported input language and its modeling capabilities, and discuss the results of an experimental activity aimed at assessing the viability of the approach. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).

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.

Representations are crucial for a robot to learn effective navigation policies. Recent work has shown that mid-level perceptual abstractions, such as depth estimates or 2D semantic segmentation, lead to more effective policies when provided as observations in place of raw sensor data (e.g., RGB images). However, such policies must still learn latent three-dimensional scene properties from mid-level abstractions. In contrast, high-level, hierarchical representations such as 3D scene graphs explicitly provide a scene's geometry, topology, and semantics, making them compelling representations for navigation. In this work, we present a reinforcement learning framework that leverages high-level hierarchical representations to learn navigation policies. Towards this goal, we propose a graph neural network architecture and show how to embed a 3D scene graph into an agent-centric feature space, which enables the robot to learn policies for low-level action in an end-to-end manner. For each node in the scene graph, our method uses features that capture occupancy and semantic content, while explicitly retaining memory of the robot trajectory. We demonstrate the effectiveness of our method against commonly used visuomotor policies in a challenging object search task. These experiments and supporting ablation studies show that our method leads to more effective object search behaviors, exhibits improved long-term memory, and successfully leverages hierarchical information to guide its navigation objectives.

This thesis explores the benefits machine learning algorithms can bring to online planning and scheduling for autonomous vehicles in off-road situations. Mainly, we focus on typical problems of interest which include computing itineraries that meet certain objectives, as well as computing scheduling strategies to execute synchronized maneuvers with other vehicles. We present a range of learning-based heuristics to assist different itinerary planners. We show that these heuristics allow a significant increase in performance for optimal planners. Furthermore, in the case of approximate planning, we show that not only does the running time decrease, the quality of the itinerary found also becomes almost always better. Finally, in order to synthesize strategies to execute synchronized maneuvers, we propose a novel type of scheduling controllability and a learning-assisted algorithm. The proposed framework achieves significant improvement on known benchmarks in this controllability type over the performance of state-of-the-art works in a related controllability type. Moreover, it is able to find strategies on complex scheduling problems for which previous works fail to do so.

To build challenging multi-hop question answering datasets, we propose a bottom-up semi-automatic process of constructing multi-hop question via composition of single-hop questions. Constructing multi-hop questions as composition of single-hop questions allows us to exercise greater control over the quality of the resulting multi-hop questions. This process allows building a dataset with (i) connected reasoning where each step needs the answer from a previous step; (ii) minimal train-test leakage by eliminating even partial overlap of reasoning steps; (iii) variable number of hops and composition structures; and (iv) contrasting unanswerable questions by modifying the context. We use this process to construct a new multihop QA dataset: MuSiQue-Ans with ~25K 2-4 hop questions using seed questions from 5 existing single-hop datasets. Our experiments demonstrate that MuSique is challenging for state-of-the-art QA models (e.g., human-machine gap of $~$30 F1 pts), significantly harder than existing datasets (2x human-machine gap), and substantially less cheatable (e.g., a single-hop model is worse by 30 F1 pts). We also build an even more challenging dataset, MuSiQue-Full, consisting of answerable and unanswerable contrast question pairs, where model performance drops further by 13+ F1 pts. For data and code, see \url{https://github.com/stonybrooknlp/musique}.