Developing storage systems always presents the same challenges and factors to consider. Deciding what to optimize for has a substantial influence on the result. You can spend more time during write in order to lay out structures for more efficient reads, reserve extra space for in-place updates, facilitate faster writes, and buffer data in memory to ensure sequential write operations. It is impossible, however, to do this all at once. An ideal storage system would have the lowest read cost, lowest write cost, and no overhead.

MOSCOW (Reuters) - Russia's Federal Security Service has searched a research facility controlled by the country's space agency Roskosmos over the suspected leaking of secrets about new hypersonic weapons to Western spies, the TASS news agency reported on Friday.

The importance of hierarchically structured representations for tractable planning has long been acknowledged. However, the questions of how people discover such abstractions and how to define a set of optimal abstractions remain open. This problem has been explored in cognitive science in the problem solving literature and in computer science in hierarchical reinforcement learning. Here, we emphasize an algorithmic perspective on learning hierarchical representations in which the objective is to efficiently encode the structure of the problem, or, equivalently, to learn an algorithm with minimal length. We introduce a novel problem-solving paradigm that links problem solving and program induction under the Markov Decision Process (MDP) framework. Using this task, we target the question of whether humans discover hierarchical solutions by maximizing efficiency in number of actions they generate or by minimizing the complexity of the resulting representation and find evidence for the primacy of representational efficiency.

Abstract-- Autonomous systems will play an essential role in many applications across diverse domains including space, marine, air, field, road, and service robotics. They will assist us in our daily routines and perform dangerous, dirty and dull tasks. However, enabling robotic systems to perform autonomously in complex, real-world scenarios over extended time periods (i.e. Some of these have been investigated by sub-disciplines of Artificial Intelligence (AI) including navigation & mapping, perception, knowledge representation & reasoning, planning, interaction, and learning. The different sub-disciplines have developed techniques that, when re-integrated within an autonomous system, can enable robots to operate effectively in complex, long-term scenarios. In this paper, we survey and discuss AI techniques as'enablers' for long-term robot autonomy, current progress in integrating these techniques within long-running robotic systems, and the future challenges and opportunities for AI in long-term autonomy. I. INTRODUCTION Robot technology has improved tremendously over the last decade. Consequently, autonomous robot systems have been able to operate in increasingly complex environments and for increasingly long periods of time, i.e. weeks, months, or years. When a fully modelled robot is deployed in a completely known, static environment, the challenge of long-term autonomy (LTA) reduces to one of robustness, i.e. enabling the robot to remain operational for as long as possible. Without these simplifying assumptions autonomous robots face a number of interrelated challenges. The first refers to the application requirements, e.g., the robot platform (hardware and software), environment and tasks to be performed.

This paper introduces a fully automatic method for generating video game tutorials. The AtDELFI system (AuTomatically DEsigning Legible, Full Instructions for games) was created to investigate procedural generation of instructions that teach players how to play video games. We present a representation of game rules and mechanics using a graph system as well as a tutorial generation method that uses said graph representation. We demonstrate the concept by testing it on games within the General Video Game Artificial Intelligence (GVG-AI) framework; the paper discusses tutorials generated for eight different games. Our findings suggest that a graph representation scheme works well for simple arcade style games such as Space Invaders and Pacman, but it appears that tutorials for more complex games might require higher-level understanding of the game than just single mechanics.

Learning and logic are distinct and remarkable approaches to prediction. Machine learning has experienced a surge in popularity because it is robust to noise and achieves high performance; however, ML experiences many issues with knowledge transfer and extrapolation. In contrast, logic is easily intepreted, and logical rules are easy to chain and transfer between systems; however, inductive logic is brittle to noise. We then explore the premise of combining learning with inductive logic into AI Reasoning Systems. Specifically, we summarize findings from PAC learning (conceptual graphs, robust logics, knowledge infusion) and deep learning (DSRL, $\partial$ILP, DeepLogic) by reproducing proofs of tractability, presenting algorithms in pseudocode, highlighting results, and synthesizing between fields. We conclude with suggestions for integrated models by combining the modules listed above and with a list of unsolved (likely intractable) problems.

Within the realm of service robotics, researchers have placed a great amount of effort into learning motions and manipulations for task execution by robots. The task of robot learning is very broad, as it involves many tasks such as object detection, action recognition, motion planning, localization, knowledge representation and retrieval, and the intertwining of computer vision and machine learning techniques. In this paper, we focus on how knowledge can be gathered, represented, and reproduced to solve problems as done by researchers in the past decades. We discuss the problems which have existed in robot learning and the solutions, technologies or developments (if any) which have contributed to solving them. Specifically, we look at three broad categories involved in task representation and retrieval for robotics: 1) activity recognition from demonstrations, 2) scene understanding and interpretation, and 3) task representation in robotics - datasets and networks. Within each section, we discuss major breakthroughs and how their methods address present issues in robot learning and manipulation.

We introduce MeSys, a meaning-based approach, for solving English math word problems (MWPs) via understanding and reasoning in this paper. It first analyzes the text, transforms both body and question parts into their corresponding logic forms, and then performs inference on them. The associated context of each quantity is represented with proposed role-tags (e.g., nsubj, verb, etc.), which provides the flexibility for annotating an extracted math quantity with its associated context information (i.e., the physical meaning of this quantity). Statistical models are proposed to select the operator and operands. A noisy dataset is designed to assess if a solver solves MWPs mainly via understanding or mechanical pattern matching. Experimental results show that our approach outperforms existing systems on both benchmark datasets and the noisy dataset, which demonstrates that the proposed approach understands the meaning of each quantity in the text more.

In the following writing we discuss a conceptual framework for representing events and scenarios from the perspective of a novel form of causal analysis. This causal analysis is applied to the events and scenarios so as to determine measures that could be used to manage the development of the processes that they are a part of in real time. An overall terminological framework and entity-relationship model are suggested along with a specification of the functional sets involved in both reasoning and analytics. The model is considered to be a specific case of the generic problem of finding sequential series in disparate data. The specific inference and reasoning processes are identified for future implementation.

Upper limb Prosthetic can be viewed as an independent cognitive system in order to develop a conceptual space. In this paper, we provide a detailed analogical reasoning of prosthetic arm to build the conceptual spaces with the help of the theory called geometric framework of conceptual spaces proposed by Gardenfors. Terminologies of conceptual spaces such as concepts, similarities, properties, quality dimensions and prototype are applied for a specific prosthetic system and conceptual space is built for prosthetic arm. Concept lattice traversals are used on the lattice represented conceptual spaces. Cognitive functionalities such as generalization (Similarities) and specialization (Differences) are achieved in the lattice represented conceptual space. This might well prove to design intelligent prosthetics to assist challenged humans. Geometric framework of conceptual spaces holds similar concepts closer in geometric structures in a way similar to concept lattices. Hence, we also propose to use concept lattice to represent concepts of geometric framework of conceptual spaces. Also, we extend our discussion with our insights on conceptual spaces of bidirectional hand prosthetics.