In this work, we introduce the problem of visualization search and highlight two underlying challenges of search enumeration and visualization matching.

Macro-operators (macros) are a well-known technique for enhancing performance of planning engines by providing "short-cuts" in the state space. Existing macro learning systems usually generate macros by considering most frequent action sequences in training plans. Unfortunately, frequent action sequences might not capture meaningful activities as a whole, leading to a limited beneficial impact for the planning process. In this paper, inspired by resource locking in critical sections in parallel computing, we propose a technique that generates macros able to capture whole activities in which limited resources (e.g., a robotic hand, or a truck) are used. Specifically, such a Critical Section macro starts by locking the resource (e.g., grabbing an object), continues by using the resource (e.g., manipulating the object) and finishes by releasing the resource (e.g., dropping the object). Hence, such a macro bridges states in which the resource is locked and cannot be used. We also introduce versions of Critical Section macros dealing with multiple resources and phased locks. Usefulness of macros is evaluated using a range of state-of-the-art planners, and a large number of benchmarks from the deterministic and learning tracks of recent editions of the International Planning Competition.

Model-based methods have recently shown promising for offline reinforcement learning (RL), aiming to learn good policies from historical data without interacting with the environment. Previous model-based offline RL methods learn fully connected nets as world-models that map the states and actions to the next-step states. However, it is sensible that a world-model should adhere to the underlying causal effect such that it will support learning an effective policy generalizing well in unseen states. In this paper, We first provide theoretical results that causal world-models can outperform plain world-models for offline RL by incorporating the causal structure into the generalization error bound. We then propose a practical algorithm, oFfline mOdel-based reinforcement learning with CaUsal Structure (FOCUS), to illustrate the feasibility of learning and leveraging causal structure in offline RL. Experimental results on two benchmarks show that FOCUS reconstructs the underlying causal structure accurately and robustly. Consequently, it performs better than the plain model-based offline RL algorithms and other causal model-based RL algorithms.

In this paper, we frame homogeneous-feature multi-task learning (MTL) as a hierarchical representation learning problem, with one task-agnostic and multiple task-specific latent representations. Drawing inspiration from the information bottleneck principle and assuming an additive independent noise model between the task-agnostic and task-specific latent representations, we limit the information contained in each task-specific representation. It is shown that our resulting representations yield competitive performance for several MTL benchmarks. Furthermore, for certain setups, we show that the trained parameters of the additive noise model are closely related to the similarity of different tasks. This indicates that our approach yields a task-agnostic representation that is disentangled in the sense that its individual dimensions may be interpretable from a task-specific perspective.

Recent advances in the areas of multimodal machine learning and artificial intelligence (AI) have led to the development of challenging tasks at the intersection of Computer Vision, Natural Language Processing, and Embodied AI. Whereas many approaches and previous survey pursuits have characterised one or two of these dimensions, there has not been a holistic analysis at the center of all three. Moreover, even when combinations of these topics are considered, more focus is placed on describing, e.g., current architectural methods, as opposed to also illustrating high-level challenges and opportunities for the field. In this survey paper, we discuss Embodied Vision-Language Planning (EVLP) tasks, a family of prominent embodied navigation and manipulation problems that jointly use computer vision and natural language. We propose a taxonomy to unify these tasks and provide an in-depth analysis and comparison of the new and current algorithmic approaches, metrics, simulated environments, as well as the datasets used for EVLP tasks. Finally, we present the core challenges that we believe new EVLP works should seek to address, and we advocate for task construction that enables model generalizability and furthers real-world deployment.

Data Structures and Algorithms - A data structure is a named location that can be used to store and organize data. And, an algorithm is a collection of steps to

Controlled Natural Languages (CNLs) are effective languages for Knowledge Representation and Reasoning that look like the ones you use every day, so you can easily read and understand them. However, when they are based on Logical AI, meaning behind what is being said can be accurately processed not just by humans but also by machines. As logical CNLs can represent information about the real world in a way that machines can process, you will be able to ensure that meaning of what you write is accurately understood by creating definitions of words yourself or selecting the definitions from pre-defined vocabularies (ontologies). For the first time on any social platform, utility and information will not be lost or neglected, because, by writing in logical CNLs, you will be able to see the overlapping points of agreement, disagreements, and contradictions in the meaning map of all conversations and use logical reasoning to solve complex tasks such as diagnosing a medical condition. Other social platforms (such as Facebook, Twitter, etc.) do not really understand meaning of what you're saying.

The Rubik's Cube is a famous 3-D puzzle toy. A regular Rubik's Cube has six faces, each of which has nine coloured stickers, and the puzzle is solved when each face has a united colour. If we count one quarter (90) turn as one move and two quarter turns (a "face" turn) as two moves, the best algorithms human-invented can solve any instance of the cube in 26 moves. My target is to let the computer learn how to solve the Rubik's Cube without feeding it any human knowledge like the symmetry of the cube. The most challenging part is the Rubik's Cube has 43,252,003,274,489,856,000 possible permutations.

National correspondent Bill Melugin has the latest from Eagle Pass, Texas, on'Special Report.' FIRST ON FOX: The Problem Solvers Caucus is backing legislation in the House that would extend Title 42 -- the latest sign of bipartisan pushback against the Biden administration's plans to end the public health order in May. The Biden administration announced earlier this month that it will end the order on May 23, a measure that has been used since March 2020 to quickly remove a majority of migrants encountered at the southern border due to the COVID-19 pandemic. But with growing fears that the already massive border numbers will only accelerate if the order is lifted, a number of moderate Democrats have joined with Republicans in pushing the administration to delay its move. Dec 09: 2021: A U.S. Border Patrol agent speaks with immigrants before transporting some of them to a processing center in Yuma, Arizona.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner How to built an #architecture for autonomous #intelligence? A system architecture for #autonomous intelligence, The configurator module performs executive control: Given a task to be executed, it preconfigures the perception module, the world model, the cost and the actor for the task at hand, possibly by modulating the parameters of those modules. The perception module receives signals from sensors and estimates the current state of the world. For a given task, only a small subset of the perceived state of the world is relevant and useful. The configurator module primes the perception system to extract the relevant information from the percept for the task at hand.