To solve the problems, from 1991 to 1993, Korea Advanced Institute of Science and Technology (KAIST) and Daewoo jointly conducted the Daewoo Shipbuilding Scheduling (das) Project. To integrate the scheduling expert systems for shipbuilding, we used a hierarchical scheduling architecture. To automate the dynamic spatial layout of objects in various areas of the shipyard, we developed spatial scheduling expert systems. For reliable estimation of person-hour requirements, we implemented the neural network-based person-hour estimator. In addition, we developed the paneledblock assembly shop scheduler and the longrange production planner.

This article describes a method for building a cognitive map of a virtual urban environment. Our routines enable virtual humans to map their environment using a realistic model of perception. We based our implementation on a computational framework proposed by Yeap and Jefferies (1999) for representing a local environment as a structure called an absolute space representation (ASR). Their algorithms compute and update ASRs from a 2-1/2-dimensional (2-1/2D) sketch of the local environment and then connect the ASRs together to form a raw cognitive map. Our work extends the framework developed by Yeap and Jefferies in three important ways.

I am pleased to present this issue, most of which is devoted to a single subject-Spatial Reasoning. Our guest editor is Avi Kak, of Purdue University. Avi called me in the Summer of 1987, very enthused about a workshop he had recently attended. The idea of a "theme issue" on spatial reasoning sounded like a winner to me. I asked Avi to take the responsibility for selecting and editing the articles, and he agreed.

Keith M. Andress, coauthor of "Evidence Accumulation and Flow of Control in a Hierarchical Spatial Reasoning System, " is a research associate in the Robot Vision Lab at Purdue University His research interests are in formalisms for accumulation of evidence, expert systems, and computer vision. Steven J. Frank, author of "What AI Practitioners Should Know about the Law. Part Two" is an attorney practicing with Nutter, McClennen & Fish, One International Place, Boston, Massachusetts 02210-2699. Martin Herman, coauthor of "A Framework for Representing and Reasoning about Three-Dimensional Objects for Vision" is group leader of the Sensory Intelligence Group in the Robot Systems Division at the National Bureau of Standards, Gaithersburg, MD 20899. His research interests are robotics, robot vision, image understanding, world modeling, real-time planning, autonomous vehicles, and remotely operated vehicles Avinash C. Kak, coauthor of "Evidence Accumulation and Flow of Control in a Hierarchical Spatial Reasoning System, " is a professor of electrical engineering at Purdue University.

The processes that affect the distributions of animals and plants operate at multiple spatial and temporal scales, presenting a unique challenge for the development and coordination of effective conservation strategies, particularly for wide-ranging species. In order to study ecological systems across scales, data must be collected at fine resolutions across broad spatial and temporal extents. Crowdsourcing has emerged as an efficient way to gather these data by engaging large numbers of people to record observations. However, data gathered by crowdsourced projects are often biased due to the opportunistic approach of data collection. In this article, we propose a general class of models called AdaSTEM (for adaptive spatiotemporal exploratory models) that are designed to meet these challenges by adapting to multiple scales while exploiting variation in data density common with crowdsourced data.

We treat robotic assembly planning on two distinct conceptual levels. Planning at the higher level involves deriving nominal trajectories along which the bodies to be assembled are to be moved. These trajectories are nominal in the sense that they would accomplish the assembly were we to have a perfect robot manipulating bodies whose shapes were perfectly accurate. Planning at the lower level transforms such a high-level specification into an assembly plan that takes account of uncertainty. High-level robotic assembly planning is concerned with how bodies fit together and how spatial relationships among bodies are established over time.

Are You an Ecologist or Conservationist Interested in Learning GIS and Machine Learning in R? Then this course is for you! I will take you on an adventure into the amazing of field Machine Learning and GIS for ecological modelling. You will learn how to implement species distribution modelling/map suitable habitats for species in R. My name is MINERVA SINGH and i am an Oxford University MPhil (Geography and Environment) graduate. I finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life spatial data from different sources and producing publications for international peer reviewed journals.

About this course: In this project-based course, you will design and execute a complete GIS-based analysis – from identifying a concept, question or issue you wish to develop, all the way to final data products and maps that you can add to your portfolio. Your completed project will demonstrate your mastery of the content in the GIS Specialization and is broken up into four phases: Milestone 1: Project Proposal - Conceptualize and design your project in the abstract, and write a short proposal that includes the project description, expected data needs, timeline, and how you expect to complete it. Milestone 2: Workflow Design - Develop the analysis workflow for your project, which will typically involve creating at least one core algorithm for processing your data. The model need not be complex or complicated, but it should allow you to analyze spatial data for a new output or to create a new analytical map of some type. Milestone 3: Data Analysis – Obtain and preprocess data, run it through your models or other workflows in order to get your rough data products, and begin creating your final map products and/or analysis.

Spatial understanding is a fundamental problem with wide-reaching real-world applications. The representation of spatial knowledge is often modeled with spatial templates, i.e., regions of acceptability of two objects under an explicit spatial relationship (e.g., "on", "below", etc.). In contrast with prior work that restricts spatial templates to explicit spatial prepositions (e.g., "glass on table"), here we extend this concept to implicit spatial language, i.e., those relationships (generally actions) for which the spatial arrangement of the objects is only implicitly implied (e.g., "man riding horse"). In contrast with explicit relationships, predicting spatial arrangements from implicit spatial language requires significant common sense spatial understanding. Here, we introduce the task of predicting spatial templates for two objects under a relationship, which can be seen as a spatial question-answering task with a (2D) continuous output ("where is the man w.r.t. a horse when the man is walking the horse?"). We present two simple neural-based models that leverage annotated images and structured text to learn this task. The good performance of these models reveals that spatial locations are to a large extent predictable from implicit spatial language. Crucially, the models attain similar performance in a challenging generalized setting, where the object-relation-object combinations (e.g.,"man walking dog") have never been seen before. Next, we go one step further by presenting the models with unseen objects (e.g., "dog"). In this scenario, we show that leveraging word embeddings enables the models to output accurate spatial predictions, proving that the models acquire solid common sense spatial knowledge allowing for such generalization.

The Faroe Islands didn't have Google street view, but they wanted to. So they strapped 360-degree cameras on the backs of sheep to make their own.