Fuzzy Representations of Spatial Relations for Spatial Reasoning

2008 ◽  
pp. 629-655
Author(s):  
Isabelle Bloch
Keyword(s):  
Spatial Reasoning ◽  
Spatial Relations

Modelling spatial reasoning systems with shape algebras and formal logic

1997 ◽  
Vol 11 (4) ◽  
pp. 273-285 ◽  
Author(s):  
Scott C. Chase
Keyword(s):  
Spatial Reasoning ◽  
Predicate Logic ◽  
Spatial Relations ◽  
Large Set ◽  
First Order ◽  
Reasoning Systems ◽  
Geometric Objects ◽  
Small Set ◽  
Definition Of ◽  
High Level

AbstractThe combination of the paradigms of shape algebras and predicate logic representations, used in a new method for describing designs, is presented. First-order predicate logic provides a natural, intuitive way of representing shapes and spatial relations in the development of complete computer systems for reasoning about designs. Shape algebraic formalisms have advantages over more traditional representations of geometric objects. Here we illustrate the definition of a large set of high-level design relations from a small set of simple structures and spatial relations, with examples from the domains of geographic information systems and architecture.

Spatial Reasoning and GIS in Linguistic Prehistory. Two Case Studies from Lower Fungom (Northwest Cameroon)

2012 ◽  
Vol 2 (2) ◽  
pp. 150-183 ◽  
Author(s):  
Pierpaolo Di Carlo ◽  
Giovanna Pizziolo
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Real World ◽  
Interdisciplinary Research ◽  
Historical Development ◽  
Language Change ◽  
Spatial Reasoning ◽  
Spatial Relations ◽  
Geographical Information ◽  
Ongoing Project

Being an ontologically multidisciplinary topic, language change is among the best candidates to be addressed using Geographic Information Systems (GIS). GIS can integrate datasets from diverse disciplines along with real-world geographical information, hence facilitating the investigation of (i) the spatial relations existing between research items and (ii) (past) landscapes. Drawing from an ongoing project focused on the historical development of the extremely diverse linguistic situation documented in the Lower Fungom region (Northwest Cameroon), this article explores the possibility of placing authentic interdisciplinary research pivoting on linguistic issues within a GIS framework.

scholarly journals Combining Deep Learning and Qualitative Spatial Reasoning to Learn Complex Structures from Sparse Examples with Noise

2019 ◽  
Vol 33 ◽  
pp. 2911-2918 ◽  
Author(s):  
Nikhil Krishnaswamy ◽  
Scott Friedman ◽  
James Pustejovsky
Keyword(s):  
Deep Learning ◽  
Heuristic Search ◽  
Spatial Reasoning ◽  
Spatial Relations ◽  
Search Space ◽  
Training Data ◽  
Learning Approaches ◽  
Qualitative Spatial Reasoning ◽  
Heuristic Search Algorithms ◽  
Novel Approach

Many modern machine learning approaches require vast amounts of training data to learn new concepts; conversely, human learning often requires few examples—sometimes only one—from which the learner can abstract structural concepts. We present a novel approach to introducing new spatial structures to an AI agent, combining deep learning over qualitative spatial relations with various heuristic search algorithms. The agent extracts spatial relations from a sparse set of noisy examples of block-based structures, and trains convolutional and sequential models of those relation sets. To create novel examples of similar structures, the agent begins placing blocks on a virtual table, uses a CNN to predict the most similar complete example structure after each placement, an LSTM to predict the most likely set of remaining moves needed to complete it, and recommends one using heuristic search. We verify that the agent learned the concept by observing its virtual block-building activities, wherein it ranks each potential subsequent action toward building its learned concept. We empirically assess this approach with human participants’ ratings of the block structures. Initial results and qualitative evaluations of structures generated by the trained agent show where it has generalized concepts from the training data, which heuristics perform best within the search space, and how we might improve learning and execution.

Cooperative Spatial Reasoning for Image Understanding

1997 ◽  
Vol 11 (01) ◽  
pp. 205-227 ◽  
Author(s):  
Takashi Matsuyama ◽  
Toshikazu Wada
Keyword(s):  
Problem Solving ◽  
Intelligent Agents ◽  
Spatial Reasoning ◽  
Spatial Information ◽  
Spatial Relations ◽  
Image Understanding ◽  
Aerial Image ◽  
Region Segmentation ◽  
Distributed Problem Solving ◽  
Cooperative Distributed Problem Solving

Spatial Reasoning, reasoning about spatial information (i.e. shape and spatial relations), is a crucial function of image understanding and computer vision systems. This paper proposes a novel spatial reasoning scheme for image understanding and demonstrates its utility and effectiveness in two different systems: region segmentation and aerial image understanding systems. The scheme is designed based on a so-called Multi-Agent/Cooperative Distributed Problem Solving Paradigm, where a group of intelligent agents cooperate with each other to fulfill a complicated task. The first part of the paper describes a cooperative distributed region segmentation system, where each region in an image is regarded as an agent. Starting from seed regions given at the initial stage, region agents deform their shapes dynamically so that the image is partitioned into mutually disjoint regions. The deformation of each individual region agent is realized by the snake algorithm14 and neighboring region agents cooperate with each other to find common region boundaries between them. In the latter part of the paper, we first give a brief description of the cooperative spatial reasoning method used in our aerial image understanding system SIGMA. In SIGMA, each recognized object such as a house and a road is regarded as an agent. Each agent generates hypotheses about its neighboring objects to establish spatial relations and to detect missing objects. Then, we compare its reasoning method with that used in the region segmentation system. We conclude the paper by showing further utilities of the Multi-gent/Cooperative Distributed Problem Solving Paradigm for image understanding.

scholarly journals Qualitative Spatial Representation for the Humanities

2019 ◽  
Vol 13 (1-2) ◽  
pp. 2-27 ◽  
Author(s):  
John G. Stell
Keyword(s):  
Geographical Information Systems ◽  
Spatial Representation ◽  
Spatial Reasoning ◽  
Spatial Relations ◽  
Geographical Information ◽  
Computational Techniques ◽  
Computational Technique ◽  
Qualitative Spatial Reasoning ◽  
Gis Technology ◽  
Geographical Space

‘Qualitative spatial reasoning and representation’ is a range of techniques developed in Artificial Intelligence to meet the need for a computational treatment of qualitative spatial relations. Examples of such relations include ‘next to’, ‘overlapping’, ‘to the left of’, ‘separate from’, ‘including’, and so on. These relations occur within the data found in the spatial humanities, but the computational techniques described here do not appear to have been used in connection with this context. While Geographical Information Systems (GIS) are widely used as a means of visualizing and exploring material in the spatial humanities, GIS technology is acknowledged to be ill-suited to information that is vague, uncertain, ambiguous, imprecise or having other qualities that in a scientific setting could be regarded as imperfections. In the humanities such ‘imperfections’ are of course important, and qualitative spatial relations are one source of data that challenges scientifically based GIS. This article reviews the origin of qualitative spatial reasoning and representation in A. N. Whitehead's mereotopology and argues for exploring how these methods could complement GIS as a computational technique in the humanities. Qualitative representation is applicable to modelling spatial arrangements in many domains, not just geographical space. This is demonstrated through an example of spatial relations in lines of printed text.

Mining Geo-Referenced Databases

2011 ◽  
pp. 113-150 ◽  
Author(s):  
Maribel Yasmina Santos ◽  
Luís Alfredo Amaral
Keyword(s):  
Knowledge Discovery ◽  
Spatial Data ◽  
Domain Knowledge ◽  
Relational Databases ◽  
Spatial Reasoning ◽  
Spatial Relations ◽  
Semantic Knowledge ◽  
Knowledge Discovery In Databases ◽  
Data Sets ◽  
Qualitative Spatial Reasoning

Knowledge discovery in databases is a process that aims at the discovery of associations within data sets. The analysis of geo-referenced data demands a particular approach in this process. This chapter presents a new approach to the process of knowledge discovery, in which qualitative geographic identifiers give the positional aspects of geographic data. Those identifiers are manipulated using qualitative reasoning principles, which allows for the inference of new spatial relations required for the data mining step of the knowledge discovery process. The efficacy and usefulness of the implemented system — Padrão — has been tested with a bank dataset. The results support that traditional knowledge discovery systems, developed for relational databases and not having semantic knowledge linked to spatial data, can be used in the process of knowledge discovery in geo-referenced databases, since some of this semantic knowledge and the principles of qualitative spatial reasoning are available as spatial domain knowledge.

Topological representation and reasoning in space

2000 ◽  
Vol 14 (5) ◽  
pp. 373-389
Author(s):  
BAHER A. EL-GERESY ◽  
ALIA I. ABDELMOTY
Keyword(s):  
Adjacency Matrix ◽  
Spatial Databases ◽  
Spatial Reasoning ◽  
Hybrid Approach ◽  
Spatial Relations ◽  
Theoretic Approach ◽  
Topological Representation ◽  
Underlying Space ◽  
General Rules ◽  
Reasoning Mechanism

In this article, an approach is presented for the representation and reasoning over qualitative spatial relations. A set-theoretic approach is used for representing the topology of objects and underlying space by retaining connectivity relationships between objects and space components in a structure, denoted, adjacency matrix. Spatial relations are represented by the intersection of components, and spatial reasoning is achieved by the application of general rules for the propagation of the intersection constraints between those components. The representation approach is general and can be adapted for different space resolutions and granularities of relations. The reasoning mechanism is simple and the spatial compositions are achieved in a finite definite number of steps, controlled by the complexity needed in the representation of objects and the granularity of the spatial relations required. The application of the method is presented over geometric structures that takes into account qualitative surface height information. It is also shown how directional relationships can be used in a hybrid approach for richer composition scenarios. The main advantage of this work is that it offers a unified platform for handling different relations in the qualitative space, which is a step toward developing general spatial reasoning engines for large spatial databases.

Spatial Reasoning with Place Information on the Semantic Web

2014 ◽  
Vol 23 (05) ◽  
pp. 1450011 ◽  
Author(s):  
Philip D. Smart ◽  
Alia I. Abdelmoty ◽  
Baher El-Geresy
Keyword(s):  
Semantic Web ◽  
Spatial Reasoning ◽  
Spatial Relations ◽  
Integrity Constraints ◽  
Data Sets ◽  
Logical Framework ◽  
Geometric Information ◽  
Web Resources ◽  
The Web ◽  
Geospatial Ontologies

Geographical referencing of data and resources on the Web has become prevalent. Discovering and linking this information poses eminent research challenges to the geospatial semantic web, with regards to the representation and manipulation of information on geographic places. Towards addressing these challenges, this work explores the potential of the current semantic web languages and tools. In particular, an integrated logical framework of rules and ontologies, using current W3C standards, is assessed for modeling geospatial ontologies of place and for encoding both symbolic and geometric references to place locations. Spatial reasoning is incorporated in the framework to facilitate the deduction of implicit spatial relations and for expressing spatial integrity constraints. The logical framework is extended with geo-computation engines that offer more effective manipulation of geometric information. Example data sets mined from web resources are used to demonstrate and evaluate the framework, offering insights to its potentials and limitations.

Cross-Cultural Preferences in Spatial Reasoning

2011 ◽  
Vol 11 (1-2) ◽  
pp. 1-21 ◽  
Author(s):  
Marco Ragni ◽  
Markus Knauff
Keyword(s):  
Cultural Study ◽  
Spatial Reasoning ◽  
Spatial Relations ◽  
Cross Cultural ◽  
Cultural Backgrounds ◽  
Cultural Preferences ◽  
Cross Cultural Study ◽  
Problem Description ◽  
Cognitive Principles ◽  
Cognition And Culture

AbstractHow do people reason about spatial relations? Do people with different cultural backgrounds differ in how they reason about space? The aim of our cross-cultural study on spatial reasoning is to strengthen this link between spatial cognition and culture. We conducted two reasoning experiments, one in Germany and one in Mongolia. Topological relations, such as “A overlaps B” or “B lies within C”, were presented to the participants as premises and they had to find a conclusion that was consistent with the premises (“What is the relation between A and C?”). The problem description allowed multiple possible “conclusions”. Our results, however, indicate that the participants had strong preferences: They consistently preferred one of the possible conclusions and neglected other conclusions, although they were also logically consistent with the premises. The preferred and neglected conclusions were similar in Germany and Mongolia. We argue that the preferences are caused by universal cognitive principles that work the same way in the western culture and Mongolia.

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