The paper is a overview of the major qualitative spatial representation and reasoning techniques. We survey the main aspects of the representation of qualitative knowledge including ontological aspects, topology, distance, orientation and shape. We also consider qualitative spatial reasoning including reasoning about spatial change. Finally there is a discussion of theoretical results and a glimpse of future work. The paper is a revised and condensed version of [33, 34].

Abstract not found

The need for spatial representations and spatial reasoning is ubiquitous in AI – from robot planning and navigation, to interpreting visual inputs, to understanding natural language – in all these cases the need to represent and reason about spatial aspects of the world is of key importance. Related fields of research, such as geographic information science

The \Naive Physics Manifesto " of Pat Hayes (1978) proposes a large-scale project of developing a formal theory encompassing the entire knowledge of physics of naive reasoners, expressed in a declarative symbolic form. The theory is organized in clusters of closely interconnected concepts and axioms. More recent work in the representation of commonsense physical knowledge has followed a somewhat di erent methodology. The goal has been to develop a competence theory powerful enough to justify commonsense physical inferences, and the research is organized in microworlds, each microworld covering a small range of physical phenomena. In this paper we compare the advantages and disadvantages of the two approaches. Three Scenarios Consider the following scenario: Common sense is a wild thing, savage, and beyond rules.

Abstract. Spider diagram systems provide a visual language that extends the popular and intuitive Venn diagrams and Euler circles. Designed to complement object-oriented modelling notations in the specification of large software systems they can be used to reason diagrammatically about sets, their cardinalities and their relationships with other sets. A set of reasoning rules for a spider diagram system is shown to be sound and complete. We discuss the extension of this result to diagrammatically richer notations and also consider their expressiveness. Finally, we show that for a rich enough system we can diagrammatically express the negation of any diagram.

Models that are constructed within the bounds of a single paradigm are not sufficient for modeling all aspects of complex systems. Therefore, even though reasoning and simulation systems that utilize a single modeling paradigm are the current norm, we explore a multimodel approach in this paper. A multimodel approach is defined as one in which more than one model --- each derived from a different perspective, and utilizing correspondingly distinct reasoning and simulation strategies --- are employed. By describing four models which illustrate the use of different modeling techniques, we show how a multimodel approach can enrich the modeling environment and make it correspond better with real world information. Our models come from many sources --- Systems and Simulation Theory for the modeling of natural phenomenaand artificial devices, and Artificial Intelligence and Cognitive Science for the modeling of human intuition and expertise in reasoning. Generalizing from these four models, we suggest that modeling complex systems may best be approached from an integrated architectural viewpoint which combines multiple modeling paradigms.

. We propose a computational model for analogy solving based on a topological formalism of representation. The source and the target analogs are represented as simplexes and the analogy solving is modeled as a topological deformation of these simplexes along a polygonal chain and according to some constraints. We apply this framework to the resolution of IQ-tests typically presented as "given A, B and C, find D such that A is to B what C is to D". 1 Introduction In this paper, we present a topological framework for knowledge representation based on the concept of simplicial complex. We present then the ESQIMO system which is the application of this framework to an analogy solving problem. The underlying idea developed here is that spatial relationships and more precisely topological relationships such as neighbor, border, dimension, obstruction, deformation, separabitily, path, etc, enable the building and structuration of knowledge representation. More precisely, we explore the possi...

Profile carrying agents offer the opportunity of meeting like minds and increasing efficiency in many information search applications. Profiles can also increase the sophistication of relationships and interactions in multi-agent systems in general. Such profiles (feature lists) may be of the agent owner (interests) or of the information sought (specifications). At the same time there is a continuing increase of profiling data of increasing complexity becoming available.

AI applications require the representation and manipulation of partial spatial knowledge of many different kinds. This paper argues that a representation rich in primitives but fairly restricted in logical form will suffice for many of these purposes. We present and discuss one such representation language. We demonstrate that the language is expressive enough to capture exactly or closely approximate many of the representations that have been used in the AI literature. It also contains some original constructs for dealing with collections of regions of unknown cardinality. 1 Introduction AI researchers working in spatial reasoning have developed many different kinds of representations that can express incomplete spatial information. However, for the most part, each of these representations have been developed in isolation and addresses a different problem. Little attention has been given to the question of how these different representations fit together. In this paper, we pro...

Psychologists have argued that visual imagery plays a vital role in human reasoning. If so, then reasoning with materials that are easy to visualize should be better than reasoning with materials that are hard to visualize. The literature, however, reports inconsistent results. Our starting point was that the inconsistencies arise from confounding imageability with the spatial nature of the materials. Hence, we manipulated the ease of envisaging the materials as visual images and also as spatial layouts. An experiment showed that materials that are easy to visualize impair reasoning unless t hey are also easy to envisage spatially.