this paper argues for the rich world of representation that lies between these two extremes." Levesque and Brachman (1985) 1 Introduction Time and space belong to those few fundamental concepts that always puzzled scholars from almost all scientific disciplines, gave endless themes to science fiction writers, and were of vital concern to our everyday life and commonsense reasoning. So whatever approach to AI one takes [ Russell and Norvig, 1995 ] , temporal and spatial representation and reasoning will always be among its most important ingredients (cf. [ Hayes, 1985 ] ). Knowledge representation (KR) has been quite successful in dealing separately with both time and space. The spectrum of formalisms in use ranges from relatively simple temporal and spatial databases, in which data are indexed by temporal and/or spatial parameters (see e.g. [ Srefik, 1995; Worboys, 1995 ] ), to much more sophisticated numerical methods developed in computational geom

In recent years there has been increasing interest in constructing cognitive vision systems capable of interpreting the high level semantics of dynamic scenes. Purely quantitative approaches to the task of constructing such systems have met with some success. However, qualitative analysis of dynamic scenes has the advantage of allowing easier generalisation of classes of different behaviours and guarding against the propagation of errors caused by uncertainty and noise in the quantitative data. Our aim is to integrate quantitative and qualitative modes of representation and reasoning for the analysis of dynamic scenes. In particular, in this paper we outline an approach for constructing cognitive vision systems using qualitative spatial-temporal representations including prototypical spatial relations and spatio-temporal event descriptors automatically inferred from input data. The overall architecture relies on abduction: the system searches for explanations, phrased in terms of the learned spatio-temporal event descriptors, to account for the video data.

We consider an extension of linear-time temporal logic (LTL) with constraints interpreted over a concrete domain. We use a new automata-theoretic technique to show pspace decidability of the logic for the constraint systems (Z, <, =) and (N, <, =). Along the way, we give an automata-theoretic proof of a result of [BC02] when the constraint system D satisfies the completion property. Our decision procedures extend easily to handle extensions of the logic with past operators and constants, as well as an extension of the temporal language itself to monadic second order logic. Finally, we show that the logic...

In this paper, we construct and investigate a hierarchy of spatio-temporal formalisms that result from various combinations of propositional spatial and temporal logics such as the propositional temporal logic the spatial logics RCC-8, BRCC-8, S4 u and their fragments. The obtained results give a clear picture of the trade-off between expressiveness and `computational realisability' within the hierarchy. We demonstrate how di#erent combining principles as well as spatial and temporal primitives can produce NP-, PSPACE-, EXPSPACE-, 2EXPSPACE-complete, and even undecidable spatio-temporal logics out of components that are at most NP- or PSPACE-complete.

There exist a number of qualitative constraint calculi that are used to represent and reason about temporal or spatial configurations. However, there are only very few approaches aiming to create a spatio-temporal constraint calculus. Similar to Bennett et al., we start with the spatial calculus RCC-8 and Allen's interval calculus in order to construct a qualitative spatio-temporal calculus. As we will show, the basic calculus is NP-complete, even if we only permit base relations. When adding the restriction that the size of the spatial regions persists over time, or that changes are continuous, the calculus becomes more useful, but the satisfiability problem appears to be much harder. Nevertheless, we are able to show that satisfiability is still in NP.

Reasoning on time and location is receiving increasing attention on the Web due to emerging fields like Web adaptation, mobile computing, and the Semantic Web. Web applications in these fields often refer to rather complex temporal, calendric, and location information. Unfortunately, today’s Web languages and formalisms have merely primitive temporal and location data types and temporal and location reasoning capabilities – if any. This article reports on work in progress aiming at integrating temporal and locational reasoning into XML query and transformation operations. We analyze the problem and propose a concrete architecture. A prototype of the temporal reasoner, the Web-Cal system has already been realized.

The issue addressed in this paper concerns the discovery of frequent multi-dimensional patterns from relational sequences. The great variety of applications of sequential pattern mining, such as user profiling, medicine, local weather forecast and bioinformatics, makes this problem one of the central topics in data mining. Nevertheless, sequential information may concern data on multiple dimensions and, hence, the mining of sequential patterns from multi-dimensional information results very important. In a multi-dimensional sequence each event depends on more than one dimension, such as in spatio-temporal sequences where an event may be spatially or temporally related to other events. In literature, the multi-relational data mining approach has been successfully applied to knowledge discovery from complex data. However, there exists no contribution to manage the general case of multi-dimensional data in which, for example, spatial and temporal information may co-exist. This work takes into account the possibility to mine complex patterns, expressed in a first-order language, in which events may occur along different dimensions. Specifically, multidimensional patterns are defined as a set of atomic first-order formulae in which events are explicitly represented by a variable and the relations between events are represented by a set of dimensional predicates. A complete framework and an Inductive Logic Programming algorithm to tackle this problem are presented along with some experiments on artificial and real multi-dimensional sequences proving its effectiveness.

This work explores the interconnections between a number of different perspectives on the formalisation of space. We begin with an informal discussion of the intuitions that motivate these formal representations.

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Abstract. In this paper we discuss the adoption of a formal approach to correlation of heterogeneous information based on qualitative spatial reasoning to contribute to some relevant aspects that stream reasoning need to face in Urban Computing. The approach is based on the adoption of Commonsense Spatial Hybrid Logics to reason about events and infer higher-level scenarios of interest. This paper therefore extends previous work of the authors in the context of pervasive computing systems in order to take into account an urban-scale application context. In order to discuss the advantages of the approach a real-world application devoted to control and monitor different phenomena occurring in urban environments is described. Finally, some issues related to the exploitation of the approach in Semantic Web frameworks are discussed. 1