CLEMENTINI, E., FELICE, P. D., AND HERN ANDEZ, D. Qualitative representation of positional information. Artificial Intelligence 95, 2 (1997), 317--356.  Home/Search   Document Details and Download   Summary   Related Articles   Check

.... [46] visual languages [21,33] qualitative simulation of physical processes [16,40,53] and commonsense reasoning [17] Previous work in spatial reasoning has addressed various aspects of space, such as topology [4,9,10,12,20,54] direction [45] shape [24] size [52,65] distance or position [13]. However, most research on qualitative spatial reasoning has focussed on single aspects of space, while real world applications usually require to deal with more than just one spatial aspect. Representing and reasoning about, e.g. topological information only is often insucient. Since di erent ....

E. Clementini, P. di Felice, and D. Hernandez. Qualitative representation of positional information. Arti cial Intelligence, 95(2):317-356, 1997.

....problem for robotics in the past. The CKS system by Tom Strat and Graham Smith [16] 17] uses a spatial directory and a semantic network to store the state information. Ernst David [6] constructed a language of spatial constraints to represent spatial information. Eliseo Clementini et al.[5] developed a framework for qualitative positional information for spatial reasoning. This allows treatment of uncertain spatial data. Avi Pfe er et al. 13] constructed the SPOOK language by using Bayesian networks in combination with object orientation to address the computational loads in large ....

E. Clementini, P. D. Felice, and D. Hernndez. Qualitative representation of positional information. Articial Intelligence, 95(2):317356, september 1997.

....whole duration of existence of the object. Obviously, each OMI refers to exactly one object O and has exactly one start moment i s , end moment i e , motion direction # and motion speed v: i om = O##, v#] Several methods on representing spatial relations between objects have been suggested [Clementini et al. 1997,Guesgen, 1989,Schlieder, 1996] and [Zimmermann and Freksa, 1996] In this approach a spatial relation between two objects is specified through a direction in which the second object is located and the distance between the objects. On a quantitative level a metric distance and an angle in which ....

Clementini, E., Di Felice, P., and Hernandez, D. (1997). Qualitative representation of positional information. Artificial Intelligence, 95(2):317--356.

....calculus [13] However, in general, the environment is dynamic, which means that both the agent itself and also other objects and agents in the environment may move. Thus, in order to perform spatial reasoning, not only (qualitative) distance and orientation information is needed (as e.g. in [1]) but also information about (relative) velocity of objects (see e.g. 2] Therefore, we will introduce concepts for qualitative and relative velocity: quick) to left, neutral, quick) to right. We investigate the usefulness of this approach in a case study, namely ball interception of ....

....at the interception point. Therefore, by applying the law of sine we get sina and hence sina = v 1 In order to determine the velocity in a qualitative manner, the agent considers a finite number of sectors around the agent. Sectors are also used for qualitative orientation information in [1]. Usually the number of sectors is a power of 2. Let us first investigate the case with n = 8 sectors. Then each sector has the size f = 360 # 8 = 45 # . 8 Interception Point a Figure 3: Determining the velocity qualitatively. In this context, we map the angle a from above to one of the ....

Eliseo Clementini, Paolino Di Felice, and Daniel Hernandez. Qualitative representation of positional information. Artificial Intelligence, 95(2):317--356, 1997.

....is behind the o#ce building. Or we would use use cardinal directions to describe, for example, that Hamburg is north of Bremen. All directional, or ordinal, relations have in common that they describe an object of interest with respect to an object of reference and a specific frame of reference (Clementini, Felice, Hernandez 1997). In the context of annotation and retrieval of objects on a geographic scale, using transitive cardinal relations may su#ce to describe most situations. It would allow us to formulate queries like: Select all cities located east of the Rhine river . Metric relations describing distances ....

....qualitatively even from metrical information. In natural language, adverbs such as close or far are frequently used to express distance information. Several systems of qualitative distance relations have been proposed which can be used to represent the semantics of linguistic expressions (e.g. (Clementini, Felice, Hernandez 1997)) Reasoning about spatial relations A crucial design decision for any spatial information system is the choice of an adequate reasoning mechanism. There exist several alternative approaches to spatial inference which all show a trade o# between genericity and e#ciency. In other words, the ....

Clementini, E.; Felice, P. D.; and Hernandez, D. 1997. Qualitative representation of positional information. Artificial Intelligence 95:317--356.

....facilities, e.g. dribbling and ball interception. For these actions, almost) no spatial cognition is required. Spatial cognition is the contents of the second layer. For example, players have to recognize when passing the ball is possible or a player is offside. Many approaches (see e.g. [8, 26]) propose purely qualitative reasoning, i.e. disregarding quantitative information after it has been transferred into a qualitative representation. But this may be too inexact and too vague sometimes. Since we use logic as connecting formalism in all layers, we can access low level data at all ....

....far away (sensor data become unreliable from this distance) remote (out of reach) Quantitative distance intervals can be mapped to qualities. Concerning the other direction, chosen plan schemes must be instantiated with quantitative data for the actual execution. A related work is presented in [8]. There, reasoning on the qualitative level (alone) is provided. Fig. 2 illustrates the correspondence between quantitative and qualitative distances. 1 m kickable area 20 m short distance Fig. 2. Distances quantitative and qualitative. 3.1 Constraint Reasoning In the literature, many ....

E. Clementini, P. Di Felice, and D. Hernandez. Qualitative representation of positional information. Artificial Intelligence, 95(2):317--356, 1997.

....far away (sensor data become unreliable from this distance) remote (out of reach) Quantitative distance intervals can be mapped to qualities. Concerning the other direction, chosen plan schemes must be instantiated with quantitative data for the actual execution. A related work is presented in [8] . There, also reasoning on the qualitative level (alone) is provided. Fig. 3 illustrates the correspondence between quantitative and qualitative distances. 20 m short distance kickable area 1 m Figure 3: Distances quantitative and qualitative. 3.1 Constraint Reasoning In the literature, ....

E. Clementini, P. di Felice, and D. Hernandez. Qualitative representation of positional information. Artificial Intelligence, 95(2):317--356, 1997.

....of existence of the object. Obviously, each OMI refers to exactly one object O and has exactly one start moment i s , end moment i e , motion direction # and motion speed v: i om = O##, v#] i e i s . Several methods on representing spatial relations between objects have been suggested [Clementini et al. 1997,Guesgen, 1989,Schlieder, 1996] and [Zimmermann and Freksa, 1996] In this approach a spatial relation between two objects is specified through a direction in which the second object is located and the distance between the objects. On a quantitative level a metric distance and an angle in which ....

Clementini, E., Di Felice, P., and Hern andez, D. (1997). Qualitative representation of positional information. Artificial Intelligence, 95(2):317--356.

....Definition 4.1 each guard has to be abstracted separately. Unfortunately, g and g are not equivalent (see Theorem 4. 6) In his context, several approaches from qualitative spatial reasoning may be employed, which provide tables for the addition of qualitative distances or orientations (see e.g. [7, 33]) Theorem 4.6 For all (abstracted) situations s and guard formulae g where s must be defined at least for all free variables in the approximations g and g of g according to Definitions 4.1 and 4.4, respectively it holds that s = g implies s = g. The converse does not hold. ....

E. Clementini, P. Di Felice, and D. Hernandez. Qualitative representation of positional information. Artificial Intelligence, 95(2):317--356, 1997.

....kernel code to other programming languages. In our case, some C code is responsible for converting the RoboLog library functions into logical predicates and connect them to SWI Prolog. In order to identify and abstract over situations, additional predicates for qualitative reasoning are employed [1]. The main difference between the RoboLog 99 library and the RoboLog 2000 library lies in the overall control flow. In RoboLog 99, the Prolog predicates were proven in (quasi )parallel to the execution of the low level library code. This led to uncertain timings and sometimes even ....

E. Clementini, P. Di Felice, and D. Hernandez. Qualitative representation of positional information. Artificial Intelligence, 95(2):317--356, 1997.

....is an emerging and challenging issue in QSR. Related work has been done by Gerevini and Renz [13] which deals with the combination of topological knowledge and relative size knowledge in QSR. Similar work might be carried out for other aspects of knowledge in QSR, such as qualitative distance [2] and relative orientation [9, 10] a combination known to be highly important for GIS and robot navigation applications, and on which not much has been achieved so far. This work has been carried out within the context of a DFG project Description Logics and Spatial reasoning (DLS) The goal of ....

E Clementini, P Di Felice, and D Hernandez. Qualitative representation of positional information. Articial Intelligence, 95:317-356, 1997.

....ex 8 Complexity results for some other QSR calculi are presented in Section 5. 2 9 Actually it is straightforward to specify relative measurements given an absolute calculus: to say that x y, one may simply write x y = Cohn Hazarika QSR: An Overview 13 tended to include orientation [31] 10 . A distance system is composed of an ordered sequence of distance relations and a set of structure relations which give additional information about how the distance relations relate to each other. Each distance has an acceptance area; the distance between successive acceptance areas de nes ....

....but have not necessarily developed ecient computational reasoning techniques for them 14 . Of the qualitative approaches to shape description, approaches which work by describing the boundary of an object include those that classify the sequence of di erent types of bound 10 Whereas [31] combines qualitative orientation and absolute distance knowledge, 118] combines qualitative orientation [117] and relative distance information. Another example of a combined distance and position calculus is [68] 11 Section 5 introduces composition tables 12 e.g. because distances are ....

Clementini, E., Di Felice, P. and Hernandez, D.: \Qualitative Representation of Positional Information", Articial Intelligence, 95(2), 1997, pages 317-356.

....sometimes write y 2 p i;j;k;l instead of y 2 q i;j;k;l . P k;l = fp i;j;k;l j(v i ; v j ) 2 E k g; k = 1 : M; l = 1 : L k , are conjunctions of 4 Some propositions we make here, e.g. concerning soundness, also carry over to qualitative schemes, like ones proposed by Clementini et al. [7]. However, other statements, like those concerning eciency, rely on particular structural properties and cannot be transfered. Their treatment would weaken the statements we want to make here and, hence, are mostly neglected in the rest of this paper (cf. 40] 8 primitive constraints. When all ....

....we conclude: 8) t 0 ; 5; 9) t 3 ) t 0 ; 3; 7) t 3 ) t 0 ; 8; 12) t 3 ) t 0 ; 6; 10) t 3 ) t 0 ; 3; 12) t 3 ) On the other hand, one needs to account for background knowledge about the duration of ights. Assuming an interval structure (like the ones proposed by Clementini et al. [7]) referring to ights of short , medium , long , and very long time extension, a common grounding between very long and hour units may be that very long ights take at least 15 hours (the link between very long and its context ight durations may be computed as proposed by Staab ....

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E. Clementini, P. Di Felice, and D. Hernandez. Qualitative representation of positional information. Articial Intelligence, 95(2):317-356, 1997.

.... languages (e.g. 22] and qualitative simulation of physical processes (e.g. 11, 39, 28] Previous work in spatial reasoning has been concentrated on various types of space representation, such as topology (e.g. 23, 40, 43, 25, 9] direction (e.g. 33] distance or position (e.g. [8, 3, 4]) without considering information on the size of spatial regions. Moreover, while most research on qualitative spatial reasoning has been focussed on single aspects of space, real world applications usually require more than just one spatial aspect. Representing and reasoning about, e.g. ....

E. Clementini, P. di Felice, and D. Hernandez. Qualitative representation of positional information. Articial Intelligence, 95(2):317-356, 1997.

....distance information is represented, for example the darker shaded circles are in the same orientation but at different distances from ab. The most sophisticated qualitative distance calculus to date is the framework for representing distances [81] which has been extended to include orientation[21]. In this framework a distance is expressed in a particular frame of reference (FofR) between a primary object (PO) and a reference object (RO) A distance system is composed of an ordered sequence of distance relations (between a PO and an RO) and a set of structure relations which give ....

E Clementini, P Di Felice, and D Hern'andez. Qualitative representation of positional information. Artificial Intelligence, 1997.

....kernel to other programming languages. In our case, some C code is responsible for converting the RoboLog library functions into logical predicates and connect them to SWI Prolog. In order to identify and abstract over situations, additional predicates for qualitative reasoning are employed [1]. The main difference between the RoboLog 99 library and the RoboLog 2000 library lies in the overall control flow. In RoboLog 99, the Prolog predicates were proven in (quasi )parallel to the execution of the low level library code. This led to uncertain timings and sometimes even to ....

E. Clementini, P. Di Felice, and D. Hernandez. Qualitative representation of positional information. Artificial Intelligence, 95(2):317--356, 1997.

....has remained the same: to represent and reason about the indefiniteness of a region s boundary. An example of an egg yolk can be found in figure 1. Crisping Yolk Egg Figure 1: An Egg Yolk structure However, there is another notion of indefiniteness which may apply to a region: its location. [8] have proposed a qualitative calculus for representing and reasoning about the position of point like spatial entities. 27, 26] has proposed a technique for representing and reasoning about the location of spatial regions by locating them with respect to a background region partition (e.g. the ....

E Clementini, P Di Felice, and D Hernandez. Qualitative representation of positional information. Artificial Intelligence, 1997.

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E. Clementini, P. Di Felice, and D. Hernandez. Qualitative representation of positional information. Arti cial Intelligence, 95(2):317-356, 1997.

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CLEMENTINI, E., FELICE, P. D., AND HERN ANDEZ, D. Qualitative representation of positional information. Artificial Intelligence 95, 2 (1997), 317--356.

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Eliseo Clementini, Paolino Di Felice, and Daniel Hernandez. Qualitative representation of positional information. Artificial Intelligence, 95(2):317--356, 1997.

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Clementini, E., Di Felice, P. & Hernandez, D. (1995). Qualitative Representation of Positional Information. Report FKI-208-95. Technische Universitat Munchen, Institut fur Informatik (H2). Munchen, Germany. July 1995. (revised March 1997: http://www7.informatik- .tu-muenchen.de/mitarbeiter/danher/node3.html).

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Eliseo Clementini, Paolino Di Felice, and Daniel Hernandez. Qualitative representation of positional information. Artificial Intelligence, 95(2):317--356, 1997.

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P. F. E. Clementini and D. Hernandez. Qualitative representation of positional information. Artificial Intelligence, 85:317--356, 1997.

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E Clementini, P Di Felice, and D Hernandez. Qualitative representation of positional information. Artificial Intelligence, 95:317--356, 1997. 9

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Eliseo Clementini, Paolino Di Felice, and Daniel Hernandez, `Qualitative representation of positional information ', Articial Intelligence, 95, 317-356, (1997).

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