Nokel, K., Temporally Distributed Symptoms in Technical Diagnosis, Lecture Notes in Artificial Intelligence 517, Springer Verlag 1991.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....thus obtain contains candidates that are very improbable. State probabilities identify improbable states and transition probabilities identify unlikely temporal evolutions. For a temporal matching problem to have a solution within this framework, conditions similar to those specified by Nokel [8] have to be met. These conditions are: 1. Probabilistic pattern: The evolution pattern E is specified in terms of states and transition probabilities. 2. Availability of observations: The set O is not empty. 3. Validity of observations: Observations in O are valid states. 4. Compatibility of ....

K. Nokel. Temporally Distributed Symptoms in Technical Diagnosis, volume 517 of Lecture Notes in Artificial Intelligence. Springer-Verlag, 1991.

....main problem is to know whether this network is consistent. This problem is NP complete. The goal of numeral studies was to find particular fragments of relations for which these problems are polynomial. In this section we focus on some tractable fragments found: the sets of the convex relations [17, 5] and preconvex relations [13, 16] of IA, the sets of the convex relations [2] and strongly preconvex relations [3] of RA. f si bi b s fi di d mi oi o m eq Figure 2: The interval lattice (B int ; Ligozat [13] arranges in order the IA atomic relations and obtains the interval lattice ....

K. Nokel. Temporally distributed symptoms in technical diagnosis, LNCS 517, 1991.

....more general . In the following section we define the generalized intervals and the relations we consider between these entities. Then in section 3 we will remind the notion of convexity and we will introduce the weak preconvexity in section 4 both notions introduced respectively by Nokel [9] and by Ligozat [7] for IA. Section 5 will be devoted to the generalized constraint networks and to the path consistency and weak path consistency methods. In sections 6 and 7 we will characterize sets of generalized relations for which the consistency problem of a generalized network is a ....

....Convex Relations In this section and the following one we will define two particular subsets of generalized relations: the set of the convex relations and the set of the preconvex relations. For this purpose we will extend some notions introduced by Ligozat [7, 8] to redefine the convex relations [9] and the ORD Horn relations [10] called preconvex relations by Ligozat) of IA. Ligozat arranges the atomic relations of Aint in a partial order which defines a lattice: the interval lattice (see fig. 2) From this order we organize the atomic relations of A p;q in a o eq b bi oi m mi d fi s ....

[Article contains additional citation context not shown here]

K. Nokel. Temporally distributed symptoms in technical diagnosis, LNCS 517, 1991.

....in which one is interested in constructing a time line where each particular event or phenomenon corresponds to an interval representing its duration. These include seriation in archeology [25, 26] behavioral psychology [12] temporal reasoning [2] operations research [33] medical diagnosis [31], circuit design [43] and combinatorics [34] Indeed, it was the intersection data of time intervals that lead Hajos [23] to define and ask for a characterization of interval graphs, and which provides the clues for solving the Berge mystery story [20, p. 20] Other applications arise in ....

....during a certain period of time) and uses the following 13 primitive relations fOE; m;m Gamma1 ; o; o Gamma1 ; s; s Gamma1 ; f; f Gamma1 ; d; d Gamma1 ; jg to express the relative position of two intervals (Table 1) We will call this the 13 valued interval algebra A 13 . Nokel [31] has observed that these relations can be partially ordered to form a lattice (see Figure 2(I) which may be used for studying convexity problems. There are two lines of specialization which we study in this paper, macro relations and restricted domains. Macro relations refers to partitioning the ....

[Article contains additional citation context not shown here]

K. Nokel. Temporally Distributed Symptoms in Technical Diagnosis. Lecture Notes in Artificial Intelligence 517, Springer Verlag, 1991. -

....between a pair of intervals by their concatenation. Hence, xOE y is short for xOEy or x y , xOEz is short for xOEz or xz , etc. In addition to the applications discussed above, ISAT problems arise in artificial intelligence, in the context of temporal reasoning [2] and medical diagnosis [28]. An important notion in studying interval satisfiability problems is the domain: It is the collection of possible sets of permitted relations in the input. For example, in the domain f ; OEg, for every two events either the input requires that they intersect or it requires that they should not ....

K. Nokel. Temporally Distributed Symptoms in Technical Diagnosis. Lecture Notes in Artificial Intelligence 517. Springer Verlag, 1991.

....data of time intervals. Other applications include seriation in archaeology [22, 23] behavioral psychology [8] scheduling [30] circuit design [36] and combinatorics [33] In artificial intelligence, a lot of such work has been done in temporal reasoning [2] planning [3] and medical diagnosis [28]. There are also non temporal applications: In genetics, arrangement of genetic material along a linear chain motivated Benzer to study similar problems [5] A central 2 challenge in modern molecular biology and the Human Genome Project is physical mapping of DNA [7, 21] It calls for the ....

K. Nokel. Temporally Distributed Symptoms in Technical Diagnosis. Lecture Notes in Artificial Intelligence 517. Springer Verlag, 1991.

....arise in many practical problems which require the construction of a time line where each particular event or phenomenon corresponds to an interval representing its duration. Among the applications are planning [3] scheduling [22, 31] archaeology [26] temporal reasoning [2] medical diagnosis [29], and circuit design [36] There are also non temporal applications in genetics [6] and behavioral psychology [9] In the Human Genome Project, a central problem which bears directly on interval graphs is the physical mapping of DNA [8, 25] It calls for the reconstruction of a map (a realization) ....

K. Nokel. Temporally Distributed Symptoms in Technical Diagnosis. Lecture Notes in Artificial Intelligence 517. Springer Verlag, 1991.

....in order to study the intersection data of time intervals. Other applications include archaeology [30, 31] scheduling [38] circuit design [45] and combinatorics [40] In artificial intelligence, a lot of such work has been done in temporal reasoning [2] planning [3] and medical diagnosis [36]. There are also non temporal applications in genetics [6] and behavioral psychology [9] A central challenge in modern molecular biology and the Human Genome Project is physical mapping of DNA [8, 29] It calls for the reconstruction of a map (a realization) for a collection of DNA segments, ....

K. Nokel. Temporally Distributed Symptoms in Technical Diagnosis. Lecture Notes in Artificial Intelligence 517. Springer Verlag, 1991.

....the relative positions of time intervals such as . point to the figure while explaining the performance of the system . Further, for natural language understanding [3, 30] general planning [4, 6] presentation planning in a multi media context [7, 9] diagnosis of technical systems [29], and knowledge representation [20, 37] the representation of qualitative temporal relations and reasoning about them is essential. Allen [2] introduces an algebra of binary relations on intervals (hereafter referred to as Allen s interval algebra) for representing qualitative temporal ....

....[35] it is very unlikely that other polynomial time algorithms will be found that solve this problem in general. Subsequent research has concentrated on designing more efficient reasoning algorithms, on identifying tractable special cases, and on isolating sources of computational complexity [10, 13, 14, 15, 16, 17, 22, 23, 28, 29, 31, 32, 33, 34, 35, 36]. However, it is by no means clear whether the tractable cases that have been identified are maximal and whether the sources of computational complexity found are the only ones. We extend these previous results in three ways. Firstly, we present a new tractable subclass of Allen s interval ....

[Article contains additional citation context not shown here]

K. Nokel. Temporally Distributed Symptoms in Technical Diagnosis, volume 517 of Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, Heidelberg, New York, 1991.

....domain. In TUP, information about events is stored as relations between the absolute times of the start and end points. However, the system can only handle atomic IA relations between events. Sets of atomic IA relations are not allowed. In another diagnosis related temporal reasoning system [12], 1 convex and 2 convex relations are used. These relations are subclasses of the pointisable relations or SIA. There are other experimental works using randomly generated IA networks reported in the literature. In [7] finding path consistent IA networks of size 3 to 30 took between 2 and 5 ....

K. Nokel. Temporally distributed symptoms in technical diagnosis. Lecture Notes in Artificial Intelligence, 517, 1991.

....we apply our approach to a real world case study taken from the diagnostic domain. We focus our attention to the representation and processing of information about fault symptoms that is spread out over periods of time and for which current expert system technology is particularly deficient [10]. An example of such a dynamic behavior taken from the domain of a computerized numerical control (CNC) machining center is the following: One possible cause for an undefined position of the tool magazine is a faulty limit switch. This cause can be ruled out if the status registers IN29 and IN30 ....

....limit switch. This cause can be ruled out if the status registers IN29 and IN30 of the CNC control system show the following behavior: at the beginning both registers contain the value 1. Then IN29 drops to 0, followed by IN30. Finally, both return to their original values in the reverse order [10]. Figure 2 represents graphically the situation. In order to apply the rule, measurements have to be taken in the real world. IN29 IN30 time Figure 1. The expected behaviour of status registers. However, while measurements should be taken frequently enough to guarantee that signal transitions ....

K. Nokel, Temporally distributed symptoms in Technical Diagnosis; Springer-Verlag, 1991.

....1994. 1 Introduction Representing and reasoning about qualitative temporal information is essential for many tasks of Artificial Intelligence. In several areas, including planning [4, 5] plan recognition [21, 42] natural language understanding [3, 28, 34] and diagnosis of technical systems [30], temporal knowledge may take the form of collections of qualitative relations between time points or intervals. Temporal reasoning tasks include determining consistency (satisfiability) of such collections, finding a consistent scenario (an interpretation for all the temporal variables involved) ....

K. Nokel. Temporally Distributed Symptoms in Technical Diagnosis, volume 517. Springer-Verlag, Berlin, Heidelberg, New York, 1991.

....1 Introduction Representing and reasoning about qualitative temporal information is essential for many tasks of Artificial Intelligence. In several areas, including planning [3, 4] plan recognition [17, 39] natural language understanding [2, 20, 28] and diagnosis of technical systems [22], temporal information can be specified in terms of qualitative relations between time points or intervals. Allen [1] introduced a representation of time and a reasoning method based on thirteen basic relations that can hold between intervals. From these basic relations a relation algebra [29] ....

K. Nokel. Temporally Distributed Symptoms in Technical Diagnosis, volume 517. Springer-Verlag, Berlin, Heidelberg, New York, 1991.

....performance of the backtracking algorithm. 1 INTRODUCTION Representation of qualitative temporal information and reasoning with it is an integral part of many artificial intelligence tasks, such as presentation planning [3] natural language understanding [16] and diagnosis of technical systems [14]. Allen s [1] interval calculus is well suited for representing qualitative temporal relationships and reasoning with it. In fact, it is used in all the applications mentioned above. While the worst case computational properties of Allen s calculus and fragments of it have been quite extensively ....

....is not valid, however [1] Since ISAT is NP complete [19] it is very unlikely that any polynomial algorithm can solve ISAT. However, there exist subsets of A such that ISAT is a polynomial problem if only relations from these subsets are used. These subsets are the continuous endpoint class C [14, 17], the pointizable class P [8, 17] and the ORD Horn class H [13] which form a strict hierarchy. Interestingly, these classes lead also to completeness of the path consistency method. 3 THE BACKTRACKING ALGORITHM If an application needs more expressiveness than is granted by the above mentioned ....

K. Nokel, Temporally Distributed Symptoms in Technical Diagnosis, Springer-Verlag, Berlin, 1991.

....underlying domain of values is infinite, and each constraint is an infinite relation. Allen adapted a constraint propagation algorithm to an infinite domain to help in the analysis of such problems. See [2] 6] 13] 20] 22] 24] 25] 26] 27] 28] 29] 30] 31] 32] 45] 46] [50], 55] 62] 63] 64] and [66] We formulate CSP concepts and methods using relation algebras. We believe this clarifies the mathematics of binary constraint satisfaction methods, and allows problems with finite or potentially infinite domains to be handled in a uniform way. For example, many ....

Nokel, K., Temporally Distributed Symptoms in Technical Diagnosis, Lecture Notes in Artificial Intelligence 517, Springer Verlag 1991.

No context found.

Nokel, K., Temporally Distributed Symptoms in Technical Diagnosis, Lecture Notes in Artificial Intelligence 517, Springer Verlag 1991.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC