Nebel, B. (1990) Reasoning and Revision in Hybrid Representation Systems, New York: Springer.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....of these two aspects. In particular, starting with [14] the research on the computational complexity of the reasoning tasks associated with description logics has shown that in order to ensure decidability and or efficiency of reasoning in all cases, one must renounce some of the expressive power [76, 81, 83, 82, 47, 48, 49]. These results have led to a debate on the trade off between expressive power of representation formalisms and worst case efficiency of the associated reasoning tasks. Recently, this issue has been one of the main themes in the area of description logics, and has led to at least four different ....

....contains the details of a polynomial reduction from DI (CI) to D (C) which was not included in the original version of the thesis. 19 20 Preliminaries In this chapter we present the basic notions regarding both description logics and propositional dynamic logics. We refer the reader to [82] and [74] for an introduction to the subjects. We also prove some propositions to be used in the following chapters. 2.1 Description logics Description logics allow one to represent a domain of interest in terms of concepts and roles. Concepts model classes of individuals, while roles model ....

B. Nebel. Reasoning and Revision in Hybrid Representation Systems. Lecture Notes In Artificial Intelligence. Springer-Verlag, 1990.

....is a decidable logic that generalizes Ait Kaci s formalism by adding negation and quantification. Feature Logic makes explicit that Ait Kaci s terms, the feature descriptions developed by computational linguists [KB82, RK86, Joh88] and the knowledge representation language KL ONE [BS85, LB87, Neb89, SSS91, NS90] are all closely related members of the same family of logics. These logics offer attributive concept descriptions that are interpreted as sets and are built from sorts and binary relations (called attributes, roles or features) using set operations such as intersection, union and ....

....a decidable logic that generalizes Ait Kaci s formalism by adding negation and quantification. Feature Logic makes explicit that Ait Kaci s terms, the feature descriptions developed by computational linguists [KB82, RK86, Joh88] and the knowledge representation language KL ONE [BS85, LB87, Neb89, SSS91, NS90] are all closely related members of the same family of logics. These logics offer attributive concept descriptions that are interpreted as sets and are built from sorts and binary relations (called attributes, roles or features) using set operations such as intersection, union and ....

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Bernhard Nebel. Reasoning and Revision in Hybrid Representation Systems. PhD thesis, Universitat des Saarlandes, Saarbrucken, West Germany, June 1989. To appear in Lecture Notes in Artificial Intelligence, Springer-Verlag.

....Feature logic is a formalism for describing record structures, which in turn represent objects such as addresses or lexical entries by the values of their attributes. Feature logic has its origin in the three areas of knowledge representation with concept descriptions, frames, or y terms [13, 34, 35, 1], natural language processing, in particular approaches based on unification grammars [26, 24, 45, 43, 39, 42] and constraint (logic) programming [3, 5, 28, 47] An interesting recent application lies in software configuration management, where feature logic is used to denote software versions ....

Bernhard Nebel. Reasoning and Revision in Hybrid Representation Systems, volume 422 of Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, 1990.

.... concerned with implementation of systems, such as Klone, K Rep, Back, and Loom [19, 61, 70, 60] These systems employed so called structural subsumption algorithms, which first normalize the concept descriptions, and then recursively compare the syntactic structure of the normalized descriptions [62]. These algorithms are usually very e#cient (polynomial) but they have the disadvantage that they are complete only for very inexpressive DLs, i.e. for more expressive DLs they cannot detect all the existing subsumption instance relationships. At the end of this phase, early formal ....

B. Nebel. Reasoning and Revision in Hybrid Representation Systems. Lecture Notes In Artificial Intelligence. Springer-Verlag, 1990.

.... Term Calculus [2, 3, 4] and Kasper and Rounds logic [23, 39] employ set denoting expressions, called feature terms in this paper, that come in different syntactic guises (Figure 2 gives an example) Feature terms have much in common with the concept descriptions of terminological logics [5, 33, 34] used in knowledge representation. In fact, Ait Kaci s term calculus was developed independently from the linguistically oriented approaches for application in Logic Programming and knowledge representation. This paper shows that both kinds of feature descriptions can be captured as ....

....in F . 2 7 Sorts In this section we extend our logic to include sorts. For our purposes, a sort is simply a symbol denoting a subset of the domain of a feature algebra. Equivalently, one can regard a sort as a unary predicate. Our sorts correspond to the concepts of terminological languages [28, 33, 34] and to the templates of the PATR II system [46] They are different from sorts in sorted logics in that we don t exploit sorts to impose a well sortedness discipline on formulas. From now on we assume an additional alphabet whose symbols are called sorts. Furthermore, we assume that the ....

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B. Nebel. Reasoning and Revision in Hybrid Representation Systems, volume 422 of Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, Germany, 1990.

....a language L will be a set of expressions. A representation (r) is a set of expressions in L. No distinction will be made between ontologies, background knowledge and formal annotations: they will all be representations. There have been many studies of knowledge representation language semantics [Nebel, 1990] . The semantics is generally defined in model theory by using simple set theory. Usually, an interpretation function I , to a domain of interpretation D, is defined iteratively over the structure of the language L. The interpretation function is compositional, i.e. it builds the meaning of an ....

Bernhard Nebel. Reasoning and revision in hybrid representation systems. Lecture Notes in Artificial Intelligence 422. Springer Verlag, Berlin (DE), 1990.

....second order monadic logic. 1 Introduction Feature logic is a formalism to describe objects by the values of their attributes or features. It has its roots in the three areas of knowledge representation, with concept descriptions, frames, or y terms [Brachman Levesque, 1984, At Kaci, 1986, Nebel, 1990, Nebel Smolka, 1990] natural language processing, especially approaches based on unification grammars [Kay, 1979, Kaplan Bresnan, 1982, Shieber et al. 1983, Shieber, 1986, Pollard Sag, 1994, Rounds, 1997] and constraint programming languages with record structures [At Kaci Nasr, 1986, ....

Bernhard Nebel. Reasoning and Revision in Hybrid Representation Systems, volume 422 of Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, 1990.

....representation system or Tbox) from description of individuals (assertional representation system or Abox) Concepts are sets of individuals and roles represent binary relations between individuals. Concepts and roles descriptions are organized in hierarchies with the subsumption relation [6]. Several description logics systems are available; the ideas proposed in this paper are implemented with the CLASSIC system [7] Various works have been conducted to classify temporal structures, mainly in the field of plan recognition. 8] and [9] propose to extend the notion of subsumption to ....

Nebel, B., Reasoning and Revision in Hybrid Representation Systems. LNAI, 1990. 422.

....(e.g. explanatory text) Fig. 3. Multimodal Instructions for the Use of an Espresso Machine Currently the technical knowledge to be presented by WIP is encoded in a hybrid knowledge representation language of the KL ONE family including a terminological and assertional component (see [Nebel 90] In addition to this propositional representation, which includes the relevant information about the structure, the function, the behavior, and the use of the espresso machine, WIP has access to an analogical representation of the geometry of the machine in the form of a wire frame model. This ....

Nebel, B.: Reasoning and Revision in Hybrid Representation Systems. Lecture Notes in AI, Vol. 422, Berlin: Springer-Verlag (1990)

.... (the set of rules from our DLP) of Carin ALN , and as already discussed in section 2 we have dropped the terminological component of Carin ALN , but are instead able to express rules and assertions, where concepts are already expanded with respect to the (acyclic) terminological axioms [ Nebel, 1990a ] e.g. instead of separating terminological axioms T , rules R and assertions A as in the following example from [ Rouveirol and Ventos, 2000 ] They do not have a (stable) model, so they have to be interpretated as contradictory, but an interpretation as default rule may be more adequate. ....

Nebel, B.: 1990a, Reasoning and Revision in Hybrid Representation Systems. New York: Springer.

....see that the properties of # subsumption also hold for # I subsumption. This gives a possibility to represent cyclic concept definitions from a terminological component in a finite way. However we haven t checked the adequate semantic interpretation (descriptive, least or largest fix point [ Nebel, 1990a ] of these cyclic terms so far, so we do not consider this possibility in this paper, even if it would be helpful with respect to ABox abstraction by most specific concepts [ Baader and Kusters, 1998 ] There is no partition of literals such that one partition does not share at least one ....

.... (the set of rules from our DLP) of Carin ALN , and as already discussed in section 2 we have dropped the terminological component of Carin ALN , but are instead able to express rules and assertions, where concepts are already expanded with respect to the (acyclic) terminological axioms [ Nebel, 1990a ] e.g. instead of separating terminological axioms T , rules R and assertions A as in the following example from [ Rouveirol and Ventos, 2000 ] T = empty 0 has load, empty train car.empty car They do not have a (stable) model, so they have to be interpreted as contradictory, ....

Nebel, B.: 1990a, Reasoning and Revision in Hybrid Representation Systems. New York: Springer.

.... (the set of rules from our DLP) of Carin ALN , and as already discussed in section 2 we have dropped the terminological component of Carin ALN , but are instead able to express rules and assertions, where concepts are already expanded with respect to the (acyclic) terminological axioms [ Nebel, 1990a ] e.g. They do not have a (stable) model, so they have to be interpreted as contradictory, but an interpretation as default rule may be more adequate. But this is not a topic of this paper. instead of separating terminological axioms T , rules R and assertions A as in the following example ....

Nebel, B.: 1990a, Reasoning and Revision in Hybrid Representation Systems. New York: Springer.

....operations obtained from a given infobase. Notwithstanding these advances, much still needs to be done. Two obvious extensions that still needs to be developed has already been hinted at by Meyer et al. 13] Both involve the introduction of orderings of epistemic relevance in the spirit of Nebel [16, 17, 18]. It also remains to be seen how baisc infobase change fits into a more general theory of base change. Bibliography [1] Carlos E. Alchourr6n, Peter G irdenfors, and David Makinson. On the logic of theory change: Partial meet functions for contraction and revision. Journal of Symbolic Logic, ....

Bernhard Nebel. Reasoning and Revision in Hybrid Representation Systems, volume 422 of Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, 1990.

....# ## iff # ## ## # ## and # ## ## # ## # resp. ## ## # #### # iff ## ## # ### ## # #### # # Due to lack of space we do not discuss the subsumption problem in this paper, however we conjecture that it can be reduced to the satisfiability problem by a process similar to knowledge base expansion [7], as in the closely related fuzzy ### [8] Our unsatisfiability decision procedure closely follows [8] but introduces a new set of rules for the handling of concept modifiers. Starting from a set # of fuzzy constraints, we apply propagation rules to add simpler constraints preserving the ....

Bernhard Nebel. Reasoning and revision in hybrid representation systems. Springer, Heidelberg, FRG, 1990.

....instruct a user in filling the watercontainer of an espresso machine. 2 Fig. 1: Example Instruction Currently, the technical knowledge to be presented by WIP is encoded in a hybrid knowledge representation language of the KL ONE family including a terminological and assertional component (see Nebel 90) In addition to this propositional representation, which includes the relevant information about the structure, function, behavior, and use of the espresso machine, WIP has access to an analogical representation of the geometry of the machine in the form of a wireframe model. The automatic ....

Bernhard Nebel. Reasoning and Revision in Hybrid Representation Systems. Lecture Notes in AI, Vol. 422, Berlin: Springer-Verlag, 1990.

.... with a standard object oriented language (Smalltalk 80) and extending it with an embedded rule based layer, we built the NOpus system [Pachet 95] Turning to classical object oriented style (the so called message passing) causes some trouble to the knowledge representation specialist (see, e.g. Nebel 90] We refer to [Pachet 94] for a discussion on the technical problems posed by this integration. However, we feel that the benefit gained from potentially applying rules to the whole universe of object oriented models created by object oriented programmers does warrant these discrepancies from ....

Nebel B., Reasoning and Revision in Hybrid Representation Systems. Lecture Notes in Artificial Intelligence, Berlin: Springer-Verlag, 1990.

....of attribute values. There are several formalisms that denote sets of objects by their attributes, subsumed under the term description logics or terminological logics. Their most important domains are the areas of knowledge representation, where concept descriptions, also called frames [7, 37, 38], are used to represent sets of objects by attribute value combinations, and the semantic analysis of natural language [25, 27, 49] In programming languages, attribute value combinations are used in record structures. At Kaci was the first to study such structures mathematically, calling them ....

Nebel, B. Reasoning and Revision in Hybrid Representation Systems, vol. 422 of Lecture Notes in Artificial Intelligence. Springer-Verlag, 1990.

....is inductively defined, as shown in the third column of Table 1. A TBox is a finite set of concept definitions of the form A : C, where A is a concept name and C a concept description. In addition, we require that TBoxes are acyclic and do not contain multiple definitions (see, e.g. 8 ] In TBoxes of the DL ALE , negation may only be applied to concept names not occurring on the left hand side of a concept definition. An interpretation I is a model of the TBox T iff it satisfies all its concept definitions, i.e. A I = C I for all definitions A : C in T . One of the ....

.... r r w 2 : P r r w1 : P; Q w 0 : G(C 3 2 ) G(C 3 3 ) x1 : P; Q x 0 : w 3 : P; Q w 4 : Q w 5 : P; Q x 2 : P; Q x3 : P x4 : Q r r y 5 : Q y 6 : P y 7 : Q r r y 9 : P y 10 : P y 11 : Q r r y 12 : Q y 13 : P y 14 : Q r r y 2 : P y 3 : P y 4 : Q r r y 0 : y 1 : P y 8 : Q r r r r r r G(C 3 1 ) Theta G(C 3 2 ) Theta G(C 3 3 ) Figure 1: Description trees of C 3 1 ; C 3 2 ; C 3 3 and their product. This example can be generalized to an example that demonstrates that the lcs of n EL concept descriptions of size linear in n may be exponential ....

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B. Nebel. Reasoning and Revision in Hybrid Representation Systems, volume 422 of Lecture Notes in Computer Science. Springer--Verlag, 1990.

....these two aspects. In particular, starting with [9] the research on the computational complexity of the reasoning tasks associated with Description Logics has shown that in order to ensure decidability and or efficiency of reasoning in all cases, one must renounce to some of the expressive power [43, 45, 47, 46, 25, 26, 27]. These results have led to a debate on the trade off between expressive power of representation formalisms and worst case efficiency of the associated reasoning tasks. This issue has been one of the main themes in the area of Description Logics, and has led to at least four different approaches ....

B. Nebel. Reasoning and Revision in Hybrid Representation Systems, volume 422 of Lecture Notes in Artificial Intelligence. Springer-Verlag, 1990.

....facilities in these systems such as consistency checking, subsumption checking (classi cation) and realization were only known for rather trivial languages. However, in the last two years concept languages (term subsumption languages) have been thoroughly investigated (see for example [SS88, Neb90, HNS90, DLNN91] As a result of these investigations it is now possible to provide sound and complete algorithms for relatively large concept languages. In this paper we describe KRIS which is an implemented prototype of a kl one system where all reasoning facilities are realized by sound and ....

....sound and complete algorithms for reasoning facilities such as consistency checking, subsumption checking, retrieval, and querying. 1 Introduction and Motivation In the last decade many knowledge representation systems in the tradition of kl one [BS85] have been built, for example back [NvL88, Neb90] classic [BBMR89] kandor [Pat84] kl two [Vil85] krypton [BPGL85] loom [MB87] nikl [KBR86] sb one [Kob89] A common feature of these systems is the separation of the knowledge into a terminological part and an assertional part. Knowledge about classes of individuals and relationships ....

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B. Nebel. Reasoning and Revision in Hybrid Representation Systems, Lecture Notes in Articial Intelligence, LNAI 422, Springer Verlag, 1990.

....by AcknoSoft. Another famous classification procedure is implemented in PROTOS [ Porter et al. 1990 ] Also in this case, the terminological logics may help. Indeed reasoning services as abstraction may be used to define new concepts in the specialization hierarchy [ Coupey et al. 1998; Nebel, 1990 ] here, abstraction is a reasoning service of terminological logics that must not be confused with hierarchical abstraction as introduced in Section 2.1 and Section 6.1) For example, from the following set of facts: C(i 1 ) R(i 1 ; i 2 ) D(i 1 ) E(i 2 ) abstraction derives the new concept ....

B. Nebel. Reasoning and Revision in Hybrid Representation Systems, volume 422 of Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, Germany, 1990.

....common subsumer (lcs) which, given concept descriptions C 1 ; Cn , is the least concept description (w.r.t. subsumption) subsuming C 1 ; Cn . Thus, where the msc generalizes an individual, the lcs generalizes a set of concept descriptions. In [2 4] the msc ( rst introduced in [15]) and the lcs ( rst introduced in [5] have been proposed to support the bottom up construction of a knowledge base. The motivation comes from an application in chemical process engineering [17] where the process engineers construct the knowledge base (which consists of descriptions of standard ....

B. Nebel. Reasoning and Revision in Hybrid Representation Systems. LNAI 422, Springer-Verlag, 1990.

....particular analysis, or analysis failure is a result of the underlying theoretical principles or of the way the rules are implemented. Therefore, an appropriate robustness strategy should be based on a knowledge based approach in which (formalized) theory and algorithms are clearly separated, cf. [Nebel, 1990]. This would contribute to expandability and maintainability. Secondly, we prefer a solution to robustness that acknowledges the linguistic nature of the problem. In our view it is preferable to combat ungrammaticality with robust grammars rather than equipping NLEs with robustness mechanisms that ....

Bernhard Nebel. Reasoning and Revision in Hybrid Representation Systems. In J. Siekmann, editor, Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, 1990.

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Bernhard Nebel. Reasoning and Revision in Hybrid Representation Systems, volume 422 of Lecture Notes in Artificial Intelligence. SpringerVerlag, Berlin, Heidelberg, New York, 1990.

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Nebel, B. (1990) Reasoning and Revision in Hybrid Representation Systems, New York: Springer.

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Nebel, B.: Reasoning and Revision in Hybrid Representation Systems. Number 422 in Lecture Notes in Artificial Intelligence. Springer-Verlag (1990)

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B. Nebel. Reasoning and revision in hybrid representation systems. Springer, Heidelberg, FRG, 1990.

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Nebel, B. 1990. Reasoning and Revision in Hybrid Representation Systems. Springer--Verlag.

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Bernhard Nebel. Reasoning and revision in hybrid representation systems. Springer, Heidelberg, FRG, 1990.

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Nebel, B. (1990). Reasoning and revision in hybrid representation systems. Springer, Heidelberg, FRG.

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B. Nebel, "Reasoning and revision in Hybrid Representation systems". Lecture Notes in Artificial Intelligence 422, Springer-Verlag, ISBN 3-540-52443-6, 1987.

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B. Nebel. Reasoning and revision in hybrid representation systems. Springer, Heidelberg, FRG, 1990.

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Bernhard Nebel. Reasoning and Revision in Hybrid Representation Systems, volume 422 of Lecture Notes in Artificial Intelligence. Springer-Verlag, 1990.

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B. Nebel. Reasoning and Revision in Hybrid Representation Systems, volume 422 of Lecture Notes in Arti cial Intelligence. Springer-Verlag, Berlin, Heidelberg, New York, 1990. 5

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B. Nebel. Reasoning and revision in hybrid representation systems. volume 422 of Lecture Notes in Artificial Intelligence. Springer-Verlag, 1990.

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Bernhard Nebel. Reasoning and revision in hybrid representation systems. Springer, Heidelberg, FRG, 1990.

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Bernhard Nebel. Reasoning and revision in hybrid representation systems. Springer, Heidelberg, FRG, 1990.

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B. Nebel, Reasoning and Revision in Hybrid Representation Systems. Berlin: Springer (LNAI 422), 1990

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B. Nebel. Reasoning and Revision in Hybrid Representation Systems. volume 422 of LNCS, page 300. Springer-Verlag, New York, 1990.

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Bernhard Nebel. Reasoning and Revision in Hybrid Representation Systems. Number 422 in LNAI. Springer-Verlag, New York, 2nd edition, 1995.

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Nebel, B.: Reasoning and Revision in Hybrid Representation Systems. Number 422 in Lecture Notes in Artificial Intelligence. Springer-Verlag (1990)

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Bernhard Nebel. Reasoning and Revision in Hybrid Representation Systems, volume 422 of Lecture Notes in Artificial Intel ltel e. Springer-Verlag, 1990.

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B. Nebel, Reasoning and Revision in Hybrid Representation Systems. Berlin: Springer (LNAI 422), 1990

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B. Nebel, Reasoning and Revision in Hybrid Representation Systems. Berlin: Springer (LNAI 422), 1990

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Nebel, B.: Reasoning and Revision in Hybrid Representation Systems. Number 422 in Lecture Notes in Artificial Intelligence. Springer-Verlag (1990)

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B. Nebel, Reasoning and Revision in Hybrid Representation Systems. Berlin: Springer (LNAI 422), 1990

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: B. Nebel, Reasoning and Revision in hybrid representation systems, LNAI, n422, 1990.

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Nebel, B. Reasoning and Revision in Hybrid Representation Systems. Lecture Notes in AI 422, Springer (1990).

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B. Nebel, Reasoning and revision in hybrid representation system, (Lecture Notes in Artificial Intelligence 422, Springer-Verlag, 1990).

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Bernhard Nebel. Reasoning and Revision in Hybrid Representation Systems, volume 422 of Lecture Notes in Artificial Intelligence. Springer-Verlag, 1990.

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