H.J. Levesque and R.J. Brachman. A fundamental tradeoff in knowledge representation and reasoning. In R.J. Brachman and H.J. Levesque, editors, Readings in Knowledge Representation. Morgan Kaufman, 1985.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....The state vector can be expanded by adding additional elements to the state vector to represent the conjunctive evidence. 17 8 Related Work: AI HFC is a knowledge based system defined by a set of propositions which have a truth value, and a theory which can reason over the propositions [31, 42]. The propositions are defined over the values of the , and F vectors. The theory which reasons over the propositions is defined by the K matrix. At any point in time, working memory consists of propositions describing the domain variables and their values in the domain. No accumulation of ....

H. J. Levesque and R. J. Brachman. A Fundamental Tradeoff in Knowledge Representation and Reasoning (Revised Version). In R. J. Brachman and H. J. Levesque, editors, Readings in Knowledge Representation, pages 41-70. Kaufmann, Los Altos, CA, 1985.

....semantic relevance (scope views to just the currently relevant task) validation mechanisms; abstraction mechanisms; and formal foundation. Background on these principles may be found in [1, 4, 25, 26] Practical trade offs between design criteria can arise, e.g. expressibility tractability [29] and parsimonyconvenience [18] In this paper our focus is on validation mechanisms, expressibility and orthogonality. The most debatable feature of ORM is its avoidance of attributes in the base model. This omission was originally made to avoid fuzzy and unstable distinctions about whether a ....

Levesque, H. 1984, `A fundamental trade-off in knowledge representation and reasoning', Proc. CSCSI-84, London, Ontario, 141-52.

....objects in Obj, but in this case, t would be useless and should not be included in the ontology definition. Another remark concerns the subsumption relation OE . We interpret subsumption by strict set inclusion (ae) and not by set inclusion ( as it is commonly done (see extensional subsumption [17]) Roughly, if subsumption was interpreted by set inclusion ( then a cycle (cycles will be defined formally in section 4) might induce that a term t is synonym to a term t 0 , although t may have been declared that subsumes t 0 . However this phenomenon does not fit well to the axiomatic ....

H. Levesque and R. Brachman. A Fundamental Tradeoff in Knowledge Representation and Reasoning (revised version)". In Readings in Knowledge Representation. Morgan Kaufmann Publishers, 1985.

....has always been a temptation in KR to set the sights either too low (and provide only a data structuring facility with little or no inference) or too high (and provide a full theorem proving facility) 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 ....

H. Levesque and R. Brachman. A fundamental tradeoff in knowledge representation and reasoning (revised version). In R. Brachman and H. Levesque, editors, Readings in Knowledge Representation, pages 41--70. Morgan Kaufmann, Los Altos, California, 1985.

....Hence, we prefer pragmatic criteria such as KB testability and KB maintainability which reflect usual case behaviour. However, we apply the technique of instance generators in subsequent sections. Expressibility vs tractability trade offs are discussed extensively in the KR literature (e.g. [Levesque Brachman, 1985]) Solutions to a class of problems (the NP hard problems) are known to have an exponential upper bound on their runtimes; i.e. may be intractable. Much of the KR literature is concerned with finding restrictive cases in which a representation can be shown to tractable; i.e. worst case runtimes ....

Levesque, H. & Brachman, R. (1985). A Fundamental Tradeoff in Knowledge Representation and Reasoning (Revised Version). In Brachmann, R. & Levesque, H., (Eds.), Readings in Knowledge Representation, pages 41--70. Palo Alto, Morgan Kaufmann.

....at odds with the goal of flexibility. This conclusion has been reached by a number of researchers in artificial intelligence and machine learning. Increasing the expressive power of the language in which knowledge is encoded can make it more difficult to perform valid inference and reasoning (Levesque and Brachman, 1985) and can hinder the learning of concepts (Haussler, 1988) These kinds of tradeoffs, between representational power and tractability of computation, also appear in the design of artificial neural networks. The exact manner in which information is represented in such networks the way in which ....

Levesque, H. J. and Brachman, R. J. (1985). A fundamental tradeoff in knowledge representation and reasoning. In Brachman, R. J. and Levesque, H. J., editors, Readings in Knowledge Representation, chapter 4, pages 41--70. Morgan Kaufmann, San Mateo.

....as multiple copies of a class frame, with each instance sharing the attributes of the class, but differing in attribute values. In addition, hierarchical frame based representations are object oriented and modular 4 (Bobrow and Stefik, 1986; Brachman, 1979; Brachman, Fikes and Levesque, 1983; Levesque and Brachman, 1984; Stefik and Bobrow, 1986) This paper describes the methods we use to predict enzyme action in our knowledge based simulation. The prediction of enzyme action is the first step in the development of a system that simulates entire metabolic pathways (Galper, et al. 1990) Section 2 briefly ....

Levesque, H. J. and Brachman, R. J. (1984) A fundamental tradeoff in knowledge representation and reasoning. Proceedings of the CSCI/SCEIO Conference 1984, CSCI, London, Ontario.

.... be expressive enough to represent the rich variety of knowledge used in any intelligent activity [DP91] Not unexpectedly, there is a tradeoff between the expressiveness of a KR system and the tractability of its services increasing the expressiveness generally decreases the tractability [LB85] Studying ways to make this tradeoff between expressiveness and tractability, also known as the intractability problem, is a central focus of research in KR. There are several general approaches to the intractability problem (c.f. Cra92] 1. Restrict the expressiveness of the KR system (i.e. ....

H.J. Levesque and R.J. Brachman. A fundamental tradeoff in knowledge representation and reasoning (revised version). In R.J. Brachman and H.J. Levesque, editors, Readings in Knowledge Representation, pages 41--70. Morgan Kaufmann, Los Altos, California, 1985.

....D if the extension of C is always a subset of the extension of D. Other inference tasks of great utility such as equivalence or classification can be reduced to satisfiability and subsumption. Reasoning about individuals is also provided with these logics. Since the seminal works in the field [14] [15] reasoning in DLs and the tradeoff between between expressiveness and tractability have been deeply studied, leading to important results (see [7] for a survey) Terminological languages (also called concept languages) are implementations of DLs. Classic [22] and Fact [13] are examples of ....

H.J. Levesque and R.J. Brachman, `A fundamental tradeoff in knowledge representation and reasoning (revised version)', in Readings in Knowledge Representation, eds., R.J. Brachman and H.J. Levesque, 817--823, Morgan Kaufmann, Los Altos, CA, (1985).

....into FOL [15] although he remarks (p. NN) that hierarchical representation similar to frames and semantic networks may require default logics [34, 3] an opposing view on frames, which treats frames as terminological definitions, is to be found in the work of Brachmann, Levesque et al. on krypton [4, 5], 33] If this assertion is correct (and I believe that, essentially, it is) it does not mean that we should stop trying to develop new representational formalisms and write everything down in FOL, but that FOL should be used as the gold standard against which we measure our proposals. The ....

Brachman, R. and Levesque, H., A Fundamental Tradeoff in Knowledge Representation and Reasoning, Proc. CSCSI-84, pp. 414-152, London, Ontario, 1984.

....by compiling concept descriptions in a general frame based language into a tractable form. Journal of the ACM (to appear) 1 1 Introduction The study of the computational properties of knowledge representation systems has revealed a direct trade off between tractability and expressiveness [Levesque and Brachman, 1985]. In general, in order to obtain a computationally efficient representation system one either restricts the expressive power of the knowledge representation language or one uses an incomplete inference mechanism. In the first approach, the representation language is often too limited for practical ....

....in our later paper. In particular, we are investigating decidable (and possibly tractable) approximations to theories in undecidable languages. 10 3.1. 3 Description Logics In this section, we consider description logics, a family of frame based knowledge representation languages as studied by Levesque and Brachman [1985]. See also [Donini et al. 1991] Levesque and Brachman consider a language FL in which one can describe structured concepts in terms of other concepts, either complex or primitive. For example, if we wished to describe the concept of a person whose male friends are all doctors with some ....

Levesque, H.J. and Brachman, R.J. A fundamental tradeoff in knowledge representation and reasoning (revised version) . In Brachman, R.J. and Levesque, H.J., Eds. Readings in Knowledge Representation. Morgan Kaufmann, Los Altos, CA, 41--70.

....said Blank in column P row b: 1 if Qc = Sd = 0, 0 otherwise . Let s call statements of the former type key terminology , and of the latter type key assertions , loosely following the distinction introduced between terminological and assertional knowledge (cf. Brachman, Fikes and Levesque 1983, Levesque and Brachman 1985). 3.3 Unlimited abstraction representational systems Finally, let us say that a system is an unlimited abstraction representational system (uars) if it expresses dependencies either inside a representation, with equations or whatever, or outside the representation, via key assertions. In itself, ....

Levesque, H. and Brachman, R. (1985). A fundamental tradeoff in knowledge representation and reasoning (revised version). In Brachman, R. and Levesque, H. (Eds.) Readings in Knowledge Representation. Los Altos, Ca.: Morgan Kaufmann Publishers, Inc.

....2 [ Gamma : Gamma]Q 3 are in the KB, then it would be reasonable to add Q 1 [ Gamma : Gamma]Q 3 also. The purpose of adding it would be to ease future computations. Substantial efforts have been expended towards determining the classes of logical deductions that are computationally feasible [KS91, LB85, Lev93], and these have been found to be somewhat restricted. c) Learning to reason: The goal here is to build up a KB from examples in such a way that the validity or the probability of truth in D can be tested for any query Q in a certain class. A fixed large enough random sample of examples is ....

H.J. Levesque and R.J. Brachman. A fundamental tradeoff in knowledge representation and reasoning. In R.J. Brachman and H.J. Levesque, editors, Readings in Knowledge Representation, pages 41--70. Morgan Kaufmann, 1985.

....at Austin [1] Section 4 then concludes the paper, highlighting the primary tenets of SEPA research. 2 CHALLENGES POSED TO REQUIREMENTS MANAGEMENT AND QUERY TOOLS Developers of AI based decision support tools are faced with difficult design tradeoffs regarding representation structure and search [6]. The complexities inherent to requirements representation and refinement make requirements management a particularly challenging domain that plainly illustrates these tradeoffs. This section discusses selected issues confronted when designing a tool to support the requirements analysis and ....

.... Representation of Application and Site Requirements (domain independent requirements) Informat ion Available to RIVT Query Facility [1] Domain Reference Arch t e c t u r e Representation of Doma in Functionality and Data (domain specific requirements) 3] Technology Repository [6] Application Registration to I nfra str ucture it requires Application Registration to Domain Functions and Data that it sati sfie s [5] Representation of New and Legacy Applications [4] Figure 3 SEPA representation basis for requirements interrogation and analysis The ....

[Article contains additional citation context not shown here]

Levesque, H. J. and J., B. R. A Fundamental Tradeoff in Knowledge Representation and Reasoning. In Proceedings of CSCSI/SCEIO Conference (London, Ontario, 1984),

....within the other (section 5) The conclusion summarizes the main points and identifies topics for future research. Multi valued attributes Language design often involves a number of trade offs between competing criteria. One well known trade off is that between expressibility and tractability [18]: the more expressive a language is, the harder it is to make it efficiently executable. Another tradeoff is between parsimony and convenience: although ceteris paribus, the fewer concepts the better (cf. Occam s razor) restricting ourselves to the minimum possible number of concepts may ....

Levesque, H. 1984, `A fundamental trade-off in knowledge representation and reasoning', Proc. CSCSI-84, London, Ontario, 141-52.

....of modern declarative programming languages, such as Concurrent Constraint Logic Programming Languages. The system has been implemented and tested in a wide range of architectures, exhibiting encouraging results. 1. Introduction Functional approaches to Knowledge Representation (KR) [24, 4] have led to the introduction of control primitives in the form of computation in some domains other than Herbrand Universe. One of the primary advantages of this approach is the ability of the system to use efficient non deductive computational methods, suitable for each of these domains. Using ....

....constraint store with Tell operations for getting a more specific answer (true or false) Consequently, Ask operation suspends the computation until some Tell operation add more information in the store. Tell and Ask operations have been introduced by Saraswat [34] after Milner [26] and Levesque [24]. By these two basic operations on constraints, i.e. Ask and Tell, we can formulate a framework of computation with synchronization on variables of constraints. 3. Modeling Parallel Paradigms with AGs 3.1. Parallel AG Evaluation One of the most interesting properties of AGs is that they are ....

H. J. Levesque and R. J. Brachman. A fundamental tradeoff in knowledge representation and reasoning. In Readings in Knowledge Representation, pages 41--70. Morgan Kaufmann Publishers, 1985.

.... According to software engineering standards in knowledge based systems development, it is preferable (and even a generally accepted prerequisite) to explicitly represent information in separate knowledge bases, so as to make the whole system clearly and modularly structured, cf. Win83] LB85] and [Neb90] Systems in which knowledge and algorithms are separated (syntax directed systems) thus allow for more transparent software that is easier to maintain and expand. Moreover, syntax directed systems allow for compactly and intuitively written grammars and no mechanisms that can be used ....

Hector J. Levesque and Ronald J. Brachman. A Fundamental Tradeoff in Knowledge Representation and Reasoning. In R. J. Brachman and H.J. Levesque, editors, Readings in Knowledge Representation, pages 42--70. Morgan Kaufmann, San Mateo, Califonia, 1985.

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H.J. Levesque and R.J. Brachman. A fundamental tradeoff in knowledge representation and reasoning. In R.J. Brachman and H.J. Levesque, editors, Readings in Knowledge Representation. Morgan Kaufman, 1985.

No context found.

H. Levesque and R. Brachman. A Fundamental Tradeoff in Knowledge Representation and Reasoning (revised version)". In Readings in Knowledge Representation. Morgan Kaufmann Publishers, 1985.

No context found.

Levesque, H., & Brachman, R. A fundamental tradeoff in knowledge representation and reasoning (revised version). In R. Brachman & H. Levesque Readings in knowledge representation. Los Altos, CA: Morgan Kaufmann.

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H. J. Levesque and R. J. Brachman. A fundamental tradeoff in knowledge representation and reasoning. In R. J. Brachman and H. J. Levesque, editors, Readings in Knowledge Representation, pages 41--70. Morgan Kaufmann, Los Altos, CA, 1985.

No context found.

H.J. Levesque and R.J. Brachman. A fundamental tradeoff in knowledge representation and reasoning (revised version). In R.J. Brachman and H.J. Levesque, editors, Readings in Knowledge Representation, pages 41--70. Morgan Kaufmann, Los Altos, CA, 1985.

No context found.

Levesque, H. J., A Fundamental Tradeoff in Knowledge Representation and Reasoning (Revised Version), in: Brachman, R. J. and Levesque H. J. (eds.), Readings in Knowledge Representation, Morgan Kaufmann Publishers, Los Altos, CA, 1985, pp. 41--70.

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H. Levesque and R. Brachman. A fundamental tradeoff in knowledge representation and reasoning. In R. Brachman and H. Levesque, editors, Readings in Knowledge Representation. Morgan Kaufman, 1985.

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Levesque H.J., Brachman R.J.: A fundamental tradeoff in knowledge representation and reasoning. In R.J. Brachman, H.J. Levesque, editor, Readings in Knowledge Representation, Morgan Kaufmann, (1985). 41-70.

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