CHANDRASEKARAN, B. (1986). Generic tasks in knowledge-based reasoning: high-level building blocks for expert system design. IEEE Expert, 1, 23--30.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... rule based systems and custom programs, Problem Solving Methods (PSMs) were introduced as a knowledge engineering paradigm to encode domain independent, systematic and reusable sequences of inference steps involved in the process of solving certain kinds of application tasks with domain knowledge [8, 5, 20]. One of the first such strategy to have been identified, as a result of analyzing several rule based systems such as Mycin, was heuristic classification [9] the process of identifying the class of an unknown domain situation. This strategy involves three main inference steps: first, abstracting ....

....it participates in the design and implementation of knowledge based applications. 1. 2 Problem Solving Methods PSMs became prominent as a result of: 1) observing recurring patterns of reasoning such as heuristic classification [9] as mentioned above, 2) identifying high level generic tasks [5] ubiquitously performed with knowledge, such as hypothesis assessment and data abstraction, and (3) comparing di#erent role limiting methods, such as propose and revise and cover and differentiate, in the goal of characterizing a taxonomy of methods to guide system modeling [20] PSMs were ....

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B. Chandrasekaran. Generic Tasks for Knowledge-Based Reasoning: High-Level Building Blocks for Expert System Design. IEEE Expert, 1(3):23--30, 1986.

....the classical depth first and breadth first search algorithms. This algorithm is somewhat reminiscent of the hill climbing heuristic sometimes used for searching purposes. There are several knowledge modelling techniques in the literature such as the generic task, proposed by Chandrasekaran [8]also present with some variants in the KADS model [14] and in the model of components of expertise [13]and the role limiting method of McDermott [11] According to these techniques, there are two organizational principles: the task oriented principle and the domainoriented principle. Our approach ....

Chandrasekaran, B., "Generic tasks in knowledge-based reasoning: High-level building blocks for expert systems design", IEEE Expert, 23--30, 1986.

....or deciding if a clinical intervention, such as a specific insulin therapy regimen, has been effective. 0 400 200 100 50 D D 1000 2000 D ( D D D D D D . Granulocyte counts . D D D D Time (days) Platelet counts PAZ protocol M[0] M[1]M[2]M[3] M[0] M[0] BMT Expected CGVHD Figure 1. Typical inputs to and outputs of the temporal abstraction task in a clinical domain. The figure presents abstractions of platelet and granulocyte values during administration of the PAZ protocol for treating patients who have chronic graft versus host ....

....of the abstractions. The past decade has witnessed considerable advances in semiautomated methods for knowledge acquisition and knowledge representation, based on approaches that operate at the knowledge level [23] and that assume taskspecific but domain independent problem solving methods [3, 5, 11, 20, 21, 43] which often succeed in alleviation of the knowledge acquisition bottleneck. However, these methods often are not associated with runtime, interactive end user applications, and focus on use by knowledge engineers and domain experts. Thus, visualization of time oriented abstractions of clinical ....

Chandrasekaran, B. (1986). Generic tasks in knowledgebased reasoning: High-level building blocks for expert system design. IEEE Expert 1, 23--30.

....knowledge acquisition. 1. Introduction In the 1980s, several researchers began to develop task specific architectures where specific problem solving methods , such as propose and revise and cover and differentiate, are used to solve classes of problems, such as configuration and diagnosis [7] [23] The first generation model based knowledge acquisition (KA) tools, such as SALT for the propose and revise method [22] PROTG for the episodic skeletal plan refinement (ESPR) method [24] and ROGET for heuristic classification [4] use knowledge roles defined by these problem solving ....

B. Chandrasekaran, Generic tasks for knowledge-based reasoning: high-level building blocks for expert system design , IEEE Expert. 1 (1986) 23--30.

....Keravnou and Washbrook introduce ########, ########, and ###### to distinguish various types of instantaneous and interval based information (patient specific or general) 6] 3. Temporal Reasoning Temporal reasoning has been used in medical domains as part of a wide variety of generic tasks [76], such as diagnosis (or, in general, abstraction and interpretation) monitoring, projection, forecasting, and planning. These tasks are often interdependent. Projection is the task of computing the likely consequences of a set of conditions or actions, usually given as a set of causeeffect ....

Chandrasekaran B. Generic tasks in knowledge-based reasoning: High-level building blocks for expert system design. IEEE Expert 1986; (1):2330.

.... Note that our approach differs from the recently appeared Web services, that tend to offer only predefined, limited reasoning functionalities, very specific to a given domain context (see Section 4) Our approach stems from research on knowledge based system (KBS) development by reuse [1, 3, 4, 6, 7, 11]. This area has identified domainindependent problem solving strategies, or problem solving methods (PSMs) 1, 6, 7, 11, 12] that provide standard ways of addressing stereotypical knowledge intensive problems, or generic tasks [4] such as diagnosis, design, and classification. To foster the ....

.... knowledge based system (KBS) development by reuse [1, 3, 4, 6, 7, 11] This area has identified domainindependent problem solving strategies, or problem solving methods (PSMs) 1, 6, 7, 11, 12] that provide standard ways of addressing stereotypical knowledge intensive problems, or generic tasks [4], such as diagnosis, design, and classification. To foster the reuse of PSMs, structured libraries have been developed, in which the methods are indexed and retrieved for different domains and purposes [1, 3, 6, 11 13] Furthermore, comprehensive frameworks take a PSM centered view to support KBS ....

Chandrasekaran, B. Generic tasks for knowledge-based reasoning: High-level building blocks for expert system design. IEEEExpert, 1 (3). 23-30.

....online a generic classification technology, which different communities (e.g. paleontologists, geologists, biologists) or individual users can adapt for specific domains or applications. Our work is situated in the context of research on knowledge based system (KBS) development by reuse [1, 4, 5, 7, 9, 14]. Research in this area has identified recurring patterns of reasoning that occur in solving knowledgeintensive problems. These domain independent problemsolving strategies, or problem solving methods (PSMs) 1, 7, 9, 14, 16] provide standard ways of addressing stereotypical problems, or generic ....

....in this area has identified recurring patterns of reasoning that occur in solving knowledgeintensive problems. These domain independent problemsolving strategies, or problem solving methods (PSMs) 1, 7, 9, 14, 16] provide standard ways of addressing stereotypical problems, or generic tasks [5], such as diagnosis, design, and classification. To foster the reuse of PSMs, structured libraries have been developed, in which the methods are indexed and retrieved for different domains and purposes [1, 4, 7, 14, 16, 17] Comprehensive frameworks and methodologies for developing KBSs from ....

Chandrasekaran, B. Generic tasks for knowledge-based reasoning: High-level building blocks for expert system design. IEEE Expert, 1 (3). 23-30.

....MIT Press. Sacerdoti, 1974] Sacerdoti, E. D. 1974) Planning in hierarchy of abstraction spaces. Artificial Itclligccc, 5:115 135. Samuel, 1963] Samuel, A. L. 1963) Some studies in machine learning using the game of checkers. Computers and Thought, pages 71 105. Sembugamoorthy and Chandrasekaran, 1986] Sembugamoorthy, V. and Chandrasekaran, B. 1986) Functional representation of devices and compilation of diagnostic problem solving systems. Expcr iccc Mcmor L ad lrlcasoig, pages 47 73. Shortliffe, 1976] Shortliffe, E. H. 1976) Computer based Medical Cosultatio: MYCIN. American Elsevier, ....

....E. D. 1974) Planning in hierarchy of abstraction spaces. Artificial Itclligccc, 5:115 135. Samuel, 1963] Samuel, A. L. 1963) Some studies in machine learning using the game of checkers. Computers and Thought, pages 71 105. Sembugamoorthy and Chandrasekaran, 1986] Sembugamoorthy, V. and Chandrasekaran, B. 1986). Functional representation of devices and compilation of diagnostic problem solving systems. Expcr iccc Mcmor L ad lrlcasoig, pages 47 73. Shortliffe, 1976] Shortliffe, E. H. 1976) Computer based Medical Cosultatio: MYCIN. American Elsevier, New York. Smith et al. 1985] Smith, Jr. J. ....

Chandrasekaran, B. (1986). Generic tasks in knowledge- based reasoning: High-level building blocks for expert system design. IEEE Expert, pages 23 30. Fall 1986.

....in open data base. KEYWORDS: Information Retrieval, Citation, History, In dex INTRODUCTION We have many chances today to prepare documents with a personal computer or on a word processor. There are a variety of documentation support tools such as outline processors [2] idea processor [3, 1] and groupwares[13, 7] Recently, there has been research for computer supported creation[4, 8, 14] as well. But these systems are not for expressing ideas on texts, not for developing ideas through writing lines of texts but only for handling words or formats of texts. They can neither process ....

Chandrasekaran, B. 1986. Generic Tasks in KnowledgeBased Reasoning: High-Level Building blocks for expert system design. IEEE Expert, Fall, pages 23-30

....at the symbol level in terms of representations, data structures and processes. The knowledge level paradigms are: heuristic classification [Clancey 1985] distinction between deep and shallow knowledge [Keravnoe and Washbrook 1989] the problem solving method [McDermott 1988] and generic tasks [Chandrasekaran 1986]. Heuristic classification focuses on the inference structure that underlies expertise, while the deep shallow knowledge distinction focuses on the theoretical structure and contents of domain knowledge. The problem solving method focuses neither on inference structure nor on domain knowledge, but ....

.... shallow knowledge Deep knowledge, causal relations, shallow knowledge Keravnoe and Washbrook 1989 Problem solving method Problem decomposition, domain independent strategies, sequencing inferences McDermott 1988 Generic tasks Problem type, problem decomposition, task, ordering of tasks Chandrasekaran 1986 Epistemological model Ontology, inference model, medical tasks Ramoni et al. 1990 Development philosophy Need, development methodology, methods, metrics, tools, integral evaluation, professional approach Heathfield and Wyatt 1993 The object of a knowledge based system has been construed as ....

Chandrasekaran B, Generic tasks in knowledge-based reasoning: High level building blocks for expert systems. IEEE Expert, Fall 1986, 23-30.

....and thus M. Benaroch Goal Directed Reasoning with ACE SSM 2 2 enabled KBSs to explain some aspects of their behavior. More importantly, many subtasks capturing S became viewed as task independent reusable modules, because they were found to be common to various applications. Chandrasekaran [4], who termed such modules generic tasks (GTs) showed that a complete inference procedure for an application can be functionally configured as a hierarchy of GTs that were specialized for the application. McDermott [18] relatedly found that some hierarchies of GTs form taskspecific inference ....

Chandrasekaran, B., "Generic Tasks in Knowledge-Based Reasoning: High-Level Building Blocks for Expert System Design," IEEE Expert, 1(3), pp. 23-30, 1986.

....Examples of such control knowledge are the order in which observations must be obtained during diagnostic reasoning, or the order in which components must be configured during design reasoning. Many Knowledge Engineering methodologies provide some form of expressing the control knowledge in a KBS [28, 20, 3, 27]. A more recent, and less explored approach to dealing with the intractability of KBSs is the development of anytime algorithms [19] An anytime algorithm gradually approaches the perfect solution. As runtime increases, the quality of the solution increases. The algorithm can be interrupted at ....

B. Chandrasekaran. Generic tasks in knowledge based reasoning: High level building blocks for expert system design. IEEE Expert, 1(3):23--30, 1986.

....However, many other approaches emerged, aimed at a more direct way of support: by reuse. This was enabled by a direct mapping of conceptual models to operational, runnable models, as in role limiting methods (Marcus, 1988) SBF (Klinker, Bhola, Dallemagre, Marques McDermott, 1991) generic tasks (Chandrasekaran, 1986), EXPECT (Swartout Gil, 1995) PROTE#GE# (Musen, 1989) and in KREST, the components of expertise workbench (Steels, 1993; Geldof Slodzian, 1994) Where interpretation models provided a sketch or outline of a model, these reusable models or components are real blueprints that can be copied. If ....

....of generic models, below) Modeling components have a fixed structural relationship with each other, i.e. their connections in terms of contents and role in modeling are known beforehand. f Generic models are frames that represent a class of complete expertise models, much like generic tasks (Chandrasekaran, 1986). A generic model is an expertise model frame, complete in form but not necessarily in contents. This notion of generic model provides for any kind of model to support a modeling approach. f Modeling operators are relations between generic models, representing a potential step in modeling. A ....

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CHANDRASEKARAN, B. (1986). Generic tasks in knowledge-based reasoning: high level building blocks for expert system design. IEEE Expert, 1, 23---30.

....tools present to the user a language of predesigned terms that are the parameters of domain independent problem solving methods, each of which is designed to solve a particular class of tasks. These algorithmic methods are called generic task problem solvers [Bylander and Chandrasekaran, 1987; Chandrasekaran, 1986] and the parameters are called roles of the problem solving method [McDermott, 1988] For well 3 understood classes of tasks and problem solving methods, the system designer can build a method specific shell and associated knowledge acquisition tool that can elicit statements in these terms, ....

Chandrasekaran, B. (1986). Generic tasks in knowledge-based reasoning: high-level building blocks for expert system design. IEEE Expert, 1(3), 23--30.

....of knowledge based systems there is a recognizable shift from the traditional rapid prototyping approach to model based approaches. The need for explicit and higher level descriptions of problem solving methods arose in knowledge acquisition [40] 20] in attempts to reuse knowledge bases [5] and in research on explanations [22] and tutoring [9] Newell [23] argued that the knowledge level is the right level for these purposes. All model based approaches to problem solving fall into one of two categories: Either they provide a universal framework to specify arbitrary problem ....

B. Chandrasekaran. Generic tasks in knowledge-based reasoning: High-level building blocks for expert system design. IEEE Expert, 1(4):279 -- 299, 1986.

....strategies (top down design) 2 . 6 Generic Tasks: The Initial Formulation By 1983, we had gained experience with several tasks. In particular we were beginning to learn how to decompose a complex task into component tasks. Chandrasekaran had started formulating the notion of a Generic Task [11, 13]. The main intuition was that classification, data retrieval, plan selection and refinement, state abstraction and abductive assembly all were in some sense re usable subtasks. These subtasks were proposed as especially useful as components in other more complex problem solving tasks. In our work ....

Chandrasekaran, B.: Generic tasks in knowledge-based reasoning: High-level building blocks for expert system design. IEEE Expert, 1(3):23-30, 1986

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CHANDRASEKARAN, B. (1986). Generic tasks in knowledge-based reasoning: high-level building blocks for expert system design. IEEE Expert, 1, 23--30.

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B. Chandrasekaran. Generic tasks in knowledge based reasoning: High level building blocks for expert system design. IEEE Expert, 1(3):23--30, 1986.

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Chandrasekaran B., "Generic tasks in knowledge based reasoning: High level building blocks for expert systems design", IEEE Expert, 1986.

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Chandrasekaran, B.: Generic Task in Knowledge Based Reasoning: High level building blocks for expert systems design. IEEE Expert, 1, (1986) 23-29. Reimpreso en Buchanan,B.G. y Wilkins,D.C. (eds.) Reading in Knowledge acquisition and learning. Morgan Kaufmann. San Mateo. CA. (1993) 170-177

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Chandrasekaran, B. Generic Tasks in Knowledge-based reasoning: High level building blocks for system design. IEEE expert 1986; 1(3): 23-30.

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Chandrasekaran B. Generic Tasks in knowledge-based reasoning: high-level building blocks for Expert System design. IEEE Expert, vol. 1(3), p.23-30.

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Chandrasekaran B. Generic tasks in knowledgebased reasoning: High-level building blocks for expert system design. IEEE Expert, 1986; 1: 23-- 30.

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B. Chandrasekaran, "Generic Tasks in Knowledge-Based Reasoning: High-Level Building Blocks for ES Design", IEEE Expert, pp. 23-30, Fall 1986.

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B. Chandrasekaran. Generic Tasks in Knowledge-Based Reasoning: High-Level Building Blocks for Expert System Design. IEEE Expert, 1(3):23--30, 1986.

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