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.

....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 [ Wielinga and Breuker, 1986 ] Marcus, 1988 ] in attempts to reuse knowledge bases [ Chandrasekaran, 1986 ] and in research on explanations [ Neches et al. 1985 ] and tutoring [ Clancey, 1986 ] Newell [ Newell, 1982 ] argued that the knowledge level is the right level for these purposes. 1 This document is part of a research project partially funded by the Esprit Basic Research Programme of the ....

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

....create use dependsOn Figure 8 Decision Oriented Process Model step, the team members have to execute one of several tasks. Table 1 lists these steps together with the tasks and humans pertinent to them. In order to classify the tasks, we have adopted the generic task model as described in [Chandrasekaran, 1993]. We have developed suitable computer support for QFD 1 based on our findings. Each of these steps mentioned above consists of a series of actions listed below. After the start of each step, the team leader (coordinator) has to coordinate the group s actions, i.e. define subtasks, and assign ....

B. Chandrasekaran. Generic Tasks in KnowledgeBased Reasoning: High-Level Building Blocks for Expert System Design. In B. Buchanan and D. Wilkins (Ed.), Readings in Knowledge Acquisition and Learning, pp. 170--177. Morgan Kaufmann Publishers, 1993.

....in medical problem solving, a re use of such strategies in physics problem solving seems promising. As part of the methodology for protocol analysis, we have found a feasible re use of certain coding categories from one domain to another. Re usability can be achieved by using generic models e.g. [20], even though problems with selecting and using generic models cannot be underestimated e.g. 1] This is an area for further examination. Generic models have been recognised as an important aid in the process of modelling problem solving behaviour. The CommonKADS methodology for instance ....

....systems [14] in varous areas such as medical diagnosis and diagnosis in engineering. Our approach rather relies on an empirical study of diagnosis in different domains. Generic tasks useful for diagnostic problem solving (such as hierarchical classification, hypothesis matching) has been proposed [20]. This paper did not aim to provide a generic model from the three cognitive modelling activities reported here but rather to examine aspects of comparison and reusability. We need further evidence and support to carry out such a task. We have become aware that to consider satisfactorily the issue ....

B. Chandrasekaran. Generic tasks in knowledge-based reasoning: High-level building blocks for expert design. IEEE Expert, vol.1, no.3, p23-32, 1986.

.... of systems which embody vast amounts of common sense knowledge [31] others propose that developing sharable and reusable libraries of problem solving components is the way forward [5, 57] a third group suggest that developing systems which tackle whole classes of problems is the way to progress [14, 55, 71]; while a final school of thought argues that smaller, more manageable components which can communicate and cooperate should be used [6, 29, 34] The work described here concentrates on the cooperating systems paradigm, recounting the insights gained from building a number of real world industrial ....

....brought to bear during problem solving and clearly distinguishing it from representational issues is hardly a new idea. Second generation expert systems, for example, have explicit models of their inference structure [15] their problem solving method [56] and the generic task they are tackling [14]. This analogy is particularly pertinent because, at the time of writing, DAI systems are suffering from many of the problems which beset first generation expert systems, including: difficulties operating in dynamic environments, weak explanation of (social) problem solving activity, poor software ....

B. Chandrasekaran, Generic Tasks in Knowledge Based Reasoning: High Level Building Blocks for Expert System Design, IEEE Expert 1 (3) (1983) 23-30.

....to build complete systems that are mostly considered final before being put into routine use. These approaches are based on Newell s (1982) Knowledge Level which advocates the modeling of knowledge at a level above its symbolic representation and includes modeling of problem solving methods (Chandrasekaran 1986, McDermott 1988, Puerta et al. 1992, Schreiber, Weilinga and Breuker 1993 and Steels 1993) and ontologies (Guha and Lenat 1990, Patil et al. 1992 and Pirlein and Studer 1994) The need for complex modeling as a prerequisite to knowledge acquisition (KA) has resulted in the development of ....

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

....being studied by researchers. It will focus more on how knowledge is used as opposed to how knowledge is implemented. These systems will reduce the duplication of effort by defining common frameworks from which expert systems are derived. These frameworks are based on ideas such as generic tasks [Chandrasekaran, 1986] and problemsolving methods [McDermott, 1988] which have spawned several development environments for expert systems [Puerta, Tu, and Musen, 1992; Steels, 1990; Marques et al. 1992] PROTG II is one of such environments that uses a unit of knowledge, called a mechanism, to build problemsolving ....

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

....problem solving methods different from the ones from which they were extracted. Other groups are carrying out parallel research to find ways to define the roles of domain dependent and domain independent knowledge at the knowledge level. Examples are Chandrasekaran s research on generic tasks [Chandrasekaran, 1986], Steels work on the componential framework [Steels, 1990] and McDermott s study of role limiting methods [McDermott, 1988] 1.1. A New PROTG The use in our group of mechanisms for the generation of knowledge acquisition tools has dictated the need for reimplementation of PROTG. To address ....

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

....Learning 1 Introduction In the field of Knowledge Acquisition many approaches deal with a separation of several levels on which to represent knowledge (i. e KADS [WSB92] Components of Expertise [Ste90] Role limiting methods [McD88] Generalized Directive Models [vH95] and Generic Tasks [Cha86, Cha88] Most approaches make a distinction between knowledge at the domain layer (domain specific knowledge) and problem solving knowledge represented at the generic level. One argument that justifies this approach is that systems described on several levels separate different types of ....

B. Chandrasekaran. Generic tasks in knowledge-based reasoning: High level building blocks for expert system design. IEEE Expert, 1, 1986. 7 One can think of situations where statistics can do less due to data-characteristics, or situations where more knowledge is in the expert as in their databases(!)

....for the parametric design task. These tools of course required research into such task specific models. An early example of this was the work by Clancey on hierarchical classification [3] Much of the Knowledge Engineering research in the 80 s was dedicated to identifying such generic tasks [2]. Integrated Environments The task specific architectures from the 80 s were mostly aimed at instantiating models (phase 2) and provided little support for the other phases mentioned above. More integrated Knowledge Engineering environments have been constructed which provided integrated support ....

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

....us to develo a set of data and control models that knowledge engineers can use to configure and assemble methods, and to explore techniques for generating domainspecific knowledge acquisition systems from such configured methods. Faced with brittleness problems in the Generic Task architecture (Chandrasekaran, 1986), investigators at Ohio State University are developing to an approach in which generic tasks are implemented in SOAR (Johnson et al. 1990) Each generic task is represented within a SOAR problem space (Laird et al. 1986) As such, the inputs to the generic task are modeled as the initial ....

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

....Corporation, and by scholarships from the Swedish Institute, from the Fulbright Commission, and from Stanford University. The development of dots has been supported by the Swedish National Board for Industrial and Technical Development (nutek) method components that accomplish subtasks [ Chandrasekaran, 1986; Steels, 1990 ] In McDermott s [ 1988 ] approach, developers use method specific knowledgeacquisition tools to acquire the domain knowledge required by the methods. Method oriented knowledgeacquisition tools, however, are not domain oriented per se; they must be adapted to specific domains and ....

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

....the knowledge level and the knowledge representation (symbolic level) is essential for reuse as it allows each aspect to be dealt with individually. Van de Velde (1993) includes the following research as approaches which have been built on Newell s knowledge level model: Generic Task Framework (Chandrasekaran 1986) . KADS and CommonKADS (Breuker 1994, Weilinga et al. 1993) Role Limiting Methods (McDermott 1988) Components of Expertise and the Componential Methodology (Steels 1993) Protege and Protege II (Puerta et al. 1992) KIF and Ontolingua (Patil et al. 1992) While each of the approaches above ....

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

....a common conceptual framework. Knowledge engineers start the process of meta knowledge acquisition by selecting appropriate problem solving, knowledge acquisition, and domain ontology modules from the metatool s library of reusable components. Problem solving modules are similar to generic tasks (Chandrasekaran, 1986), problem solving methods (Wielinga et al. 1991) and rolelimiting methods (Klinker et al. 1991; Musen and Tu, 1991) we can be think of them as generic reasoning modules, analogous to the forward or backward chainers of rule based systems. Similarly, knowledge acquisition modules correspond to ....

....and models to guide the integration of these modules. Most of the approaches reported in literature have dealt with this integration issue by defining global data types formed by the union of the data type requirements of the individual modules (Klinker et al. 1991; Wielinga et al. 1991; Chandrasekaran, 1986). These global data types form a framework for integrating problem solving modules and (implicitly) their associated strong data models. Consequently, an individual module s data model has meaning only relative to the global data. Although this approach has been demonstrated to work, we believe ....

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

....which the engineer proceeds to design an application and then program a computer system. Design issues are left to a separate model, the design model, which is distinct from the model of expertise where psms are specified. Other research groups such as (Klinker et al. 1991; Swartout Gil, 1995; Chandrasekaran, 1986) have proposed libraries that use a different approach. Whenever a model has been specified, a number of program modules or routines are provided (or must be programmed) that can immediately operationalize the model into a working system. For example, in the expect architecture (Swartout Gil, ....

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

.... shift is sometimes also considered as the transfer from first generation expert systems to second generation expert systems [43] Based on this discussion Section 2 will be concluded by describing two prominent developments in the late eighties: Role limiting Methods [99] and Generic Tasks [36]. In Section 3 we will present some modeling frameworks which have been developed in recent years: CommonKADS [129] MIKE [6] and PROT G II [123] Section 4 gives a short overview of specification languages for KBSs. Problem solving methods have been a major research topic in KE for the last ....

....developer since he has to have the knowledge and the ability to configure the system in the right way. Generic Task and Task Structures In the early eighties the analysis and construction of various KBSs for diagnostic and design tasks evolved gradually into the notion of a Generic Task (GT) [36]. GTs like Hierarchical Classification or State Abstraction are building blocks which can be reused for the construction of different KBSs. The basic idea of GTs may be characterized as follows (see [36] A GT is associated with a generic description of its input and output. A GT comes with a ....

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

....steps gives us inference structures [Clancey 1985] and problem solving methods [McDermott 1988] 3. A specification of the tasks performed during problem solving. Whereas it is quite clear that the tasks differ from application to application, researchers try to find abstract task descriptions [Chandrasekaran 1986, 1987, 1988] These three achievements are not to be seen as separating items, but rather as methods for knowledge based systems operating on different levels. The integration of these levels can be seen in the KADS system [Hayward et al. 1987, Schreiber et al. 1988] KADS is a system ....

....if the way an expert solves a problem matches one of the abstract problem solving methods. The templates guide the knowledge acquisition process by telling what to look for. ffl Comparable to the idea of abstract inference methods is the idea of abstract task structures: The generic task approach [Chandrasekaran 1986, 1987, 1988] has its focus on tasks like diagnosis, classification, or design. They are generic in the sense, that they will be instantiated to real tasks when confronted with a specific application. A generic task defines its function the kind of problem it solves , the knowledge structure ....

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

....abstractions and on the issue of dynamic, interactive, navigation, using a domain . 0 400 200 100 50 . D D 1000 2000 D ( D D D 100K 150K ( D D 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 ....

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

....that is selected for decomposition is then a prediction task. Several ways of generating a task decomposition are possible now. In our case we want to design a solution by using a repository of pre defined solution parts. The idea of such parts is described in (Breuker and van de Velde, 1994) (Chandrasekaran, 1986), etc. and will not be repeated here. The decomposition that was chosen in our example provides us with a refinement of the initial task, namely a method for performing prediction tasks, where a model for decision making is required. Since we do not have such a model we have to generate one. Our ....

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

....other: many scientific tasks have degrees of genericity, and these should be sought out by computer science, as well as by any other interested disciplines. 7.2. Generic tasks in expert systems Chandrasekaran has previously developed the concept of generic tasks in knowledge based expert systems [5], and J. McDermott has expressed similar views [24] Chandrasekaran views generic tasks as elementary building blocks of more complex, but still rather generic, tasks such as heuristic classification. His examples of elementary generic tasks include hierarchical classification, hypothesis matching ....

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

....environments or changes in tasks [Steels 94] The formal description is inspired by the notion of the Knowledge Level [Newell 82] and called formal Knowledge Level model. It describes the components, internal structure and functioning of an agent from a knowledge content perspective [Clancey 83] Chandrasekaran 86] Steels and Mc Dermott 92] Cuena 93] van de Velde 93] using mathematical constructs. Formal KL models have both a sufficient level of preciseness to be formally analyzed and manipulated by software agents, and an appropriate level of abstraction to allow other software agents to understand ....

Chandrasekaran, B. (1986). Generic tasks in knowledge-based reasoning: High level building blocks for expert system design. IEEE Expert, 1(3):2330.

....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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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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B. Chandrasekaran, "Generic tasks for 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: highlevel 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, Vol. 1, pp. 23--30.

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Chandrasekaran, B. (1986). Generic Tasks in knowledge-based reasoning: High-level building blocks for expert system design. IEEE Expert, Vol. 1, No. 3, 23-30.

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