Knowledge Acquisition最新文献

We describe three general temporal-abstraction mechanisms needed for managing time-stamped data: point temporal abstraction (a mechanism for abstracting several parameter values into one class); temporal inference (a mechanism for inferring sound logical conclusions over a single interval or two meeting intervals); and temporal interpolation (a mechanism for bridging non-meeting temporal intervals). Making explicit the knowledge required for temporal abstractions supports the acquisition of problem-solving knowledge needed for planning, plan execution, problem identification and plan revision. These mechanisms are implemented in the RÉSUMÉ system, and will be used in the context of our ongoing PROTÉGÉ-II project, whose goal is to generate knowledge-based systems automatically, as well as the appropriate knowledge-acquisition tools, custom-tailored to acquire the specific domain and task knowledge needed by the specific problem-solving method chosen for the task.

The use of predefined models of problem-solving methods is receiving considerable attention from researchers in the area of knowledge acquisition. Using these models, developers of knowledge-acquisition tools are able to prescribe the roles in which knowledge is used in completing a given task. A number of method-oriented architectures based on a single problem-solving method have been developed by various research groups. Because the methods are domain-independent, methodoriented architectures are limited by the fact that knowledge roles that depend on domain-specific considerations cannot be represented using the model of problem solving. In addition, the interface between the knowledge-acquisition tool and the application expert cannot convey adequately the role of each knowledge type in the task model. PROTÉGÉ-II is a knowledge-acquisition shell that we are building to generate knowledge-acquisition tools automatically without presupposing a specific model of problem solving. The shell manages a library of mechanisms—procedures of grain size smaller than that of problem-solving methods. Mechanisms can be combined in PROTÉGÉ-II to construct problem-solving methods and to define the roles of knowledge that depend on domain considerations. Furthermore, PROTÉGÉ-II utilizes the concept of adaptation in interfaces to allow the knowledge engineer to produce interfaces that are task- and domain-specific. In this paper, we present the PROTÉGÉ-II shell and examine the components of its architecture. We also demonstrate the use of PROTÉGÉ-II with a running example and discuss the design techniques used to overcome the limitations of method-specific architectures.

PROTÉGÉ is a meta-level program that generates knowledge-acquisition tools that are based on the method of skeletal-plan refinement. In this paper, we propose a flexible and extensible architecture that allows the problem-solving method to be assembled from more basic methods. In this architecture, we emphasize (1) a uniform view of problem solving at different levels of granularity, (2) an explicit data model that allows construction of complex datatypes from predefined datatypes and (3) the inclusion of domain-dependent control information within a domain-independent problem-solving method. We show how such a model of problem solving can drive the generation of knowledge-acquisition tools.

The domain of knowledge engineering reflects a strong emphasis on the acquisition and modelling of knowledge structures and the processes used to manipulate and transform them. Knowledge engineering activity has been largely focused on the epistemological and representational aspects of knowledge. A different, yet complementary view would identify knowledge engineering as the interaction of cogniting agents with the emphasis on the cognitive processes used in knowledge engineering. This paper presents the results of an intensive discussion between the author and Marc Linster who acted as the interviewee. The topic of discussion focused on his activity with Mark Musen during the modelling stages of knowledge engineering activity in the development of K-ONCOCIN which is a KADS conceptual model of a part of the ONCOCIN system. For each state of this ‘bottom-up’ modelling, the tasks were identified, the available knowledge and information resources identified, the classes of cognitive processes inferred and the outcome structures described.