In the field of Knowledge Engineering, dissatisfaction with the rapid-prototyping approach has led to a number of more principled methodologies for the construction of knowledgebased systems. Instead of immediately implementing the gathered and interpreted knowledge in a given implementation formalism according to the rapid-prototyping approach, many such methodologies centre around the notion of a conceptual model: an abstract, implementation independent description of the relevant problem solving expertise. A conceptual model should describe the task which is solved by the system and the knowledge which is required by it. Although such conceptual models have often been formulated in an informal way, recent years have seen the advent of formal and operational languages to describe such conceptual models more precisely, and operationally as a means for model evaluation. In this paper, we study a number of such formal and operational languages for specifying conceptual mode...

Problem solving methods (PSMs) are domain-independent reasoning components, which specify patterns of behavior which can be reused across applications. While the availability of extensive PSM libraries and the emerging consensus on PSM specification languages indicate the maturity of the field, a number of important research issues are still open. In particular, very little progress has been achieved on foundational and methodological issues. Existing libraries of PSMs lack a clear theoretical basis and only provide weak support for the method development process, usually in the form of informal guidelines. In this paper we will address these issues by illustrating a framework which characterizes PSMs in terms of problem commitments, problem-solving paradigms and domain assumptions. This framework provides i) a theoretical foundation for situating PSM research and individual PSMs, as well as ii) an organization which allows us to characterize method development and selection as a process of navigating through a three-dimensional space (defined by the three components of our framework). Individual moves through this space are specified by means of adapters. In the paper we will illustrate these ideas in detail, with examples taken from parametric design problem solving. 1.

Officer praised dog for barking at object." Enables Detect Drugs out FK Initiates Retrieval 5 6 Missing Figure 10. Forgetting to fill the tank with gas A=actual intention; E=expectation; Q=question; C=context; I=index; G=goal Tank Out of Gas Tank Full Tank Low Fill Tank Should have filled up with gas when tank low Expectation What Action to Do? KEY: G = goal; I = index; C = context; Q = question; E = expectation; A = actual intention Results At Store connections with related concepts. Other learning goals take multiple arguments. For instance, a knowledge differentiation goal (Cox & Ram, 1995) is a goal to determine a change in a body of knowledge such that two items are separated conceptually. In contrast, a knowledge reconciliation goal (Cox & Ram, 1995) is one that seeks to merge two items that were mistakenly considered separate entities. Both expansion goals and reconciliation goals may include or spawn a knowledge organization goal (Ram, 1993) that seeks to reorganize the existing knowledge so that it is made available to the reasoner at the appropriate time, as well as modify the structure or content of a concept itself. Such reorganization of knowledge affects the conditions under which a particular piece of knowledge is retrieved or the kinds of indexes associated with an item in memory.

Problem-solving methods provide reusable architectures and components for implementing the reasoning part of knowledge-based systems.

PROTG-II is a suite of tools and a methodology for building knowledge-based systems and domain-specific knowledge-acquisition tools. In this paper, we show how PROTG-II can be applied to the task of providing protocol-based decision support in the domain of treating HIVinfected patients. For this task, we use a problem-solving method called episodic skeletal-plan refinement. This method is decomposable; we construct it from a set of reusable components. In addition, we build an application ontology that consists of the terms and relations in the domain, plus terms that supply method-specific knowledge requirements. From this ontology, we automatically generate a domain-specific knowledge-acquisition tool. The general goal of the PROTG-II approach is to produce systems and components that are easily maintained and reusable. This is the rationale for constructing a problem-solving method from a set of smaller-grained methods and mechanisms. This is also why our knowledge-acquisition tools are domain-specific and generated automatically from ontologies. Although our evaluation is still preliminary, for the application task of providing protocol-based decision support, we show that these goals of reusability and easy maintenance can be achieved. We discuss design decisions and the tradeoffs that have to be made in the development of the system. Keywords. Decision support; expert systems; knowledge acquisition.

Role-limiting approaches support knowledge acquisition (KA) by centering knowledge base construction on common types of tasks or domain-independent problem-solving strategies. Within a particular problem-solving strategy, domain-dependent knowledge plays speci c roles. A KA tool then helps a user to ll these roles. Although role-limiting approaches are useful for guiding KA, they are limited because they only support users in lling knowledge roles that have been built in by the designers of the KA system. EXPECT takes a di erent approach toKAby representing problem-solving knowledge explicitly, and deriving from the current knowledge base the knowledge gaps that must be resolved by the user during KA. This paper contrasts role-limiting approaches and EXPECT's approach, using the propose-and-revise strategy as an example. EXPECT not only supports users in lling knowledge roles, but also provides support in 1) adapting the problemsolving strategy, 2) changing the types of information to be acquired about a knowledge role, 3) adding new knowledge roles, and 4) acquiring additional background information about the domain needed by the knowledge-based system. EXPECT's guidance changes as the knowledge base changes, providing a more exible approach toknowledge acquisition. This work provides

A knowledge acquisition tool should provide a user with maximum guidance in extending and debugging a knowledge base, by preventing inconsistencies and knowledge gaps that may arise inadvertently. Most current acquisition tools are not very exible in that they are built for a predetermined inference structure or problem-solving mechanism, and the guidance they provide is specific to that inference structure and hard-coded by their designer. This paper focuses on expect, a reflective architecture that supports knowledge acquisition based on an explicit analysis of the structure of a knowledge-based system, rather than on a fixed set of acquisition guidelines. expect's problem solver is tightly integrated with loom, a state-of-the-art knowledge representation system. Domain facts and goals are represented declaratively, and the problem solver keeps records of their functionality within the task domain. When the user corrects the system's knowledge, expect tracks any possible implications of this change in the overall system and cooperates with the user to correct any potential problems that may arise. The key to the exibility of this knowledge acquisition tool is that it adapts its guidance as the knowledge bases evolve in response to changes

Metalevel tools can support the software development process by automating the design of task- and application-specific tools. Dash is a metalevel tool that allows developers to generate domain-specific knowledge-acquisition tools from domain ontologies. Domain specialists use the knowledge-acquisition tools generated by dash to instantiate the concepts and relationships defined in the domain ontologies. The output of the knowledge-acquisition tools is a collection of instances that constitute the knowledge base for a knowledge-based system.

Modifying knowledge-based systems is a complex activity. One of its di#culties is that several related portions of the system mighthavetobechanged in order to maintain the coherence of the system. However, it is di#cult for users to #gure out what has to be changed and how. This paper presents a novel approach for building knowledge acquisition tools that overcomes some of the limitations of current approaches. In this approach, knowledge of prototypical procedures for modifying knowledge-based systems is used to guide users in changing all related portions of a system. These procedures, whichwe call knowledge acquisition scripts #or KA Scripts#, capture how related portions of a knowledge-based system can be changed coordinately.By using KA scripts, a knowledge acquisition tool would be able to relate individual changes in di#erent parts of a system, enabling the analysis of each individual change from the perspective of the overall modi#cation. The paper also describes the ...

We describe a conceptual architecture and software implementation specific to the task of interpretation, summarization, visualization, explanation, and interactive exploration of time-oriented clinical data and the multiple levels of meaningful concepts that can be abstracted from these data. We build on our work on abstraction of timeoriented clinical data using a knowledge base, acquired from clinical experts, of temporal properties of the data. We call the new framework KNAVE (Knowledge-based Navigation of Abstractions for Visualization and Explanation). The visualization and exploration operators, whose semantics are domain independent, access the domain-specific knowledge base. Exploration exploits key relations (e.g., the abstraction hierarchy) in each clinical domain. Preliminary assessment of the prototype with several clinical users has been encouraging. The KNAVE methodology has broad ramifications for reducing the load that large numbers of time-oriented clinical data put on practicing physicians.