The Protg project has come a long way since Mark Musen first built the Protg metatool for knowledge-based systems in 1987. The original tool was a small application, aimed at building knowledge-acquisition tools for a few specialized programs in medical planning. From this initial tool, the Protg system has evolved into a durable, extensible platform for knowledge-based systems development and research. The current version, Protg-2000, can be run on a variety of platforms, supports customized user-interface extensions, incorporates the Open Knowledge Base Connectivity (OKBC) knowledge model, interacts with standard storage formats such as relational databases, XML, and RDF, and has been used by hundreds of individuals and research groups. In this paper, we follow the evolution of the Protg project through 3 distinct re-implementations. We describe our overall methodology, our design decisions, and the lessons we have learned over the duration of the project.. We believe that our success is one of infrastructure: Protg is a flexible, well-supported, and robust development environment. Using Protg, developers and domain experts can easily build effective knowledge-based systems, and researchers can explore ideas in a variety of knowledge-based domains.

In this paper we describe IRS-II (Internet Reasoning Service) a framework and implemented infrastructure, whose main goal is to support the publication, location, composition and execution of heterogeneous web services, augmented with semantic descriptions of their functionalities. IRS-II has three main classes of features which distinguish it from other work on semantic web services. Firstly, it supports one-click publishing of standalone software: IRS-II automatically creates the appropriate wrappers, given pointers to the standalone code. Secondly, it explicitly distinguishes between tasks (what to do) and methods (how to achieve tasks) and as a result supports capabilitydriven service invocation; flexible mappings between services and problem specifications; and dynamic, knowledge-based service selection. Finally, IRS-II services are web service compatible -- standard web services can be trivially published through the IRS-II and any IRS-II service automatically appears as a standard web service to other web service infrastructures. In the paper we illustrate the main functionalities of IRS-II through a scenario involving a distributed application in the healthcare domain.

This paper presents an approach to learning from situated, interactive tutorial instruction within an ongoing agent. Tutorial instruction is a flexible (and thus powerful) paradigm for teaching tasks because it allows an instructor to communicate whatever types of knowledge an agent might need in whatever situations might arise. To support this flexibility, however, the agent must be able to learn multiple kinds of knowledge from a broad range of instructional interactions. Our approach, called situated explanation, achieves such learning through a combination of analytic and inductive techniques. It combines a form of explanation-based learning that is situated for each instruction with a full suite of contextually guided responses to incomplete explanations. The approach is implemented in an agent called Instructo-Soar that learns hierarchies of new tasks and other domain knowledge from interactive natural language instructions. Instructo-Soar meets three key requirements of flexible...

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

Effective information sharing and decision coordination are vital to collaborative product development and integrated manufacturing. However, typical special-purpose CAE systems tend to isolate information at tool boundaries, and typical integrated CAE systems tend to limit flexibility and process innovation. The SHADE (SHAred Dependency Engineering) project strikes a balance between these undesirable extremes by supporting reconfigurable exchange of engineering knowledge among special-purpose CAE systems. SHADE's approach has three main components: a shared knowledge representation (language and domain-specific vocabulary), protocols supporting information exchange for change notification and subscription, and facilitation services for content-directed routing and intelligent matching of information consumers and producers. 1 Introduction At the heart of effective concurrent engineering is communication. In product development, something is always changing-perhaps a design requireme...

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

Developing tools that allow non-programmers to enter knowledge has been an ongoing challenge for AI. In recent years researchers have investigated a variety of promising approaches to knowledge acquisition (KA), but they have often been driven by the needs of knowledge engineers rather than by end users. This paper reports on a series of experiments that we conducted in order to understandhow far a particular KA tool that we are developing is from meeting the needs of end users, and to collect valuable feedback to motivate our future research. This KA tool, called EMeD, exploits Interdependency Models that relate individual components of the knowledge base in order to guide users in specifying problem-solving knowledge. We describe how our experiments helped us addressseveral questions and hypotheses regarding the acquisition of problem-solving knowledge from end users and the benefits of Interdependency Models, and discuss what we learned in terms of improving not only...

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 ...

In recent years two main technologies for knowledge sharing and reuse have emerged: ontologies and problem solving methods (PSMs). Ontologies specify reusable conceptualizations which can be shared by multiple reasoning components communicating during a problem solving process. PSMs describe in a domain-independent way the generic reasoning steps and knowledge types needed to perform a task. Typically PSMs are specified in a task-specific fashion, using modelling frameworks which describe their control and inference structures as well as their knowledge requirements and competence. In this paper we discuss a novel approach to PSM specification, which is based on the use of formal ontologies. In particular our specifications abstract from control, data flow and other dynamic aspects of PSMs to focus on the logical theory associated with a PSM (method ontology). This approach concentrates on the competence and knowledge requirements of a PSM, rather than internal control de...