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

: To create more powerful knowledge acquisition systems, we not only need better acquisition tools, but we need to change the architecture of the knowledge based systems we create so that their structure will provide better support for acquisition. Current acquisition tools permit users to modify factual knowledge but they provide limited support for modifying problem solving knowledge. In this paper, we argue that this limitation (and others) stem from the use of incomplete models of problem solving knowledge and inflexible specification of the interdependencies between problem solving and factual knowledge. We describe the EXPECT architecture which addresses these problems by providing an explicit representation for problem solving knowledge and intent. Using this more explicit representation, EXPECT can automatically derive the interdependencies between problem solving and factual knowledge. By deriving these interdependencies from the structure of the knowledge-based system itself ...

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 ...

A system that acquires knowledge from a user should be able to reflect upon the knowledge that it has--at each moment--and understand what kinds of new knowledge it needs to learn. For the past two decades, research in the area of knowledge acquisition has been moving towards systems that have access to richer representations of knowledge about their task. This paper reviews some well-known knowledge acquisition tools representative of this trend. It also describes our recent work in EXPECT, a system with explicit representations of knowledge about the task and the domain that supports knowledge acquisition for a wider range of tasks and applications than its predecessors. We hope our observations will be useful to researchers in user interfaces and in machine learning concerned with acquiring information from users.

EXPECT provides an environment for developing knowledge-based systems that allows end-users to add new knowledge without needing to understand the details of system organization and implementation. The key to EXPECT's approach is that it understands the structure of the knowledge-based system being built: how it solves problems and what knowledge it needs to support problem-solving. EXPECT uses this information to guide users in maintaining the knowledge -based system. We have used EXPECT to develop a tool for evaluating transportation plans. 1. Introduction To successfully attack the large scale, real world domains targeted by the ARPA/Rome Labs Planning Initiative co llaboration is required. People and machines must work together to solve problems, each contributing what they do best. In addition to planning systems, other computerized tools are needed to support that collaboration---such as tools for evaluating and cr itiquing plans. In this paper we describe the EXPECT...

We describe an editor for problem-solving knowledge that communicates with the user through English paraphrases of the knowledge. Although it does not support the full range of modifications one might want to make, the value of the tool lies in the fact that the user need not understand the syntax of the expert system to make modifications. By analyzing the problemsolving knowledge, the tool can allow the user to select semantically coherent chunks of the knowledge. It then presents English paraphrases of possible substitutions which would result in new problem-solving knowledge that is syntactically correct. In this way the tool expands the range of modifications that a nave user can make to problem-solving knowledge in an expert system. Introduction The ability to change the contents of the knowledge base without knowing the representation language and with just a basic understanding of the domain is one of the ultimate goals of any knowledge acquisition tool. (Simon 86) states the...

Our goal is to build knowledge acquisition tools that support users in making a broad range of changes to a knowledge base, including both factual and problem-solving knowledge. These changes may require several individual modifications to various parts of the knowledge base, that need to be carefully coordinated to prevent users from introducing errors in the knowledge base. Thus, it becomes essential that our KA tools understand the consequences of each kind of change that the user may initiate, detect any harmful side-effects that can be introduced in the system, and guide the user in resolving them. To address this issue, wehave developed a transaction-based approach to knowledge acquisition that can support users in making complex modifications to a knowledge base. A transaction is a sequence of changes that together modify some aspect of the behavior of a knowledge-based system, and that when only partially carried out may leave the knowledge base in an undesirable state. If a user ...

This paper presents a new approach to the process of building a knowledge-based system which relies on a tutoring paradigm rather than traditional knowledge engineering. In this approach, an expert teaches the knowledge based system in much the same way the expert would teach a human student, by providing specific examples of problems and solutions, explanations of these solutions, or supervise the system as it solves new problems. During such interactions, the system extends and corrects its knowledge base until the expert is satisfied with its performance. Three main features characterize this approach. First, it is based on a multistrategy learning method that dynamically integrates the elementary inferences that are employed by the single-strategy learning methods. Second, much of the knowledge needed by the system is generated by the system itself. Therefore, most of the time, the expert will need only to confirm or reject system-generated hypotheses. Third, the knowledge base development process is efficient due to the ability of the multistrategy learner to reuse its reasoning process, as well as the employment of plausible version spaces for controlling the knowledge base development process. This paper illustrates a cooperation between a learning system and a human expert in which the learner performs most of the tasks and the expert helps it in solving the problems that are intrinsically difficult for a learner and relatively easy for an expert. 1.

Configuration systems often use large and complex knowledge bases that need to be maintained and extended over time. The explicit representation of problem-solving knowledge and factual knowledge can greatly enhance the role of a knowledge acquisition tool by deriving from the current knowledge base, the knowledge gaps that must be resolved. This paper details EXPECT’s approach to knowledge acquisition in the configuration domain using the propose-and-revise strategy as an example. EXPECT supports users in a variety of KA tasks like filling knowledge roles, making modifications to the knowledge base including entering new components, classes and even adapting problem-solving strategies for new tasks. EXPECT’s guidance changes as the knowledge base changes, providing a more flexible approach to knowledge acquisition. The paper also examines the possible use of EXPECT as a KA tool in the complex and real world domain of computer configuration.

. Task and Method notions can be used as a paradigm to help the modelling-team to structure rough expert knowledge. In such a context an important difficulty is to keep in mind a synthetic understanding of how the characteristics of all the Tasks and Methods interfere with each other. In this article, we present how the modelling team can be given a synthetic understanding of the model by the use of explanation modules that propose synthetic points of view on the model. We present how we define these explanations using three levels: what is related to the paradigm, what is related to the general characteristics of the model one considers and what is related to the particular model one considers. We also highlight what differentiates explanations and solving traces, present the putting into practice of our approach and compare it with some knowledge acquisition tools. 1 Introduction When constructing a knowledge based system (KBS) the first step is the elaboration of an ab...