The paper argues that user models are an essential component of any system which attempts to be “user friendly”, and that expert systems should tailor explanations to their users, be they super-experts or novices. I...
详细信息
The paper argues that user models are an essential component of any system which attempts to be “user friendly”, and that expert systems should tailor explanations to their users, be they super-experts or novices. In particular, this paper discusses a data-driven user modelling front-end subsystem, UMFE, which assumes that the user has asked a question of the main system (e.g. an expert system, intelligent tutoring system etc.), and that the system provides a response which is passed to UMFE. UMFE determines the user's level of sophistication by asking as few questions as possible, and then presents a response in terms of concepts which UMFE believes the user understands. Investigator-defined inference rules are then used to suggest additional concepts the user may/may not know, given the concepts the user indicated he or she knew in earlier questioning. Several techniques are discussed for detecting and removing inconsistencies in the user model. Additionally, UMFE modifies its inference rules for individual users when it detects certain types of inconsistencies. UMFE is a portable domain-independent implementation of a system which infers overlay models for users. UMFE has been used in conjunction with NEOMYCIN; and the paper contains several protocols which demonstrate its principal features. The paper concludes with a critique of UMFE and suggestions for enhancing the current system.
The problem of automatic explanation of reasoning, especially as it relates to expert systems is examined. Explanation means the ability of a program to discuss what it is doing in some understandable way. A general f...
详细信息
The problem of automatic explanation of reasoning, especially as it relates to expert systems is examined. Explanation means the ability of a program to discuss what it is doing in some understandable way. A general framework in which to view explanation and review some of the research done in this area is presented. The explanation system for NEOMYCIN, a medical consultation program is studied. A consultation program interactively helps a user to solve a problem. Our goal is to have NEOMYCIN explain its problem-solving strategies. An explanation of strategy describes the plan the program is using to reach a solution. Such an explanation is usually concrete, referring to aspects of the current problem situation. Abstract explanations articulate a general principle, which can be applied in different situations;such explanations are useful in teaching and in explaining by analogy. The aspects of NEOMYCIN that make abstract strategic explanations possible, the representation of strategic knowledge explicitly and separately from domain knowledge and demonstrate how this representation can be used to generate explanations, is described.
作者：
Clancey, William J.Stanford Univ
Heuristic Programming Project Stanford CA USA Stanford Univ Heuristic Programming Project Stanford CA USA
Expert systems are generally described by a mixture of terms that confuse implementation language with knowledge structure and the search process. This confusion makes it difficult to analyze new problems and to deriv...
详细信息
ISBN:
(纸本)089791144X
Expert systems are generally described by a mixture of terms that confuse implementation language with knowledge structure and the search process. This confusion makes it difficult to analyze new problems and to derive a set of knowledge engineering principles. A rigorous, logical description of expert systems reveals that a small set of terms and relations can be used to describe many rule-based expert systems. In particular, one common method for solving problems is by classification - heuristically relating data abstractions to a preenumerated network of solutions. This model can be used as a framework for knowledge acquisition, particularly in the early stages for organizing the expert's vocabulary and decomposing problems.
In the past fifteen years, artificial intelligence scientists have built several signal interpretation, or understanding, programs. These programs have combined "low" level signal processing algorithms with ...
详细信息
In the past fifteen years, artificial intelligence scientists have built several signal interpretation, or understanding, programs. These programs have combined "low" level signal processing algorithms with knowledge representation and reasoning techniques used in knowledge-based, or expert, systems. [4] They have shown how the use of task domain knowledge combined with symbolic manipulation techniques can be of use in making signal understanding systems more effective and efficient. HASP/SIAP is one such program that tries to interpret the meaning of passively collected sonar data. In this paper we explore some of the AI techniques that contribute in the "understanding" process. We also describe the organization of HASP/SIAP system as an example of a programming framework that show promise for applications in a class of similar problems.
1. Introduction This is a remarkably exciting time to be involved professionally in the field of medical informatics. The underlying scientific principles are beginning to be identified and defined, educators are incr...
作者：
Clancey, William J.Stanford Univ
Heuristic Programming Project Stanford CA USA Stanford Univ Heuristic Programming Project Stanford CA USA
A broad range of heuristic programs - embracing forms of diagnosis, catalog selection, and skeletal planning - accomplish a kind of well-structured problem solving called classification. These programs have a characte...
详细信息
A broad range of heuristic programs - embracing forms of diagnosis, catalog selection, and skeletal planning - accomplish a kind of well-structured problem solving called classification. These programs have a characteristic inference structure that systematically relates data to a pre-enumerated set of solutions by abstraction, heuristic association, and refinement. This level of description specifies the knowledge needed to solve a problem, independent of its representation in a particular computer language. The classification problem-solving model provides a useful framework for recognizing and representing similar problems, for designing representation tools, and for understanding why non-classification problems require different problem-solving methods.
Production rules are a popular representation for encoding heuristic knowledge in programs for scientific and medical problem solving. However, experience with one of these programs, mycin, indicates that the represen...
Production rules are a popular representation for encoding heuristic knowledge in programs for scientific and medical problem solving. However, experience with one of these programs, mycin, indicates that the representation has serious limitations: people other than the original rule authors find it difficult to modify the rule set, and the rules are unsuitable for use in other settings, such as for application to teaching. These problems are rooted in fundamental limitations in mycin"s original rule representation: the view that expert knowledge can be encoded as a uniform, weakly structured set of if/then associations is found to be wanting. To illustrate these problems, this paper examines mycin"s rules from the perspective of a teacher trying to justify them and to convey a problem-solving approach. We discover that individual rules play different roles, have different kinds of justifications, and are constructed using different rationales for the ordering and choice of premise clauses. This design knowledge, consisting of structural and strategic concepts which lie outside the representation, is shown to be procedurally embedded in the rules. Moreover, because the data/hypothesis associations are themselves a proceduralized form of underlying disease models, they can only be supported by appealing to this deeper level of knowledge. Making explicit this structural, strategic and support knowledge enhances the ability to understand and modify the system.
GLISP is a high-level language that is based on Lisp and is compiled into Lisp. It provides a versatile abstract-data-type facility with hierarchical inheritance of properties and object-centered programming. The obje...
暂无评论