Showing papers on "Domain knowledge published in 1982"

Acquisition of cognitive skill

Abstract: A framework for skill acquisition is proposed that includes two major stages in the development of a cognitive skill: a declarative stage in which facts about the skill domain are interpreted and a procedural stage in which the domain knowledge is directly embodied in procedures for performing the skill. This general framework has been instantiated in the ACT system in which facts are encoded in a propositional network and procedures are encoded as productions. Knowledge compilation is the process by which the skill transits from the declarative stage to the procedural stage. It consists of the subprocesses of composition, which collapses sequences of productions into single productions, and proceduralization, which embeds factual knowledge into productions. Once proceduralized, further learning processes operate on the skill to make the productions more selective in their range of applications. These processes include generalization, discrimination, and strengthening of productions. Comparisons are made to similar concepts from past learning theories. How these learning mechanisms apply to produce the power law speedup in processing time with practice is discussed.

Competence in knowledge representation

Abstract: The range of domains and tasks for “knowledgebased systems” has been expanding at a furious pace. As we move away from trivial domains, such as the “blocks world”, the demands on knowledge representation systems used by expert programs are becoming more extreme. For one thing, the domains themselves are getting so complex that specialized technical vocabularies are unavoidable; consequently, the issue of a system talking with un expert in ki8 own ianguage cannot be ignored. For another, tasks such as medical diagnosis, scene analysis, speech understanding, and game playing all have as a central feature an incrementally evolving model representing probably incomplete knowledge of part of the task domain. In this paper, we explore some of the impact of these two critical issues-complexity and incompletenesson knowledge representation systems. We review some aspects of current representation research that offer a foundation for coping with these problems, and finally suggest a way of integrating these ideas into a powerful, practical knowledge representation paradigm.

A formal treatment of incomplete knowledge bases

Abstract: The formal representation issues underlying the use of incomplete knowledge bases are investigated. It is argued that to effectively deal with incomplete knowledge, a system must first be able to determine where the incompleteness lies. This has two consequences: first, the knowledge base must have knowledge about its own incompleteness and second, it must be possible to ask or tell the knowledge base about its incompleteness. A formal model of knowledge bases with these abilities is presented. The characterization is in terms of a logical language that can refer both to application domains and to what knowledge bases might know about such domains. This language is also used to formulate questions, statements and defaults for a knowledge base. A semantic and proof-theoretic analysis is provided not only for the language itself, but also for the operations of answering questions, acquiring knowledge and assuming defaults.

Reasoning about success and failure in aerial image understanding

Abstract: Why is an Image Understanding task, such as locating houses in aerial photographs, difficult to do automatically by computer? Three sources of difficulties are examined in this thesis, and a single approach is applied to each: that of reasoning about success and failure. The first problem is the frame problem: to choose the right domain model for a particular image. The second problem is the segmentation technique problem: to choose the right segmentation technique. The third problem is the parameter problem: to choose the correct parameter value for a parameter like a threshold. This thesis presents a unifying approach to these three problems: that of reasoning about success and failure. Reasoning about success and failure necessates an explicit goal representation, and evaluation technique for measuring performance, and search and reasoning techniques for trying to improve performance. This approach is tested in a three level Image Reasoning Program, which has been tried in the domain aerial photographs of suburban housing developments. An Appearance Model Expert examines an Appearance Model and decides what to look for using domain knowledge and the structures located so far. The Operator Expert responds to a query from the AME for a region with certain properties, and chooses an appropriate segmentation technique. Finally, Adaptive Operators use knowledge to adapt low-level parameters to find regions in the image that match particular high-level features. Each component reasons about success and failure using implicit knowledge and evaluations of performance derived from explicit goals and explicit image data. This kind of reasoning enables the Image Reasoning Program to locate objects it would not have otherwise found, improving performance. A more general conclusion is that adaptive techniques are required if high-level knowledge is to appropriately interact with computations at all levels of an Image Understanding system.

Towards the Principled Engineering of Knowledge

Abstract: The acquisition of expert knowledge is fundamental to the certain of expert systems. The conventional approach to building expert systems assumes that the knowledge exists, and that it is feasible to find an expert who has the knowledge and can articulate it in collaboration with a knowledge engineer. This article considers the practice of knowledge engineering when these assumptions can not be strictly justified. It draws on our experiences in the design of VLSI design methods, and in the prototyping of an expert assistant for VLSI design. We suggest methods for expanding the practice of knowledge engineering when applied to fields that are fragmented and undergoing rapid evolution. We outline how the expanded practice can shape and accelerate the process of knowledge generation and refinement. Our examples also clarify some of the unarticulated present practice of knowledge engineering.

Knowledge management: a practical amalgam of knowledge and data base technology

Abstract: This paper describes the central features of a system designed for the management of large amounts of application specific knowledge. The Knowledge Manager(KM-I) employs distinct software and hardware processors to implement: A file of general knowledge and an associated reasoning engine A file of specific knowledge and an associated searching engine We present our reasons for believing why this can be an effective strategy for realizing many practical knowledge based/expert system applications that lie in a large overlapping area between practical AI and advanced data management technology. We then outline the major features and components of the system and discuss the range of intended applications.

Augmenting a database knowledge representation for natural language generation

Abstract: The knowledge representation is an important factor in natural language generation since it limits the semantic capabilities of the generation system. This paper identifies several information types in a knowledge representation that can be used to generate meaningful responses to questions about database structure. Creating such a knowledge representation, however, is a long and tedious process. A system is presented which uses the contents of the database to form part of this knowledge representation automatically. It employs three types of world knowledge axioms to ensure that the representation formed is meaningful and contains salient information.

A knowledge engineering approach to natural language understanding

Abstract: This paper describes the results of a preliminary study of a Knowledge Engineering approach to Natural Language Understanding. A computer system is being developed to handle the acquisition, representation, and use of linguistic knowledge. The computer system is rule-based and utilizes a semantic network for knowledge storage and representation. In order to facilitate the interaction between user and system, input of linguistic knowledge and computer responses are in natural language. Knowledge of various types can be entered and utilized: syntactic and semantic; assertions and rules. The inference tracing facility is also being developed as a part of the rule-based system with output in natural language. A detailed example is presented to illustrate the current capabilities and features of the system.

Dumb drawing systems and knowledge engineering

Abstract: The link between drawing systems and knowledge of users' work-practices is discussed and illustrated by examples of experience at EdCAAD, highlighting the significance of knowledge engineering to practical CAAD applications. Drawing systems, design systems, user-interface, knowledge engineering.

Knowledge base mechanisms

Abstract: One of the principal goals of the Fifth Generation Computer System project for the coming decade is to develop a methodology for building knowledge information processing systems which will provide people with intelligent agents. The key notion of the fifth generation computer system is knowledge which is used for problem solving. In this paper we describe our plan of R & D on knowledge base mechanisms. A knowledge representation system is planned to be designed to support knowledge acquisition for the knowledge information processing systems. The system includes a knowledge representation language, a knowledge base editor and a debugger. It is also expected to perform as a kind of meta-inference system. With respect to the large scale knowledge base systems, a knowledge base mechanism based on the relational model is to be studied in the earlier stage of the project. Distributed problem solving is also one of the main issues of this work.

A message-passing control structure for text understanding

Abstract: This paper describes an object-oriented, message-passing system for natural language text understanding. The application domain is the texts of Texas Instruments' patent descriptions. The object-oriented environment permits syntactic analysis modules to communicate with domain knowledge modules to resolve ambiguities as they arise.

Associating current knowledge with that of past experience based on knowledge about automata

Abstract: Important to the performance of interactive systems is the ability of its members to associate current knowledge with knowledge of past experience. Knowledge association discussed here results in greater detail of a current knowledge and is demonstrated through the use of examples. It is based on knowledge about automata and the knowledge structures are in the form of graphs.

Some Fundamental Issues in Knowledge Representation

Abstract: Fundamental issues of “knowledge representation” appear to be rather obscured by a terminological confusion prevailing in and across subfields of computer science involved, namely Artificial Intelligence, Date Bases, and Programming Languages. Accordingly, the different understanding of “knowledge representation” in these areas is investigated resulting in an explanation proposal for the heading term: knowledge reconstruction and its organisation. Some epistemological, formal and computational requirements to be met for achieving this task are given. The epistemological requirements are illustrated by an attempt to model a natural language utterance adequately. Formal and computational issues are addressed by considering some recently proposed “knowledge representation languages”, namely OMEGA [Hewitt et al. 1980]. It is concluded that there are three lines of research that should be pursued or adopted: the philosophically profound investigation of epistemological primitives, the elaboration of model-theoretic semantics, and the investigation of organisational and engineering principles of database and language design.

Knowledge engineering: where we are and where we are going

Abstract: The emergence of expert system technology has given rise to a new discipline called knowledge engineering. The Navy Center for Applied Research in AI has been evaluating the current state of this discipline in an attempt to assess its viability for transfer from the AI laboratory into selected application areas within the Navy. A summary of those observations and conclusions is presented. 21 references.

Knowledge representation method based on predicate calculus in an intelligent CAI system

Abstract: The knowledge representation method is introduced to be applied in the ICAI system to teach programming language. Knowledge about syntax and semantios of that language is represented by a set of axioms written in the predicate calculus language. The directed graph of concepts is mentioned as a method to represent an instructional structure of the domain knowledge. The proof procedure to answer student's questions is described.