Showing papers on "Domain knowledge published in 1981"

Dynamic Strategy Selection in Flexible Parsing

Abstract: Robust natural language interpretation requires strong semantic domain models, "fail-soft" recovery heuristics, and very flexible control structures. Although single-strategy parsers have met with a measure of success, a multi-strategy approach is shown to provide a much higher degree of flexibility, redundancy, and ability to bring task-specific domain knowledge (in addition to general linguistic knowledge) to bear on both grammatical and ungrammatical input. A parsing algorithm is presented that integrates several different parsing strategies, with case-frame instantiation dominating. Each of these parsing strategies exploits different types of knowledge; and their combination provides a strong framework in which to process conjunctions, fragmentary input, and ungrammatical structures, as well as less exotic, grammatically correct input. Several specific heuristics for handling ungrammatical input are presented within this multi-strategy framework.

The interaction with incomplete knowledge bases: a formal treatment

Abstract: Some formal representation Issues underlying the Interaction between an expert system and Its knowledge base are discussed It is argued that a language that can refer both to the application domain and to the state of the knowledge base is required to specify and to question an incomplete knowledge base A formal logical language with this ability is presented and its semantics and proof theory are defined It is then shown how this language must be used to interact with the knowledge base

Domain Knowledge and the Design Process

Abstract: During the past 10 or 12 years, Artificial Intelligence researchers have explored techniques for bringing large amounts of domain knowledge to bear in solving ill-structured problems. Several programs that make use of these knowledge-based techniques are currently being developed to assist in various design tasks. This paper introduces one technique -- rule-based programming - and illustrates its use with two programs, R1 and XSEL, which are used by Digital Equipment Corporation in the design of computer system configurations.

Acquisition of procedural knowledge from domain experts

Abstract: Procedural knowledge forms an important part of expertise. This paper describes a system which allows domain experts to themselves enter procedural Knowledge into a knowledge base. The system, a stylized form of scientific English embodied within the Unit System for knowledge acquisition and representation, has been used successfully within the domain of molecular biology.

Reasoning with incomplete knowledge in a resource-limited environment: integrating reasoning and knowledge acquisition

Abstract: This paper describes an approach to reasoning with incomplete information in a resource-limited environment. Approaches to date either assume infinite resources and proceed to enumerate a large inference space, or assume few resources and ignore the missing information. They do not reason about resource constraints and the inference methods admissable under them. A hearsay-Il-like system is described where each knowledge source is a separate production system. During rule evaluation, a rule antecedant is evaluated using minimal - resource methods. A rule antecedant is evaluated to true, false, or an expected resource cost to acquire the information necessary to complete its evaluation. If conflict resolution chooses a partially evaluated rule, it posts a goal asking other knowledge sources to provide the missing information, suspends the knowledge source, and informs the knowledge source's manager about the suspension and accompanying goal. The manager decides whether the goal is worth pursuing now, the amount of resources to apply to the task, what knowledge source to apply, and when to give up. The knowledge sources that attempt the goal can implement a variety of inferential and knowledge acquisition techniques.

Multi-strategy parsing and its role in robust man-machine communication

Abstract: Robust natural language interpretation requires strong semantic domain models, "fai lsoft" recovery heuristics, flexible control structures, and focused user interaction when automatic correct ion proves infeasible. Al though single-strategy parsers have met with some success, a multi-strategy approach, with strategies selected dynamically according to the type of construct ion being parsed at any given time, is shown to provide a higher degree of flexibility, redundancy, and ability to bring task-specific domain knowledge (in addition to general linguistic knowledge) to bear on both grammatical and ungrammatical input. This construction-specif ic, multi-strategy approach can also help provide tightly focused interaction with the user in cases of semantic or structural ambiguity by allowing such ambiguities to be represented without dupl icat ion of unambiguous material. The approach also aids in task-specific language development by allowing direct interpretation of languages defined in terms natural to the task domain. A parsing algorithm integrating case-frame instantiation and partial pattern matching strategies is presented. The algorithm can deal with conjunct ions, fragmentary input, and ungrammatical structures, as well as less exotic, grammatically correct input. This research was sponsored in part by the Defense Advanced Research Projects Agency (DOD), ARPA Order No. 3597, monitored by the Air Force Avionics Laboratory under contract F33615-78-C-1551, and in part by the Air Force Office of Scientific Research under Contract F49620-79-C-0143. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of DARPA, the Air Force Office of Scientific Research or the US government. University Libraries Carnegie Mellon University Pittsburgh PA 15213-389©

Natural language interaction with dynamic knowledge bases: monitoring as response

Abstract: In this communication, we discuss an interesting aspect of natural language interaction with dynamically changing knowledge bases - the ability to monitor for relevant future changes in that knowledge. We also indicate the status of our current work in this area and the overall goals of our research on question-answering and monitoring dynamic knowledge bases.

Language Production: the Source of the Dictionary

Abstract: Ultimately in any natural language production system the largest amount of human effort will go into the construction of the dictionary: the data base that associates objects and relations in the program's domain with the words and phrases that could be used to describe them. This paper describes a technique for basing the dictionary directly on the semantic abstraction network used for the domain knowledge itself, taking advantage of the inheritance and specialization machanisms of a network formalism such as KL-ONE. The technique creates considerable economics of scale, and makes possible the automatic description of individual objects according to their position in the semantic net. Furthermore, because the process of deciding what properties to use in an object's description is now given over to a common procedure, we can write general-purpose rules to, for example, avoid redundancy or grammatically awkward constructions.

Data Base Support For Automated Photo Interpretation

Abstract: This paper is concerned with the use of a database to support automated photo interpretation. The function of the database is to provide an environment in which to perform photo interpretation utilizing software tools, and represent domain knowledge about the scenes being interpreted. Within the framework of the database, image interpretation systems use knowledge stored as map, terrain, or scene descriptions to provide structural or spatial constraints to guide human and machine processing. We describe one such system under development, MAPS (Map Assisted Photo interpretation System), and give some general rationales for its design and implementation.© (1981) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Elements of Naval Domain Knowledge. A First Step in the Manual Knowledge Acquisition Process Required by Large Artificial Intelligence Systems.

Abstract: : The objective of this study was to collect in one location a large amount of information in and about the Navy domain. While directed at the particular problem of mission planning, this report generalizes to serve as a first step in the manual knowledge acquisition process for new workers in the domain. It contains examples that can be used in examining various existing and proposed knowledge representation systems. This report is intended as a living document, subject to update and correction.