Randy M. Kaplan

Knowledge-based acquisition of causal relationships in text

Abstract In this paper, we describe a procedure that integrates several techniques for recognizing causal relationships in expository text. Applying these techniques yields a knowledge representation consisting of classifications of the causal relationships contained in a text. This procedure is very robust. If any one of the techniques for recognizing a causal relationship fails, an alternate methodology can be used to continue the causal analysis. The procedure we will describe is embodied in a program called the causal analyser. We have applied the causal analyser to several texts to produce a representation of the causal relationships in these texts. The causal analyser described in this paper will be part of a knowledge acquisition system called TAKT (tool for the acquisition of knowledge from text), which is currently being developed.

A generalizable architecture for building intelligent tutoring systems

Summary form only given. HYDRIVE (HYDRaulics Interactive Video Experience) is an intelligent tutoring system incorporating assessment as part of the tutoring process. HYDRIVE departs from typical intelligent tutoring systems in that its underlying architecture is domain independent. The domain that is presently supported by HYDRIVE is troubleshooting for the hydraulics supported systems of an Air Force F-15 fighter aircraft. HYDRIVE addresses both system-specific and system-independent skills. Several novel approaches to intelligent tutoring underlie the rationale for HYDRIVE. The reasoning component of the system makes extensive use of a hierarchical knowledge representation. Reasoning within the system is accomplished using a logic-based approach and is linked to a highly interactive interface using multimedia.<<ETX>>

An Automatic Scoring System For Advanced Placement Biology Essays

This paper describes a prototype for automatically scoring College Board Advanced Placement (AP) Biology essays.1. The scoring technique used in this study was based on a previous method used to score sentence-length responses (Burstein, et al, 1996). One hundred training essays were used to build an example-based lexicon and concept grammars. The prototype accesses information from the lexicon and concept grammars to score essays by assigning a classification of Excellent or Poor based on the number of points assigned during scoring. Final computer-based essay scores are based on the systems recognition of conceptual information in the essays. Conceptual analysis in essays is essential to provide a classification based on the essay content. In addition, computer-generated information about essay content can be used to produce diagnostic feedback. The set of essays used in this study had been scored by human raters. The results reported in the paper show 94% agreement on exact or adjacent scores between human rater scores and computer-based scores for 105 test essays. The methods underlying this application could be used in a number of applications involving rapid semantic analysis of textual materials, especially with regard to scientific or other technical text.

A plea for readable pleas for a readable Prolog programming style

Writing a plea for a readable Prolog programming style is not like writing most pleas. In writing a plea for a readable Prolog programming style in particular, one could possibly bring forth many results. This means that special care must be taken when writing a plea for a readable Prolog programming style. Not only must the plea be readable, it must also be written in such a way as to make clear the process of writing well-styled Prolog programs.Plea writing makes use of a set of rules to specify how a certain task might be accomplished. In general, if these rules are written without attention to their meaning, there is no telling how the style of a particular Prolog program will be affected. It is from this aspect of plea writing both the power and danger of plea writing arise.We will attempt to discuss ways in which pleas for a readable Prolog programming style may be written so that they may be clearly understood and applied to make Prolog programs more readable. We will also give an example of the application of plea writing rules by writing a plea and then applying this plea to excerpts of Prolog code.

Practical and generalizable architecture for an intelligent tutoring system

In this paper we describe an intelligent tutoring system (ITS) called HYDRIVE (hydraulics interactive video experience). This system is built using several novel approaches to intelligent tutoring. The underlying rationale for HYDRIVE is based on the results of a cognitive task analysis. The reasoning component of the system makes extensive use of a hierarchical knowledge representation. Reasoning within the system is accomplished using a logic-based approach and is linked to a highly interactive interface using multimedia. The knowledge representation contains information that drives the multimedia elements of the system, and the reasoning components select the appropriate information to assess student knowledge or guide the student at any particular moment. As this system will be deployed throughout the Air Force maintenance function, the implementation platform is the IBM PC.

small-X — a language and interpreter for building expert systems

This paper describes a programming language and its interpreter that have been developed for the construction of expert systems on microcomputers. The language has been developed with attention to simplicity and functionality. Its interpreter, now running on MS-DOS machines executes expert system programs written in small-X and provides a simple environment for expert system development.