Combining factual and heuristic knowledge in knowledge acquisition

A knowledge acquisition technique that combines heuristic and factual knowledge represented as two hierarchies is described. These ideas were applied to the construction of a knowledge acquisition interface to the Expert System Analyst (OPERA). The goal of OPERA is to improve the operations support of the computer network in the space shuttle launch processing system. The knowledge acquisition bottleneck lies in gathering knowledge from human experts and transferring it to OPERA. OPERA's knowledge acquisition problem is approached as a classification problem-solving task, combining this approach with the use of factual knowledge about the domain. The interface was implemented in a Symbolics workstation making heavy use of windows, pull-down menus, and other user-friendly devices

Acceleration of the retrieval of past experiences in Case Based Reasoning : application for preliminary design in Chemical Engineering

The way to manage knowledge accumulated is one of the firm’s trends, in order to capitalize and to transmit this knowledge. Some Artificial Intelligence methods are devoted to preserve and to reuse past experiences. Case Based Reasoning (CBR) is one of these methods dedicated to problem solving, new knowledge acquisition and knowledge management. CBR is a cyclic method where the central notion is a case which represents an earlier experience. Several cases are collected and stored in a memory: the case base. The goal of this paper is to soften the way to describe problem and to increase the effectiveness of the system during the retrieval of relevant case

The integration of automated knowledge acquisition with computer-aided software engineering for space shuttle expert systems

A prediction was made that the terms expert systems and knowledge acquisition would begin to disappear over the next several years. This is not because they are falling into disuse; it is rather that practitioners are realizing that they are valuable adjuncts to software engineering, in terms of problem domains addressed, user acceptance, and in development methodologies. A specific problem was discussed, that of constructing an automated test analysis system for the Space Shuttle Main Engine. In this domain, knowledge acquisition was part of requirements systems analysis, and was performed with the aid of a powerful inductive ESBT in conjunction with a computer aided software engineering (CASE) tool. The original prediction is not a very risky one -- it has already been accomplished

Beyond rules: The next generation of expert systems

The PARAGON Representation, Management, and Manipulation system is introduced. The concepts of knowledge representation, knowledge management, and knowledge manipulation are combined in a comprehensive system for solving real world problems requiring high levels of expertise in a real time environment. In most applications the complexity of the problem and the representation used to describe the domain knowledge tend to obscure the information from which solutions are derived. This inhibits the acquisition of domain knowledge verification/validation, places severe constraints on the ability to extend and maintain a knowledge base while making generic problem solving strategies difficult to develop. A unique hybrid system was developed to overcome these traditional limitations

Knowledge and reasonableness

The notion of relevance plays a role in many accounts of knowledge and knowledge ascription. Although use of the notion is well-motivated, theorists struggle to codify relevance. A reasonable person standard of relevance addresses this codification problem, and provides an objective and flexible standard of relevance; however, treating relevance as reasonableness seems to allow practical factors to determine whether one has knowledge or not—so-called “pragmatic encroachment.” I argue that a fuller understanding of reasonableness and of the role of practical factors in the acquisition of knowledge lets us avoid pragmatic encroachment

Requirements for Information Extraction for Knowledge Management

Knowledge Management (KM) systems inherently suffer from the knowledge acquisition bottleneck - the difficulty of modeling and formalizing knowledge relevant for specific domains. A potential solution to this problem is Information Extraction (IE) technology. However, IE was originally developed for database population and there is a mismatch between what is required to successfully perform KM and what current IE technology provides. In this paper we begin to address this issue by outlining requirements for IE based KM

CHREST tutorial: Simulations of human learning

CHREST (Chunk Hierarchy and REtrieval STructures) is a comprehensive, computational model of human learning and perception. It has been used to successfully simulate data in a variety of domains, including: the acquisition of syntactic categories, expert behaviour, concept formation, implicit learning, and the acquisition of multiple representations in physics for problem solving. The aim of this tutorial is to provide participants with an introduction to CHREST, how it can be used to model various phenomena, and the knowledge to carry out their own modelling experiments

Prompting students' knowledge integration and ill-structured problem solving in a Web-based learning environment.

The purpose of this study was to determine the effect of different types of question prompts on students' knowledge acquisition and ill-structured problem solving outcomes. Although there are consistent findings regarding the processes of knowledge integration in science, few studies have focused on supporting students' knowledge acquisition and problem solving in more ill-structured domains. Hence, this study explored the use of knowledge integration prompts and problem solving prompts to support students' knowledge acquisition and ill-structured problem solving, especially the processes of problem representation, developing and evaluating solutions, and monitoring and justifying a plan of action.In order to investigate how such integrated understandings can be promoted in a web-based learning environment, this research used a knowledge integration approach to teaching and learning that provided a framework for promoting the application of educational concepts to classroom problems. Educational measurement served as the educational context for the focal curriculum. The curriculum design incorporated knowledge integration prompts to help students integrate knowledge, and problem solving prompts to facilitate students' problem solving processes.Students need to apply knowledge to important issues every day. Therefore, designing school curricula that foster integration of knowledge and everyday problem solving becomes critical. Students must develop integrated understandings of principles, prior experience, and applicable knowledge in order to be effective and efficient problem solvers.The research design and assessment protocols included two phases of the curriculum---knowledge acquisition phase and problem solving phase---and four treatments---knowledge integration condition, problem solving condition, combination of knowledge integration and problem solving condition, and control condition. The web-based environment was tested with undergraduate pre-service teachers. Pretest-posttest and ill-structured problem solving outcomes were compared between the treatments. Results indicated that knowledge integration prompts promoted better knowledge acquisition than did problem solving prompts. However, knowledge integration prompts alone were not completely sufficient for ill-structured problem solving. In fact, the combination of knowledge integration and problem solving prompts helped students not only integrate but also apply knowledge of principles and concepts to real-world problems. These results have implications for designing curricula in ill-defined domains and for instructional designers seeking to integrate and promote the application of educational principles to real-world problems