Acquisition And Understanding Of Process Knowledge Using Problem Solving Methods
Download Acquisition And Understanding Of Process Knowledge Using Problem Solving Methods PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Acquisition And Understanding Of Process Knowledge Using Problem Solving Methods book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Acquisition and Understanding of Process Knowledge Using Problem Solving Methods
The development of knowledge-based systems is usually approached through the combined skills of knowledge engineers (KEs) and subject matter experts (SMEs). One of the most critical steps in this activity aims at transferring knowledge from SMEs to formal, machine-readable representations, which allow systems to reason with such knowledge. However, this is a costly and error prone task. Alleviating the knowledge acquisition bottleneck requires enabling SMEs with the means to produce the desired knowledge representations without the help of KEs. This is especially difficult in the case of complex knowledge types, like processes. The analysis of different application domains uncovers that process knowledge is one of the most frequent knowledge types, whose complexity requires specific means to enable SMEs to represent processes in a computational form. Additionally, such complexity and the increasingly large amount of data that process executions generate in knowledge-intensive domains, like Biology or Astronomy, requires analytical means with high abstraction capabilities to support SMEs in the analysis of such processes. This book presents methods and tools that enable SMEs to acquire process knowledge from the domains, formally represent such knowledge, reason about it, and understand process executions by analyzing their provenance. We describe the utilization of Problem Solving Methods as the main knowledge artifacts for process acquisition and analysis in two innovative ways. First, as formalizations of the reasoning strategies needed for processes and, second, as high-level, domain-independent, and reusable abstractions of process knowledge to provide SMEs with interpretations of process executions.
Problem solving activities in post-editing and translation from scratch
Companies and organisations are increasingly using machine translation to improve efficiency and cost-effectiveness, and then edit the machine translated output to create a fluent text that adheres to given text conventions. This procedure is known as post-editing. Translation and post-editing can often be categorised as problem-solving activities. When the translation of a source text unit is not immediately obvious to the translator, or in other words, if there is a hurdle between the source item and the target item, the translation process can be considered problematic. Conversely, if there is no hurdle between the source and target texts, the translation process can be considered a task-solving activity and not a problem-solving activity. This study investigates whether machine translated output influences problem-solving effort in internet research, syntax, and other problem indicators and whether the effort can be linked to expertise. A total of 24 translators (twelve professionals and twelve semi-professionals) produced translations from scratch from English into German, and (monolingually) post-edited machine translation output for this study. The study is part of the CRITT TPR-DB database. The translation and (monolingual) post-editing sessions were recorded with an eye-tracker and a keylogging program. The participants were all given the same six texts (two texts per task). Different approaches were used to identify problematic translation units. First, internet research behaviour was considered as research is a distinct indicator of problematic translation units. Then, the focus was placed on syntactical structures in the MT output that do not adhere to the rules of the target language, as I assumed that they would cause problems in the (monolingual) post-editing tasks that would not occur in the translation from scratch task. Finally, problem indicators were identified via different parameters like Munit, which indicates how often the participants created and modified one translation unit, or the inefficiency (InEff) value of translation units, i.e. the number of produced and deleted tokens divided by the final length of the translation. Finally, the study highlights how these parameters can be used to identify problems in the translation process data using mere keylogging data.
Automating Knowledge Acquisition for Expert Systems
Author: Sandra Marcus
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-03-08
In June of 1983, our expert systems research group at Carnegie Mellon University began to work actively on automating knowledge acquisition for expert systems. In the last five years, we have developed several tools under the pressure and influence of building expert systems for business and industry. These tools include the five described in chapters 2 through 6 - MORE, MOLE, SALT, KNACK and SIZZLE. One experiment, conducted jointly by developers at Digital Equipment Corporation, the Soar research group at Carnegie Mellon, and members of our group, explored automation of knowledge acquisition and code development for XCON (also known as R1), a production-level expert system for configuring DEC computer systems. This work influenced the development of RIME, a programming methodology developed at Digital which is the subject of chapter 7. This book describes the principles that guided our work, looks in detail at the design and operation of each tool or methodology, and reports some lessons learned from the enterprise. of the work, brought out in the introductory chapter, is A common theme that much power can be gained by understanding the roles that domain knowledge plays in problem solving. Each tool can exploit such an understanding because it focuses on a well defined problem-solving method used by the expert systems it builds. Each tool chapter describes the basic problem-solving method assumed by the tool and the leverage provided by committing to the method.