Engineering experts critics for cooperative systems

1993 ◽  
Vol 8 (4) ◽  
pp. 309-328 ◽  
Author(s):  
Barry G. Silverman ◽  
R. Gregory Wenig
Keyword(s):  
Knowledge Base ◽  
Traditional Knowledge ◽  
Knowledge Engineering ◽  
Domain Expert ◽  
Human Knowledge ◽  
Cooperative System ◽  
Knowledge Engineer ◽  
Human Expert ◽  
Knowledge Based ◽  
To Come

AbstractKnowledge collection systems often assume they are cooperating with an unbiased expert. They have few functions for checking and fixing the realism of the expertise transferred to the knowledge base, plan, document or other product of the interaction. The same problem arises when human knowledge engineers interview experts. The knowledge engineer may suffer from the same biases as the domain expert. Such biases remain in the knowledge base and cause difficulties for years to come.To prevent such difficulties, this paper introduces the reader to “critic engineering”, a methodology that is useful when it is necessary to doubt, trap and repair expert judgment during a knowledge collection process. With the use of this method, the human expert and knowledge-based critic form a cooperative system. Neither agent alone can complete the task as well as the two together.The methodology suggested here offers a number of extensions to traditional knowledge engineering techniques. Traditional knowledge engineering often answers the questions delineated in generic task (GT) theory, yet GT theory fails to provide four additional sets of questions that one must answer to engineer a knowledge base, plan, design or diagnosis when the expert is prone to error. This extended methodology is called “critic engineering”.

scholarly journals Modeling Frameworks for Knowledge Engineering Approaches

2022 ◽  
pp. 15-20
Author(s):  
Daniel Ashlock
Keyword(s):  
Knowledge Base ◽  
Transfer Process ◽  
Knowledge Engineering ◽  
Knowledge Based Systems ◽  
Human Knowledge ◽  
Production Rules ◽  
Knowledge Based ◽  
New Ideas ◽  
The Creation ◽  
Applied Knowledge

Human knowledge was regarded as a transfer process into an applied knowledge base in the early 1980s as the creation of a Knowledge-Based Systems (KBS). The premise behind this transfer was that the KBS-required information already existed and only needed to be gathered and applied. Most of the time, the necessary information was gleaned through talking to professionals about how they handle particular problems. This knowledge was usually put to use in production rules, which were then carried out by a rule interpreter linked to them. Here, we demonstrate a number of new ideas and approaches that have emerged during the last few years. This paper presents MIKE, PROTÉGÉ-II, and Common KADS as three different modeling frameworks that may be used together or separately.

Towards Knowledge-Based Spatial Planning

2019 ◽  
pp. 1-15
Author(s):  
Robert Laurini
Keyword(s):  
Big Data ◽  
Spatial Planning ◽  
Knowledge Engineering ◽  
Big Data Analytics ◽  
Human Knowledge ◽  
Collective Knowledge ◽  
Knowledge Based ◽  
Engineering Practices ◽  
Physical Laws

For millennia, spatial planning has been based on human knowledge about the context and its environment together with some objectives of development. Now, with artificial intelligence and especially knowledge engineering, practices of spatial planning can be renovated. Presently, novel practices can be designed. In addition to human collective knowledge, some new chunks of knowledge can be introduced, coming from physical laws, administrative regulations, standards, data mining, and best practices. By big data analytics, some regularities and patterns can be discovered, which again will lead to new actions towards cities: in other words, there is a virtuous circle linking smart territories and big data that can be the basis for novel spatial planning. The role of this chapter will be to analyze those new chunks of knowledge and to explain how human knowledge, possibly coming from different stakeholders, can be harmonized with machine-processable knowledge as to be the basis for territorial intelligence.

Knowledge Acquisition for Large-Scale Expert Systems in Transportation

1998 ◽  
Vol 1651 (1) ◽  
pp. 59-65
Author(s):  
Jeffrey L. Adler ◽  
Eknauth Persaud
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Traditional Knowledge ◽  
Large Scale ◽  
Knowledge Engineering ◽  
Problem Domain ◽  
Client Server ◽  
Server Application

One of the greatest challenges in building an expert system is obtaining, representing, and programming the knowledge base. As the size and scope of the problem domain increases, knowledge acquisition and knowledge engineering become more challenging. Methods for knowledge acquisition and engineering for large-scale projects are investigated in this paper. The objective is to provide new insights as to how knowledge engineers play a role in defining the scope and purpose of expert systems and how traditional knowledge acquisition and engineering methods might be recast in cases where the expert system is a component within a larger scale client-server application targeting multiple users.

Development of Knowledge Based System to Facilitate Design of On-Board Car Safety Systems

2003 ◽  
Author(s):  
Walid Ben Ahmed ◽  
Michel Bigand ◽  
Mounib Mekhilef ◽  
Yves Page
Keyword(s):  
Knowledge Base ◽  
Knowledge Engineering ◽  
Cognitive Approach ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Car Safety ◽  
Accident Data ◽  
Safety Systems ◽  
Expert Classification

The development of on-board car safety systems requires an accidentology knowledge base for the development of new functionalities as well as their improvement and evaluation. The Knowledge Discovery in accident Database (KDD) is one of the approaches allowing the construction of this knowledge base. However, considering the complexity of the accident data and the variety of their sources (biomechanics, psychology, mechanics, ergonomics, etc.), the analytical methods of the KDD (clustering, classification, association rules etc.) should be combined with expert approaches. Indeed, there is background knowledge in accidentology which exists in the minds of accidentologist experts and which is not formalized in the accident database. The aim of this paper is to develop a Knowledge Representation Model (KRM) intended to incorporate this knowledge in the KDD process. The KRM is implemented in a knowledge-based system, which provides an expert classification of the attributes characterizing an accident. This expert classification provides an efficient tool for data preparation in a KDD process. Our method consists of combining the modeling systemic approach of complex systems and the modeling cognitive approach KOD (Knowledge Oriented Design) in knowledge engineering.

scholarly journals Knowledge Engineering the Web

2021 ◽  
Vol 11 (1) ◽  
pp. 68-76
Author(s):  
Ben Choi ◽  
Keyword(s):  
Search Engine ◽  
Knowledge Engineering ◽  
State Of The Art ◽  
The State ◽  
Human Knowledge ◽  
Search Results ◽  
New Methods ◽  
Knowledge Based ◽  
The Web

This paper focuses on the largest source of human knowledge: The Web. It presents the state of the art and patented technologies on search engine, automatic organization of webpages, and knowledge-based automatic webpage summarization. For the patented search engine technology, it describes new methods to present search results to the users and through browsers to allow the users to customize and organize webpages. For the patented classification technology, it describes new methods to automatically organize webpages into categories. For the knowledge-based summarization technology, it presents new technics for computers to "read" webpages and then to "write" a summary by creating new sentences to describe the contents of the webpages. These search engine, classification, and summarization technologies build a strong framework for knowledge engineering the Web.

Application of Expert Systems Technology in Water Quality Modeling

1989 ◽  
Vol 21 (8-9) ◽  
pp. 1045-1056 ◽  
Author(s):  
Thomas O. Barnwell ◽  
Linfield C. Brown ◽  
Wiktor Marek
Keyword(s):  
Water Quality ◽  
Expert Systems ◽  
Knowledge Engineering ◽  
Stream Water ◽  
Environmental Modeling ◽  
Water Quality Modeling ◽  
Stream Water Quality ◽  
Quality Model ◽  
Knowledge Based ◽  
Available Technology

Computerized modeling is becoming an integral part of decision making in water pollution control. Expert systems is an innovative methodology that can assist in building, using, and interpreting the output of these models. This paper reviews the use and evaluates the potential of expert systems technology in environmental modeling and describes elements of an expert advisor for the stream water quality model QUAL2E. Some general conclusions are presented about the tools available to develop this system, the level of available technology in knowledge-based engineering, and the value of approaching problems from a knowledge engineering perspective.

Knowledge-Based System supported by Chatbot to assist the Risk Classification Process of Patients in Hospital Emergency care (Preprint)

2021 ◽  
Author(s):  
Marciane Mueller ◽  
Rejane Frozza ◽  
Liane Mählmann Kipper ◽  
Ana Carolina Kessler
Keyword(s):  
Medical Care ◽  
Knowledge Base ◽  
Language Processing ◽  
Emergency Care ◽  
Risk Classification ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Hospital Emergency ◽  
Knowledge Based System ◽  
Knowledge Based

BACKGROUND This article presents the modeling and development of a Knowledge Based System, supported by the use of a virtual conversational agent called Dóris. Using natural language processing resources, Dóris collects the clinical data of patients in care in the context of urgency and hospital emergency. OBJECTIVE The main objective is to validate the use of virtual conversational agents to properly and accurately collect the data necessary to perform the evaluation flowcharts used to classify the degree of urgency of patients and determine the priority for medical care. METHODS The agent's knowledge base was modeled using the rules provided for in the evaluation flowcharts comprised by the Manchester Triage System. It also allows the establishment of a simple, objective and complete communication, through dialogues to assess signs and symptoms that obey the criteria established by a standardized, validated and internationally recognized system. RESULTS Thus, in addition to verifying the applicability of Artificial Intelligence techniques in a complex domain of health care, a tool is presented that helps not only in the perspective of improving organizational processes, but also in improving human relationships, bringing professionals and patients closer. The system's knowledge base was modeled on the IBM Watson platform. CONCLUSIONS The results obtained from simulations carried out by the human specialist allowed us to verify that a knowledge-based system supported by a virtual conversational agent is feasible for the domain of risk classification and priority determination of medical care for patients in the context of emergency care and hospital emergency.

Cosmology and Anankê in the Timaeus and Our Knowledge of the Forms

Apeiron ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Naomi Reshotko
Keyword(s):  
Human Knowledge ◽  
New Beginning ◽  
The Law ◽  
To Come ◽  
Entire Cosmos ◽  
Coming To Know

Abstract At Tm. 47e, Timaeus steps back from his discussion of what came about through noûs and turns toward an account of what came about through anankê. Broadie, 2012, Nature and Divinity in Plato’s Timaeus, sketches out two routes for the interpretation of this ‘new beginning.’ The ‘metaphysical’ approach uses perceptibles qua imitations of intelligibles in order to glimpse the intelligibles (just as we look at our reflection in a mirror in order to view ourselves). The ‘cosmological’ reading assumes we use the perceptible part of the cosmos in order to come to know the entire cosmos. Broadie openly favors the cosmological reading for understanding the Timeaus as a whole. However, she confines its utility to the Timaeus and does not recommend it for other dialogues. I use Broadie’s ‘cosmological reading’ to better understand what Plato distinguishes as anankê in his second beginning. This sets the stage for my argument that Broadie’s cosmological reading is a promising means for understanding the metaphysics and epistemology of the Forms. By making some comparisons to Sophist (251c–256a), I show that a refined understanding of anankê in the second beginning of the Timaeus clarifies what Plato thinks is involved in coming to know a Form. I argue that a close look at what was available to the Demiurge for cosmic creation by means of noûs yields three distinct ways in which his construction of the cosmos was limited by anankê. Clarifying these three ways in which anankê operates shows that the Demiurge’s manipulation of the foundational elements yields a perceptible world that brings out some potential relationships among Forms while suppressing others. In particular, the Demiurge’s geometricization of the elements leads him to make compromises concerning how Forms can combine in the Receptacle. These choices produce nomological relationships among the Forms with respect to where they can overlap in the Receptacle. This produces the law-like and reliable, but unnecessary, behavior of the perceptible world. I argue that our understanding of these limitations and their translation into where the Receptacle can partake in more than one Form simultaneously, figures importantly in the estimating the potential for human knowledge of the Forms. I question the use of ‘necessity’ as a translation for ‘anankê’ in the Timaeus.

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