Towards Games for Knowledge Acquisition and Modeling

2010 ◽  
Vol 2 (4) ◽  
pp. 48-66 ◽  
Author(s):  
Stijn Hoppenbrouwers ◽  
Bart Schotten ◽  
Peter Lucas
Keyword(s):  
Knowledge Acquisition ◽  
Machine Learning Techniques ◽  
Formal Representation ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Learning Techniques ◽  
Domain Specific Knowledge ◽  
Modelling Approach ◽  
Representation Of Knowledge

Many model-based methods in AI require formal representation of knowledge as input. For the acquisition of highly structured, domain-specific knowledge, machine learning techniques still fall short, and knowledge elicitation and modelling is then the standard. However, obtaining formal models from informants who have few or no formal skills is a non-trivial aspect of knowledge acquisition, which can be viewed as an instance of the well-known “knowledge acquisition bottleneck”. Based on the authors’ work in conceptual modelling and method engineering, this paper casts methods for knowledge modelling in the framework of games. The resulting games-for-modelling approach is illustrated by a first prototype of such a game. The authors’ long-term goal is to lower the threshold for formal knowledge acquisition and modelling.

Towards Games for Knowledge Acquisition and Modeling

2014 ◽  
pp. 281-299
Author(s):  
Stijn Hoppenbrouwers ◽  
Bart Schotten ◽  
Peter Lucas
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Elicitation ◽  
Machine Learning Techniques ◽  
Method Engineering ◽  
Formal Representation ◽  
Formal Knowledge ◽  
Domain Specific ◽  
Learning Techniques ◽  
Domain Specific Knowledge

Many model-based methods in AI require formal representation of knowledge as input. For the acquisition of highly structured, domain-specific knowledge, machine learning techniques still fall short, and knowledge elicitation and modelling is then the standard. However, obtaining formal models from informants who have few or no formal skills is a non-trivial aspect of knowledge acquisition, which can be viewed as an instance of the well-known “knowledge acquisition bottleneck”. Based on the authors’ work in conceptual modelling and method engineering, this paper casts methods for knowledge modelling in the framework of games. The resulting games-for-modelling approach is illustrated by a first prototype of such a game. The authors’ long-term goal is to lower the threshold for formal knowledge acquisition and modelling.

Conference interpreting and knowledge acquisition

Interpreting ◽  
2018 ◽  
Vol 20 (2) ◽  
pp. 204-231 ◽  
Author(s):  
Chia-chien Chang ◽  
Michelle Min-chia Wu ◽  
Tien-chun Gina Kuo
Keyword(s):  
Knowledge Acquisition ◽  
Target Audience ◽  
Language Usage ◽  
Specific Knowledge ◽  
Acquisition Process ◽  
Knowledge Domains ◽  
Domain Specific ◽  
Analytical Skills ◽  
Short Notice ◽  
Domain Specific Knowledge

Abstract This paper describes knowledge acquisition of professional conference interpreters in Taiwan when dealing with unfamiliar topics: the focus is on how the required knowledge is developed before, during and after a conference. We interviewed 10 Chinese-English interpreters, to find out about their preparation for such conferences and their approach to developing domain-specific knowledge. We first collected each interpreter’s five latest conference programs and used these to analyze the knowledge domains covered. We then based each interview on one conference agenda, considered representative by the interpreter, to examine the knowledge acquisition process from pre- to post-conference. The results show strategic preparation of unfamiliar topics: to facilitate comprehension and reformulation, interpreters make good use of conference documents and compile glossaries in which they organize the concepts and terminology specific to the conference. As they assimilate the language usage of the presenters and other participants during the conference, they use their analytical skills to manage any difficulties. Keeping in mind the aims of the event (e.g., commercial, scientific), as well as the profiles of the speakers and target audience, helps to optimize availability of relevant knowledge at short notice and continue updating it during the assignment.

scholarly journals Software Process Ontology: A case study of software organisations software process sub domains

2019 ◽  
pp. 122-130
Author(s):  
R. O. Oveh ◽  
O. Efevberha-Ogodo ◽  
F. A. Egbokhare
Keyword(s):  
Knowledge Base ◽  
Software Process ◽  
Formal Representation ◽  
Specific Knowledge ◽  
Process Ontology ◽  
Process Knowledge ◽  
Domain Specific ◽  
Domain Specific Knowledge ◽  
Intellectual Knowledge

In a domain like software process that is intensively knowledge driven, transforming intellectual knowledge by formal representation is an invaluable requirement. An improved use of this knowledge could lead to maximum payoff in software organisations which is key. The purpose of formal representation is to help organisations achieve success by modelling successful organisations. In this paper, Software process knowledge from successful organisations was harvested and formally modeled using ontology. Domain specific knowledge base ontology was produced for core software process subdomain, with its resulting software process ontology produced.

A Multi-Agent Decision Support Architecture for Knowledge Representation and Exchange

2011 ◽  
pp. 2558-2574
Author(s):  
Rahul Singh
Keyword(s):  
Decision Support ◽  
Knowledge Representation ◽  
Intelligent Agents ◽  
Machine Learning Techniques ◽  
Specific Knowledge ◽  
Knowledge Based ◽  
Intelligent Software Agents ◽  
Learning Techniques ◽  
Domain Specific Knowledge ◽  
Multi Agent

Organizations rely on knowledge-driven systems for delivering problem-specific knowledge over Internet-based distributed platforms to decision-makers. Recent advances in systems support for problem solving have seen increased use of artificial intelligence (AI) techniques for knowledge representation in multiple forms. This article presents an Intelligent Knowledge-based Multi-agent Decision Support Architecture” (IKMDSA) to illustrate how to represent and exchange domain-specific knowledge in XMLformat through intelligent agents to create, exchange and use knowledge in decision support. IKMDSA integrates knowledge discovery and machine learning techniques for the creation of knowledge from organizational data; and knowledge repositories (KR) for its storage management and use by intelligent software agents in providing effective knowledge-driven decision support. Implementation details of the architecture, its business implications and directions for further research are discussed.

scholarly journals Grammar Filtering for Syntax-Guided Synthesis

2020 ◽  
Vol 34 (02) ◽  
pp. 1611-1618
Author(s):  
Kairo Morton ◽  
William Hallahan ◽  
Elven Shum ◽  
Ruzica Piskac ◽  
Mark Santolucito
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Automated Reasoning ◽  
Search Space ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Domain Specific ◽  
Learning Techniques ◽  
Data Driven Approach ◽  
Domain Specific Knowledge

Programming-by-example (PBE) is a synthesis paradigm that allows users to generate functions by simply providing input-output examples. While a promising interaction paradigm, synthesis is still too slow for realtime interaction and more widespread adoption. Existing approaches to PBE synthesis have used automated reasoning tools, such as SMT solvers, as well as works applying machine learning techniques. At its core, the automated reasoning approach relies on highly domain specific knowledge of programming languages. On the other hand, the machine learning approaches utilize the fact that when working with program code, it is possible to generate arbitrarily large training datasets. In this work, we propose a system for using machine learning in tandem with automated reasoning techniques to solve Syntax Guided Synthesis (SyGuS) style PBE problems. By preprocessing SyGuS PBE problems with a neural network, we can use a data driven approach to reduce the size of the search space, then allow automated reasoning-based solvers to more quickly find a solution analytically. Our system is able to run atop existing SyGuS PBE synthesis tools, decreasing the runtime of the winner of the 2019 SyGuS Competition for the PBE Strings track by 47.65% to outperform all of the competing tools.

A Multi-Agent Decision Support Architecture for Knowledge Representation and Exchange

2009 ◽  
pp. 372-389
Author(s):  
Rahul Singh
Keyword(s):  
Decision Support ◽  
Knowledge Representation ◽  
Intelligent Agents ◽  
Machine Learning Techniques ◽  
Specific Knowledge ◽  
Knowledge Based ◽  
Intelligent Software Agents ◽  
Learning Techniques ◽  
Domain Specific Knowledge ◽  
Multi Agent

Organizations rely on knowledge-driven systems for delivering problem-specific knowledge over Internet-based distributed platforms to decision-makers. Recent advances in systems support for problem solving have seen increased use of artificial intelligence (AI) techniques for knowledge representation in multiple forms. This article presents an Intelligent Knowledge-based Multi-agent Decision Support Architecture” (IKMDSA) to illustrate how to represent and exchange domain-specific knowledge in XMLformat through intelligent agents to create, exchange and use knowledge in decision support. IKMDSA integrates knowledge discovery and machine learning techniques for the creation of knowledge from organizational data; and knowledge repositories (KR) for its storage management and use by intelligent software agents in providing effective knowledge-driven decision support. Implementation details of the architecture, its business implications and directions for further research are discussed.

Sign in / Sign up

Export Citation Format

Share Document