|
Ripple Down Rules
Ripple-down rules (RDR) are a way of approaching knowledge acquisition. Knowledge acquisition refers to the transfer of knowledge from human experts to knowledge-based systems. Introductory material Ripple-down rules are an incremental approach to knowledge acquisition and covers a family of techniques. RDR were proposed by Compton and Jansen based on experience maintaining the expert system GARVAN-ES1 (Compton and Jansen 1988). The original GARVAN-ES1 (Horn et al. 1985) employed a knowledge acquisition process, based on Test Driven Development, where new cases that were poorly classified by the system were added to a data base and then used to incrementally refine the knowledge base. The added cases, whose conclusions conflicted with the advice of the system were termed "cornerstone cases". Consequently, the data base grew iteratively with each refinement to the knowledge. The data base could then be used to test changes to the knowledge. Knowledge acquisition tools, similar t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge-based Systems
A knowledge-based system (KBS) is a computer program that reasons and uses a knowledge base to solve complex problems. The term is broad and refers to many different kinds of systems. The one common theme that unites all knowledge based systems is an attempt to represent knowledge explicitly and a reasoning system that allows it to derive new knowledge. Thus, a knowledge-based system has two distinguishing features: a knowledge base and an inference engine. The first part, the knowledge base, represents facts about the world, often in some form of subsumption ontology (rather than implicitly embedded in procedural code, in the way a conventional computer program does). Other common approaches in addition to a subsumption ontology include frames, conceptual graphs, and logical assertions. The second part, the inference engine, allows new knowledge to be inferred. Most commonly, it can take the form of IF-THEN rules coupled with forward chaining or backward chaining approaches ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Paul Justin Compton
Paul Compton (born 1944) is an Emeritus Professor at the University of New South Wales (UNSW). He was also the former Head of the UNSW School of Computer Science and Engineering. He is known for proposing "ripple-down rules". Career Paul Compton worked at the Garvan Institute of Medical Research, Garvan Institute before his appointment at UNSW. He was the Head of School from 1996–1998, and again from 2003-2010. He was very popular as Head of School, and upon his retirement a large gathering fare-welled him, as well as creating a YouTube slide-show tribute. Research Paul Compton along with R. Jansen proposed "ripple-down rules" in 1988.P. Compton and R. Jansen (1988). "Knowledge in Context: a strategy for expert system maintenance". Proc. Second Australian Joint Artificial Intelligence Conference. pp. 292–306. PhD graduates *Tri Minh Cao *Angela Finlayson *Mihye Kim *Maria Lee *Ashesh Mahidadia *Tim Menzies *Akara Prayote *Debbie Richards *Pramod Singh *Hendra Suryanto Jo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Decision Tree
A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in machine learning. Overview A decision tree is a flowchart-like structure in which each internal node represents a "test" on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represent classification rules. In decision analysis, a decision tree and the closely related influence diagram are used as a visual and analytical decision support tool, where t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Java (programming Language)
Java is a high-level, class-based, object-oriented programming language that is designed to have as few implementation dependencies as possible. It is a general-purpose programming language intended to let programmers ''write once, run anywhere'' ( WORA), meaning that compiled Java code can run on all platforms that support Java without the need to recompile. Java applications are typically compiled to bytecode that can run on any Java virtual machine (JVM) regardless of the underlying computer architecture. The syntax of Java is similar to C and C++, but has fewer low-level facilities than either of them. The Java runtime provides dynamic capabilities (such as reflection and runtime code modification) that are typically not available in traditional compiled languages. , Java was one of the most popular programming languages in use according to GitHub, particularly for client–server web applications, with a reported 9 million developers. Java was originally developed ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Weka (machine Learning)
Waikato Environment for Knowledge Analysis (Weka), developed at the University of Waikato, New Zealand, is free software licensed under the GNU General Public License, and the companion software to the book "Data Mining: Practical Machine Learning Tools and Techniques". Description Weka contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to these functions. The original non-Java version of Weka was a Tcl/ Tk front-end to (mostly third-party) modeling algorithms implemented in other programming languages, plus data preprocessing utilities in C, and a makefile-based system for running machine learning experiments. This original version was primarily designed as a tool for analyzing data from agricultural domains, but the more recent fully Java-based version (Weka 3), for which development started in 1997, is now used in many different application areas, in particular for educat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Case-based Reasoning
In artificial intelligence and philosophy, case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems. In everyday life, an auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning. A lawyer who advocates a particular outcome in a trial based on legal precedents or a judge who creates case law is using case-based reasoning. So, too, an engineer copying working elements of nature (practicing biomimicry), is treating nature as a database of solutions to problems. Case-based reasoning is a prominent type of analogy solution making. It has been argued that case-based reasoning is not only a powerful method for computer reasoning, but also a pervasive behavior in everyday human problem solving; or, more radically, that all reasoning is based on past cases personally experienced. This view is related to prototype theory, which is most deepl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Decision Trees
A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in machine learning. Overview A decision tree is a flowchart-like structure in which each internal node represents a "test" on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represent classification rules. In decision analysis, a decision tree and the closely related influence diagram are used as a visual and analytical decision support tool, where the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Multiple-classification Ripple-down Rules
Multiple-classification ripple-down rules (MCRDR) is an incremental knowledge acquisition technique which preserves the benefits and essential strategy of ripple-down rules (RDR) in handling the multiple classifications. MCRDR, the extension of RDR, is based on the assumption that the knowledge an expert provides is essentially a justification for a conclusion in a particular context. Implementations Below is a list of implementations of MCRDR * The alpha version of RDR(MCRDR) Framework was developed by UNSW and UTAS Research Team and funded by ARC (System available aBESTRDR * RDR(MCRDR) document classifier was developed by Dr.Yang Sok Kim and AProf.Byeong Ho Kang (System available aBESTRDR * RDR(MCRDR) smart expert system was developed by UTAS Research Team and funded by Hyundai Steel. * Pacific Knowledge Systems (PKS) uses a commercial product called RippleDown Expert that is based on Multiple Classification Ripple Down Rules Medscope Medication Review Mentoruses Multiple Class ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knowledge Management
Knowledge management (KM) is the collection of methods relating to creating, sharing, using and managing the knowledge and information of an organization. It refers to a multidisciplinary approach to achieve organisational objectives by making the best use of knowledge. An established List of academic disciplines, discipline since 1991, KM includes courses taught in the fields of business administration, information systems, management, Library science, library, and information science. Other fields may contribute to KM research, including information and media, computer science, public health and policy, public policy. Several universities offer dedicated master's degrees in knowledge management. Many large companies, public institutions, and non-profit organisations have resources dedicated to internal KM efforts, often as a part of their strategic management, business strategy, information technology, IT, or human resource management departments. Several consulting companies ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
_waving.svg "Click for more on -> Java (programming Language)")
