Journal•ISSN: 0921-7126
Ai Communications
IOS Press
About: Ai Communications is an academic journal published by IOS Press. The journal publishes majorly in the area(s): Computer science & Automated theorem proving. It has an ISSN identifier of 0921-7126. Over the lifetime, 890 publications have been published receiving 24191 citations. The journal is also known as: Artificial intelligence communications & AICOM.
Topics: Computer science, Automated theorem proving, Decision support system, Multi-agent system, Context (language use)
Papers published on a yearly basis
Papers
More filters
••
TL;DR: An overview of the foundational issues related to case-based reasoning is given, some of the leading methodological approaches within the field are described, and the current state of the field is exemplified through pointers to some systems.
Abstract: Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.
5,750 citations
••
TL;DR: Computer and Robot Vision Vol.
Abstract: Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.
1,426 citations
••
TL;DR: This paper gives an overview of the open source project Potassco, the Potsdam Answer Set Solving Collection, bundling tools for Answer Set Programming developed at the University ofPotsdam.
Abstract: This paper gives an overview of the open source project Potassco, the Potsdam Answer Set Solving Collection, bundling tools for Answer Set Programming developed at the University of Potsdam.
538 citations
••
TL;DR: A solid intuition is built for what is LDA, and how LDA works, thus enabling readers of all levels to get a better understanding of the LDA and to know how to apply this technique in different applications.
Abstract: Linear Discriminant Analysis (LDA) is a very common
technique for dimensionality reduction problems as a preprocessing
step for machine learning and pattern classification
applications. At the same time, it is usually used as a
black box, but (sometimes) not well understood. The aim of
this paper is to build a solid intuition for what is LDA, and
how LDA works, thus enabling readers of all levels be able
to get a better understanding of the LDA and to know how to
apply this technique in different applications. The paper first
gave the basic definitions and steps of how LDA technique
works supported with visual explanations of these steps.
Moreover, the two methods of computing the LDA space, i.e.
class-dependent and class-independent methods, were explained
in details. Then, in a step-by-step approach, two numerical
examples are demonstrated to show how the LDA
space can be calculated in case of the class-dependent and
class-independent methods. Furthermore, two of the most
common LDA problems (i.e. Small Sample Size (SSS) and
non-linearity problems) were highlighted and illustrated, and
state-of-the-art solutions to these problems were investigated and explained. Finally, a number of experiments was conducted
with different datasets to (1) investigate the effect of
the eigenvectors that used in the LDA space on the robustness
of the extracted feature for the classification accuracy,
and (2) to show when the SSS problem occurs and how it can
be addressed.
518 citations
•
TL;DR: E is a sound and complete prover for clausal first order logic with equality and has a very flexible interface for specifying search control heuristics, and an efficient inference engine.
Abstract: We describe the superposition-based theorem prover E. E is a sound and complete prover for clausal first order logic with equality. Important properties of the prover include strong redundancy elimination criteria, the DISCOUNT loop proof procedure, a very flexible interface for specifying search control heuristics, and an efficient inference engine. We also discuss strength and weaknesses of the system.
515 citations