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Conference on Tools With Artificial Intelligence 

About: Conference on Tools With Artificial Intelligence is an academic conference. The conference publishes majorly in the area(s): Knowledge-based systems & Artificial neural network. Over the lifetime, 148 publication(s) have been published by the conference receiving 1660 citation(s).

Papers published on a yearly basis

Papers
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Proceedings ArticleDOI
08 Nov 1993
TL;DR: Results are presented which suggested that genetic algorithms can be used to increase the robustness of feature selection algorithms without a significant decrease in compuational efficiency.
Abstract: Selecting a set of features which is optimal for a given task is a problem which plays an important role in wide variety of contexts including pattern recognition, adaptive control and machine learning. Experience with traditional feature selection algorithms in the domain of machine learning leads to an appreciation for their computational efficiency and a concern for their brittleness. The authors describe an alternative approach to feature selection which uses genetic algorithms as the primary search component. Results are presented which suggested that genetic algorithms can be used to increase the robustness of feature selection algorithms without a significant decrease in compuational efficiency.

176 citations

Proceedings ArticleDOI
08 Nov 1993
TL;DR: A new class of constraint recording algorithms called Nogood Recording is proposed that may be used for solving both static and dynamic CSPs and offers an interesting compromise, polynomially bounded in space, between an ATMS-like approach and the usual static constraint satisfaction algorithms.
Abstract: Many AI synthesis problems such as planning, scheduling or design may be encoded in a constraint satisfaction problem (CSP). A CSP is typically defined as the problem of finding any consistent labeling for a fixed set of variables satisfying all given constraints between these variables. However, for many real tasks, the set of constraints to consider may evolve because of the environment or because of user interactions. The problem considered here is the solution maintenance problem in such a dynamic CSP (DCSP). The authors propose a new class of constraint recording algorithms called Nogood Recording that may be used for solving both static and dynamic CSPs. It offers an interesting compromise, polynomially bounded in space, between an ATMS-like approach and the usual static constraint satisfaction algorithms.

162 citations

Proceedings ArticleDOI
13 Nov 2000
TL;DR: The paper describes the results of applying Latent Semantic Analysis (LSA), an advanced information retrieval method, to program source code and associated documentation to assist in the understanding of a nontrivial software system, namely a version of Mosaic.
Abstract: The paper describes the results of applying Latent Semantic Analysis (LSA), an advanced information retrieval method, to program source code and associated documentation. Latent semantic analysis is a corpus based statistical method for inducing and representing aspects of the meanings of words and passages (of natural language) reflective in their usage. This methodology is assessed for application to the domain of software components (i.e., source code and its accompanying documentation). Here LSA is used as the basis to cluster software components. This clustering is used to assist in the understanding of a nontrivial software system, namely a version of Mosaic. Applying latent semantic analysis to the domain of source code and internal documentation for the support of program understanding is a new application of this method and a departure from the normal application domain of natural language.

108 citations

Proceedings ArticleDOI
13 Nov 2000
TL;DR: It is believed that defeasible logic, with its efficiency and simplicity is a good candidate to be used as a modelling language for practical applications, including modelling of regulations and business rules.
Abstract: For many years, the non-monotonic reasoning community has focussed on highly expressive logics. Such logics have turned out to be computationally expensive, and have given little support to the practical use of non-monotonic reasoning. In this work we discuss defeasible logic, a less-expressive but more efficient non-monotonic logic. We report on two new implemented systems for defeasible logic: a query answering system employing a backward chaining approach, and a forward-chaining implementation that computes all conclusions. Our experimental evaluation demonstrates that the systems can deal with large theories (up to hundreds of thousands of rules). We show that defeasible logic has linear complexity, which contrasts markedly with most other non-monotonic logics and helps to explain the impressive experimental results. We believe that defeasible logic, with its efficiency and simplicity is a good candidate to be used as a modelling language for practical applications, including modelling of regulations and business rules.

78 citations

Proceedings ArticleDOI
13 Nov 2000
TL;DR: This paper proposes an intercross iterative approach for training SVM to incremental learning taking the possible impact of new training data to history data each other into account and shows that this approach has more satisfying accuracy in classification precision.
Abstract: The classification algorithm that is based on a support vector machine (SVM) is now attracting more attention, due to its perfect theoretical properties and good empirical results. In this paper, we first analyze the properties of the support vector (SV) set thoroughly, then introduce a new learning method, which extends the SVM classification algorithm to the incremental learning area. The theoretical basis of this algorithm is the classification equivalence of the SV set and the training set. In this algorithm, knowledge is accumulated in the process of incremental learning. In addition, unimportant samples are discarded optimally by a least-recently used (LRU) scheme. Theoretical analyses and experimental results showed that this algorithm could not only speed up the training process, but it could also reduce the storage costs, while the classification precision is also guaranteed.

78 citations

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Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
20011
200064
199383