THE USE OF EXPERT SYSTEM BUILDING TOOLS IN PROCESS PLANNING

1992-01-01
ESKICIOGLU, H
The use of expert systems is a break-through in solving problems which require complex decision-making based on the past experience of an expert human being rather than relying on sequential algorithms which include simple combination of logical rules and calculations. Expert system building tools are programming systems developed to facilitate the construction of expert systems in a particular domain with less effort and in a relatively shorter time. Expert system building tools are now moving from Lisp machines to low-cost PC-based work stations, providing almost the same environment for representing, reasoning and explaining knowledge as on larger systems. This paper describes the basic characteristics and features of expert system building tools and discusses the use and the importance of these basic characteristics and features from a perspective of computer aided process planning (CAPP), requirements. Existing features are explained and evaluated to facilitate the selection and use of expert system building tools in automating the process planning task.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Suggestions

Coordination of intelligent agents in real-time search
Cakir, A; Polat, Faruk (Wiley, 2002-05-01)
Search is a fundamental problem-solving method in artificial intelligence. Traditional off-line search algorithms attempt to find an optimal solution whereas real-time search algorithms try to find a suboptimal solution more quickly than traditional algorithms to meet real-time constraints. In this work, a new multi-agent real-time search algorithm is developed and its effectiveness is illustrated on a sample domain, namely maze problems. Searching agents can see their environment with a specified visual de...
AN EFFICIENT DATABASE TRANSITIVE CLOSURE ALGORITHM
Toroslu, İsmail Hakkı; HENSCHEN, L (Springer Science and Business Media LLC, 1994-05-01)
The integration of logic rules and relational databases has recently emerged as an important technique for developing knowledge management systems. An important class of logic rules utilized by these systems is the so-called transitive closure rules, the processing of which requires the computation of the transitive closure of database relations referenced by these rules. This article presents a new algorithm suitable for computing the transitive closure of very large database relations. This algorithm proc...
Quantitative measure of observability for linear stochastic systems
Subasi, Yuksel; Demirekler, Mübeccel (Elsevier BV, 2014-06-01)
In this study we define a new observability measure for stochastic systems: the mutual information between the state sequence and the corresponding measurement sequence for a given time horizon. Although the definition is given for a general system representation, the paper focuses on the linear time invariant Gaussian case. Some basic analytical results are derived for this special case. The measure is extended to the observability of a subspace of the state space, specifically an individual state and/or t...
Domain adaptation on graphs by learning graph topologies: theoretical analysis and an algorithm
Vural, Elif (The Scientific and Technological Research Council of Turkey, 2019-01-01)
Traditional machine learning algorithms assume that the training and test data have the same distribution, while this assumption does not necessarily hold in real applications. Domain adaptation methods take into account the deviations in data distribution. In this work, we study the problem of domain adaptation on graphs. We consider a source graph and a target graph constructed with samples drawn from data manifolds. We study the problem of estimating the unknown class labels on the target graph using the...
A pattern classification approach for boosting with genetic algorithms
Yalabık, Ismet; Yarman Vural, Fatoş Tunay; Üçoluk, Göktürk; Şehitoğlu, Onur Tolga (2007-11-09)
Ensemble learning is a multiple-classifier machine learning approach which produces collections and ensembles statistical classifiers to build up more accurate classifier than the individual classifiers. Bagging, boosting and voting methods are the basic examples of ensemble learning. In this study, a novel boosting technique targeting to solve partial problems of AdaBoost, a well-known boosting algorithm, is proposed. The proposed system finds an elegant way of boosting a bunch of classifiers successively ...
Citation Formats
H. ESKICIOGLU, “THE USE OF EXPERT SYSTEM BUILDING TOOLS IN PROCESS PLANNING,” ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, pp. 33–42, 1992, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/64257.