Prototype Sistem Pakar untuk Penjadwalan

Scheduling is a complex problem, which requires huge resource to solve. The resources required include faculty, lecturer, classrooms, and the period of time for teaching. The solution of academic scheduling in large scale still face up so many obstacles to done manually. The college has to give a schedule in certain time when every academic activity is not crash. Scheduling is needed to anticipate crash of students hours to study and lectures time to teach. Scheduling have to fill the boundary and condition so that it convenient when it used. Under these conditions, a system is needed to set the schedule will not crash so as to improve the work of everyone. The possibility to find the best result and the implemented method approach to solve the problem is use constrain satisfaction method. Making this scheduling application is started with build table of combination from class data, room data, lecture data and time slot and followed by initiation and calculation with genetic algorithm. During the process, we generate the time and room data of each lecturer and test if crash or no crash. From the result, indicate fine schedule means there is no crash between each other and all class can be scheduled. Scheduling is optimal if all of space and time that is provided can be filled without happen crash

Preprints of Proceedings of GWAI-92

This is a preprint of the proceedings of the German Workshop on Artificial Intelligence (GWAI) 1992. The final version will appear in the Lecture Notes in Artificial Intelligence

Structure oriented case based reasoning

Das Ziel dieser Arbeit war es, durch Verwendung geeigneter vorhandener CAD-Pläne die Bearbeitung neuer CAD-Pläne zu unterstützen. Entstanden ist ein generischer Ansatz zum fallbasierten Schließens. Da in CAD-Plänen die räumliche Struktur eine wichtige Rolle spielt, ist das Konzept auf strukturorientierte Anwendungen ausgerichtet. Deshalb bezeichne ich es als ein Konzept zum " strukturorientierten fallbasierten Schließen". Die Arbeit spezifiziert das Minimum an Wissen, welches zur Suche und Wiederverwendung von Fällen benötigt wird, wie das darüber hinausgehende Wissen verarbeitet wird, welche Zusammenhänge es zum Beispiel zwischen Vergleichs- und Anpassungswissen gibt und wie man das Wissen modellieren kann. Zur Erläuterung wird das benötigte Wissen anhand verschiedener Anwendungen dargestellt. Das in der Arbeit vorgestellte Konzept erlaubt die Ergänzung, Detaillierung und Korrektur einer Anfrage. Die beiden entscheidenden Algorithmen dienen dem Vergleich von Anfrage und Fall und der Anpassung der Information des Falles zur Modifikation der Anfrage.The task of this thesis was the computer supported reuse of known CAD-designs in order to create new CAD-designs. The developed solution contains a generic approach to case based reasoning. Due to the relevance of spatial structures in CAD-designs the approach focusses on structure oriented applications. Therefore it is called an approach for „structure oriented case based reasoning". This thesis specifies the kind of the minimum knowledge required for retrieval and reuse of cases, how to integrate additional knowledge, relations between knowledge needed for comparision and adaption and how to model the knowledge. For illustration the required knowledge is described for different applications. The developed concept allows to extend, detail and correct a given query. The two most important algorithms are used to compare cases and query and to reuse the information found in a case to modify a query

Controlling generate & test in any time

Most problem solvers have a one-dimensional stop criterion: compute the correct and complete solution. Incremental algorithms can be interrupted at any time, returning a result that is more accurate the more time has been available. They allow the introduction of time as a new dimension into stop criteria. We can now define a system's utility in terms of the quality of its results and the time required to produce them. However, optimising utility introduces a new degree of complexity into our systems. To cope with it, we would like to separate the performance system to be optimised from utility management. Russell has proposed a completely generic precompilation approach which we show to be unsatisfactory for a generate & test problem solver. Analysing this type of systems we present four different strategies, which require different information and result in different behaviours. The strategy most suitable to our application requires on-line information, and hence had to be implemented by a meta-system rather than a precompiler. We conclude that universal utility managers are limited in power and are often inferior to more specialised though still generic one