Kaiserslautern - Fachbereich Informatik
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We present a method for making use of past proof experience called flexiblere-enactment (FR). FR is actually a search-guiding heuristic that uses past proofexperience to create a search bias. Given a proof P of a problem solved previouslythat is assumed to be similar to the current problem A, FR searches for P andin the "neighborhood" of P in order to find a proof of A.This heuristic use of past experience has certain advantages that make FRquite profitable and give it a wide range of applicability. Experimental studiessubstantiate and illustrate this claim.This work was supported by the Deutsche Forschungsgemeinschaft (DFG).
In this paper we provide a semantical meta-theory that will support the development of higher-order calculi for automated theorem proving like the corresponding methodology has in first-order logic. To reach this goal, we establish classes of models that adequately characterize the existing theorem-proving calculi, that is, so that they are sound and complete to these calculi, and a standard methodology of abstract consistency methods (by providing the necessary model existence theorems) needed to analyze completeness of machine-oriented calculi.
This paper describes an Internet-scalable knowledge base infrastructure for managing the knowledge used by an in-telligent software productivity infrastructure system. The infrastructure provides workable solutions for several significant issues: (1) Internetunique names for pieces of knowledge; (2) multi-platform, multi-language support; (3) distributed knowledge base synchronization mechanisms; (4) support for extensive customized variations in knowledge content, and (5) knowledge caching mechanisms for improved system performance. The infrastructure described here is a workable example of the kind of infrastructure that will be required to manage the evolution and reuse of millions of pieces of knowledge in the future.
Viele Entwicklungsprozesse, wie sie z.B. beim Entwurf von grossen Softwaresystemen benötigt werden, basieren in erster Linie auf dem Wissen der mit der Entwicklung betrauten Mitarbeiter. Mit wachsender Komplexität der Entwurfsaufgaben und mit wachsender Anzahl der Mitarbeiter in einem Projekt wird die Koordination und Verteilung dieses Wissens immer problematischer. Aus diesem Grund versucht man zunehmend, das Wissen der Mitarbeiter in elektronischer Form, d.h. in Rechnern zu speichern und zu verwalten. Dadurch, dass der Entwurf eines komplexen Systems ebenfalls am Rechner modelliert wird, steht benötigtes Wissen sofort zur Verfügung und kann zur Entscheidungsunterstützung herangezogen werden. Gerade bei der Planung grosser Projekte stehen jedoch oft Entscheidungen aus, die erst später, während der Abwicklung getroffen werden können. Da gängige Workflow-Management-System zumeist eine komplette Modellierung verlangen, bevor die Abwicklung eines Projektmodells beginnen kann, habt sich dieser Ansatz gerade für umfangreiche Projekte als eher ungeeignet herausgestellt.
This paper provides a description of PLATIN. With PLATIN we present an imple-mented system for planning inductive theorem proofs in equational theories that arebased on rewrite methods. We provide a survey of the underlying architecture ofPLATIN and then concentrate on details and experiences of the current implementa-tion.
We present a distributed system, Dott, for approximately solving the Trav-eling Salesman Problem (TSP) based on the Teamwork method. So-calledexperts and specialists work independently and in parallel for given time pe-riods. For TSP, specialists are tour construction algorithms and experts usemodified genetic algorithms in which after each application of a genetic operatorthe resulting tour is locally optimized before it is added to the population. Aftera given time period the work of each expert and specialist is judged by a referee.A new start population, including selected individuals from each expert and spe-cialist, is generated by the supervisor, based on the judgments of the referees.Our system is able to find better tours than each of the experts or specialistsworking alone. Also results comparable to those of single runs can be found muchfaster by a team.
Due to continuously increasing demands in the area of advanced robot control, it became necessary to speed up the computation. One way to reduce the computation time is to distribute the computation onto several processing units. In this survey we present different approaches to parallel computation of robot kinematics and Jacobian. Thereby, we discuss both the forward and the reverse problem. We introduce a classification scheme and classify the references by this scheme.
We investigate in how far interpolation mechanisms based on the nearest-neighbor rule (NNR) can support cancer research. The main objective is to usethe NNR to predict the likelihood of tumorigenesis based on given risk factors.By using a genetic algorithm to optimize the parameters of the nearest-neighbourprediction, the performance of this interpolation method can be improved sub-stantially. Furthermore, it is possible to detect risk factors which are hardly ornot relevant to tumorigenesis. Our preliminary studies demonstrate that NNR-based interpolation is a simple tool that nevertheless has enough potential to beseriously considered for cancer research or related research.
We present a general framework for developing search heuristics for au-tomated theorem provers. This framework allows for the construction ofheuristics that are on the one hand able to replay (parts of) a given prooffound in the past but are on the other hand flexible enough to deviate fromthe given proof path in order to solve similar proof problems. We substanti-ate the abstract framework by the presentation of three distinct techniquesfor learning appropriate search heuristics based on soADcalled features. Wedemonstrate the usefulness of these techniques in the area of equational de-duction. Comparisons with the renowned theorem prover Otter validatethe applicability and strength of our approach.
Retrieving multiple cases is supposed to be an adequate retrieval strategy for guiding partial-order planners because of the recognized flexibility of these planners to interleave steps in the plans. Cases are combined by merging them. In this paper, we will examine two different kinds of merging cases in the context of partial-order planning. We will see that merging cases can be very difficult if the cases are merged eagerly. On the other hand, if cases are merged by avoiding redundant steps, the guidance of the additional cases tends to decrease with the number of covered goals and retrieved cases in domains having a certain kind of interactions. Thus, to retrieve a single case covering many of the goals of the problem or to retrieve fewer cases covering many of the goals is at least equally effective as to retrieve several cases covering all goals in these domains.