In this paper we present a method and system for robot programming using virtual reality techniques. The proposed method allows intuitive teaching of a manipulation task with haptic feedback in a graphical simulation system. Based on earlier work, our system allows even an operator who lacks specialized knowledge of robotics to automatically generate a robust sensor-based robot program that is ready to execute on different robots, merely by demonstrating the task in virtual reality.
In diesem Papier beschreiben wir eine Methode zur Spezifikation und Operationalisierung von konzeptuellen Modellen kooperativer wissensbasierter Arbeitsabläufe. Diese erweitert bekannte Ansätze um den Begriff des Agenten und um alternative Aufgabenzerlegungen. Das Papier beschreibt schwerpunktmäßig Techniken, die unserem verteilten Interpreter zugrunde liegen. Dabei gehen wir insbesondere auf Methoden ein, die Abhängigkeiten zwischen Aufgaben behandeln und ein zielgerichtetes Backtracking effizient unterstützen.
In diesem Papier stellen wir einen Interpreter vor, der die Validierung von konzeptuellen Modellen bereits in fruehen Entwicklungsphasen unterstuetzt. Wir vergleichen Hypermedia- und Expertensystemansaetze zur Wissensverarbeitung und erlaeutern, wie ein integrierter Ansatz die Erstellung von Expertensystemen vereinfacht. Das von uns entwickelte Knowledge Engineering Werkzeug ermoeglicht einen "sanften" Uebergang von initialen Protokollen ueber eine semi-formale Spezifikation in Form eines getypten Hypertextes hin zu einem operationalen Expertensystem. Ein Interpreter nutzt die in diesem Prozess erzeugte Zwischenrepraesentation direkt zur interaktiven Loesung von Problemen, wobei einzelne Aufgaben ueber ein lokales Rechnernetz auf die Bearbeiter verteilt werden. Das heisst, die Spezifikation des Expertensystems wird direkt fuer die Loesung realer Probleme eingesetzt. Existieren zu einzelnen Teilaufgaben Operationalisierungen (d.h. Programme), dann werden diese vom Computer bearbeitet.
Typical instances, that is, instances that are representative for a particular situ-ation or concept, play an important role in human knowledge representationand reasoning, in particular in analogical reasoning. This wellADknown obser-vation has been a motivation for investigations in cognitive psychology whichprovide a basis for our characterization of typical instances within conceptstructures and for a new inference rule for justified analogical reasoning withtypical instances. In a nutshell this paper suggests to augment the proposi-tional knowledge representation system by a non-propositional part consistingof concept structures which may have directly represented instances as ele-ments. The traditional reasoning system is extended by a rule for justifiedanalogical inference with typical instances using information extracted fromboth knowledge representation subsystems.
We develop an order-sorted higher-order calculus suitable forautomatic theorem proving applications by extending the extensional simplytyped lambda calculus with a higher-order ordered sort concept and constantoverloading. Huet's well-known techniques for unifying simply typed lambdaterms are generalized to arrive at a complete transformation-based unificationalgorithm for this sorted calculus. Consideration of an order-sorted logicwith functional base sorts and arbitrary term declarations was originallyproposed by the second author in a 1991 paper; we give here a correctedcalculus which supports constant rather than arbitrary term declarations, aswell as a corrected unification algorithm, and prove in this setting resultscorresponding to those claimed there.
The introduction of sorts to first-order automated deduc-tion has brought greater conciseness of representation and a considerablegain in efficiency by reducing search spaces. This suggests that sort in-formation can be employed in higher-order theorem proving with similarresults. This paper develops a sorted (lambda)-calculus suitable for automatictheorem proving applications. It extends the simply typed (lambda)-calculus by ahigher-order sort concept that includes term declarations and functionalbase sorts. The term declaration mechanism studied here is powerfulenough to subsume subsorting as a derived notion and therefore gives ajustification for the special form of subsort inference. We present a set oftransformations for sorted (pre-) unification and prove the nondetermin-istic completeness of the algorithm induced by these transformations.
Most automated theorem provers suffer from the problemthat the resulting proofs are difficult to understand even for experiencedmathematicians. An effective communication between the system andits users, however, is crucial for many applications, such as in a mathematical assistant system. Therefore, efforts have been made to transformmachine generated proofs (e.g. resolution proofs) into natural deduction(ND) proofs. The state-of-the-art procedure of proof transformation fol-lows basically its completeness proof: the premises and the conclusionare decomposed into unit literals, then the theorem is derived by mul-tiple levels of proofs by contradiction. Indeterminism is introduced byheuristics that aim at the production of more elegant results. This inde-terministic character entails not only a complex search, but also leads tounpredictable results.In this paper we first study resolution proofs in terms of meaningful op-erations employed by human mathematicians, and thereby establish acorrespondence between resolution proofs and ND proofs at a more ab-stract level. Concretely, we show that if its unit initial clauses are CNFsof literal premises of a problem, a unit resolution corresponds directly toa well-structured ND proof segment that mathematicians intuitively un-derstand as the application of a definition or a theorem. The consequenceis twofold: First it enhances our intuitive understanding of resolutionproofs in terms of the vocabulary with which mathematicians talk aboutproofs. Second, the transformation process is now largely deterministicand therefore efficient. This determinism also guarantees the quality ofresulting proofs.
Using an experience factory is one possible concept for supporting and improving reuse in software development. (i.e., reuse of products, processes, quality models, ...). In the context of the Sonderforschungsbereich 501: "Development of Large Systems with Generic methods" (SFB501), the Software Engineering Laboratory (SE Lab) runs such an experience factory as part of the infrastructure services it offers. The SE Lab also provides several tools to support the planning, developing, measuring, and analyzing activities of software development processes. Among these tools, the SE Lab runs and maintains an experience base, the SFB-EB. When an experience factory is utilized, support for experience base maintenance is an important issue. Furthermore, it might be interesting to evaluate experience base usage with regard to the number of accesses to certain experience elements stored in the database. The same holds for the usage of the tools provided by the SE LAB. This report presents a set of supporting tools that were designed to aid in these tasks. These supporting tools check the experience base's consistency and gather information on the usage of SFB-EB and the tools installed in the SE Lab. The results are processed periodically and displayed as HTML result reports (consistency checking) or bar charts (usage profiles).