A Complete and Recursive Feature Theory

Various feature descriptions are being employed in logic programming languages and constrained-based grammar formalisms. The common notational primitive of these descriptions are functional attributes called features. The descriptions considered in this paper are the possibly quantified first-order formulae obtained from a signature of binary and unary predicates called features and sorts, respectively. We establish a first-order theory FT by means of three axiom schemes, show its completeness, and construct three elementarily equivalent models. One of the models consists of so-called feature graphs, a data structure common in computational linguistics. The other two models consist of so-called feature trees, a record-like data structure generalizing the trees corresponding to first-order terms. Our completeness proof exhibits a terminating simplification system deciding validity and satisfiability of possibly quantified feature descriptions.Comment: Short version appeared in the 1992 Annual Meeting of the Association for Computational Linguistic

Concept logics

Concept languages (as used in BACK, KL-ONE, KRYPTON, LOOM) are employed as knowledge representation formalisms in Artificial Intelligence. Their main purpose is to represent the generic concepts and the taxonomical hierarchies of the domain to be modeled. This paper addresses the combination of the fast taxonomical reasoning algorithms (e.g. subsumption, the classifier etc.) that come with these languages and reasoning in first order predicate logic. The interface between these two different modes of reasoning is accomplished by a new rule of inference, called constrained resolution. Correctness, completeness as well as the decidability of the constraints (in a restricted constraint language) are shown

Coordinated Pitch Observation for a Humanoid Robot Soccer Team

Abstract—While the quality of matches between the teams in the RoboCup Standard Platform League has increased a lot, there are still certain situations that prevent the game from progressing. One of the most severe ones is when a team loses track of the ball, because it cannot score goals or prevent the opponent team from scoring goals without knowing where the ball is. In this paper a method is presented to quickly find the ball again by searching the least-recently observed parts of the pitch. A consistent model shared by all robots of the team to identify these parts of the field is explained, as well as the procedure to coordinate the observation among the teammates, such that a varying number of robots can participate in the process. I

X2MORF: A Morphological Component

organization which was founded in 1988 by the shareholder companies ADV/Orga, AEG, IBM, Insiders, Fraunhofer Gesellschaft, GMD, Krupp-Atlas, Mannesmann-Kienzle, Siemens-Nixdorf, Philips and Siemens. Research projects conducted at the DFKI are funded by the German Ministry for Research and Technology, by the shareholder companies, or by other industrial contracts. The DFKI conducts application-oriented basic research in the field of artificial intelligence and other related subfields of computer science. The overall goal is to construct systems with technical knowledge and common sense which- by using AI methods- implement a problem solution for a selected application area. Currently, there are the following research areas at the DFKI: o Intelligent Engineering Systems o Intelligent User Interface