The problem to be discussed in this paper may be characterized in short by the question: "Are these two surface fragments belonging together (i.e. belonging to the same surface)?" The presented techniques try to benefit from some predefined knowledge as well as from the possibility to refine and adapt this knowledge according to a (changing) real environment, resulting in a combination of fuzzy-decision systems and neural networks. The results are encouraging (fast convergence speed, high accuracy), and the model might be used for a wide range of applications. The general frame surrounding the work in this paper is the SPIN- project, where emphasis is on sub-symbolic abstractions, based on a 3-d scanned environment.
Self-localization in unknown environments respectively correlation of current and former impressions of the world is an essential ability for most mobile robots. The method,proposed in this article is the construction of a qualitative, topological world model as a basis for self-localization. As a central aspect the reliability regarding error-tolerance and stability will be emphasized. The proposed techniques demand very low constraints for the kind and quality of the employed sensors as well as for the kinematic precisionof the utilized mobile platform. Hard real-time constraints can be handled due to the low computational complexity. The principal discussions are supported by real-world experiments with the mobile robot.
The problem to be discussed here, is the usage of neural network clustering techniques on a mobile robot, in order to build qualitative topologic environment maps. This has to be done in realtime, i.e. the internal world model has to be adapted by the flow of sensor- samples without the possibility to stop this data-flow.Our experiments are done in a simulation environment as well as on a robot, called ALICE.
Visual Search has been investigated by many researchers inspired by the biological fact, that the sensory elements on the mammal retina are not equably distributed. Therefore the focus of attention (the area of the retina with the highest density of sensory elements) has to be directed in a way to efficiently gather data according to certain criteria. The work discussed in this article concentrates on applying a laser range finder instead of a silicon retina. The laser range finder is maximal focused at any time, but therefore a low resolution total-scene-image, available with camera-like devices from scratch on, cannot be used here. By adapting a couple of algorithms, the edge-scanning module steering the laser range finder is able to trace a detected edge. Based on the data scanned so far , two questions have to be answered. First: "Should the actual (edge-) scanning be interrupted in order to give another area of interest a chance of being investigated?" and second: "Where to start a new edge-scanning, after being interrupted?". These two decision-problems might be solved by a range of decision systems. The correctness of the decisions depends widely on the actual environment and the underlying rules may not be well initialized with a-priori knowledge. So we will present a version of a reinforcement decision system together with an overall scheme for efficiently controlling highly focused devices.
Based on the experiences from an autonomous mobile robot project called MOBOT-III, we found hard realtime-constraints for the operating- system-design. ALBATROSS is "A flexible multi-tasking and realtime network-operating-system-kernel". The focusin this article is on a communication-scheme fulfilling the previous demanded assurances. The centralchapters discuss the shared buffer management and the way to design the communication architecture.Some further aspects beside the strict realtime-requirements like the possibilities to control and watch a running system, are mentioned. ALBATROSS is actually implemented on a multi-processor VMEbus-system.
This paper refers to the problem of adaptability over an infinite period of time, regarding dynamic networks. A never ending flow of examples have to be clustered, based on a distance measure. The developed model is based on the self-organizing feature maps of Kohonen ,  and some adaptations by Fritzke . The problem of dynamic surface classification is embedded in the SPIN project, where sub-symbolic abstractions, based on a 3-d scanned environment is being done.
The background of this paper is the area of case-based reasoning. This is a reasoning technique where one tries to use the solution of some problem which has been solved earlier in order to obta in a solution of a given problem. As example of types of problems where this kind of reasoning occurs very often is the diagnosis of diseases or faults in technical systems. In abstract terms this reduces to a classification task. A difficulty arises when one has not just one solved problem but when there are very many. These are called "cases" and they are stored in the case-base. Then one has to select an appropriate case which means to find one which is "similar" to the actual problem. The notion of similarity has raised much interest in this context. We will first introduce a mathematical framework and define some basic concepts. Then we will study some abstract phenomena in this area and finally present some methods developed and realized in a system at the University of Kaiserslautern.
We provide an overview of UNICOM, an inductive theorem prover for equational logic which isbased on refined rewriting and completion techniques. The architecture of the system as well as itsfunctionality are described. Moreover, an insight into the most important aspects of the internalproof process is provided. This knowledge about how the central inductive proof componentof the system essentially works is crucial for human users who want to solve non-trivial prooftasks with UNICOM and thoroughly analyse potential failures. The presentation is focussedon practical aspects of understanding and using UNICOM. A brief but complete description ofthe command interface, an installation guide, an example session, a detailed extended exampleillustrating various special features and a collection of successfully handled examples are alsoincluded.
While most approaches to similarity assessment are oblivious of knowledge and goals, there is ample evidence that these elements of problem solving play an important role in similarity judgements. This paper is concerned with an approach for integrating assessment of similarity into a framework of problem solving that embodies central notions of problem solving like goals, knowledge and learning.
To prove difficult theorems in a mathematical field requires substantial know-ledge of that field. In this thesis a frame-based knowledge representation formal-ism including higher-order sorted logic is presented, which supports a conceptualrepresentation and to a large extent guarantees the consistency of the built-upknowledge bases. In order to operationalize this knowledge, for instance, in anautomated theorem proving system, a class of sound morphisms from higher-orderinto first-order logic is given, in addition a sound and complete translation ispresented. The translations are bijective and hence compatible with a later proofpresentation.In order to prove certain theorems the comprehension axioms are necessary,(but difficult to handle in an automated system); such theorems are called trulyhigher-order. Many apparently higher-order theorems (i.e. theorems that arestated in higher-order syntax) however are essentially first-order in the sense thatthey can be proved without the comprehension axioms: for proving these theoremsthe translation technique as presented in this thesis is well-suited.