Requests to: University of Dortmund

ftp: ftp-ai.informatik.uni-dortmund.de:pub/Report

Requests to: University of Dortmund

The implication problem is the problem of deciding whether a given set of dependencies entails other dependencies. Up to now, the entailment of excluded dependencies or independencies is only regarded on a metalogical level, which is not suitable for an automatic inference process. But, the inference of independencies is of great importance for new topics in database research like knowledge discovery in databases. In this paper, the expanded implication problem is discussed in order to decide entailment of dependencies and independencies. The main results are axiomatizations of functional, inclusion and multivalued independencies and the corresponding inference relations. Also, we discuss the use of independencies in knowledge discovery in databases and semantic query optimization.

Grdt: Enhancing Model-Based Learning for Its Application in Robot Navigation

One central point of machine learning in general and inductive logic programming in special is the search space of the algorithms, defined by the control structure of the algorithms and additional knowledge. Since the sensible search space differs from domain to domain, a flexible way to describe this space is desired. To demonstrate problems occuring while using existing algorithms, we introduce learning tasks in a real world domain: concept learning for navigation of autonomous mobile robots. We point out differences between three existing algorithms used within this framework and their results. Since all of these algorithms have problems in solving the tasks, we developed grdt (grammar based rule discovery tool), an algorithm combining their ideas and techniques. In grdt a two level description language is used for describing the hypothesis space. A grammar is used to define a set of second order rule schemata and these schemata then define the hypothesis space itself. 1 Introductio..

Probabilistic Reasoning with Maximum Entropy - The System PIT

We present a system for common sense reasoning based on propositional logic, the probability calculus and the concept of model-quantification. The task of this system PIT (for Probability Induction Tool) is to deliver decisions under incomplete knowledge but to keep the necessary additional assumptions as minimal as possible. Following this task it shows non-monotonic behavior in two ways: Non-monotonic decisions can be the result of reasoning in a single probability model (via conditionalization) or in a set of probability models (via additional principles of rational decisions, justified by model-quantification). As the concept of modelquantification delivers a precise semantics we know the corresponding decisions to make sense in many problems of common sense reasoning. We will show this with an example from default reasoning and an example of medical diagnosis. 2 Introduction Propositional logic is a well-researched area of science and allows the specification of many kinds of exa..

Turning an Action Formalism Into a Planner|

ftp: kimo.informatik.uni-dortmund.de:pub/Report

Discovery of Data Dependencies in Relational Databases

Knowledge discovery in databases is not only the nontrivial extraction of implicit, previously unknown and potentially useful information from databases. We argue that in contrast to machine learning, knowledge discovery in databases should be applied to real world databases. Since real world databases are known to be very large, they raise problems of the access. Therefore, real world databases only can be accessed by database management systems and the number of accesses has to be reduced to a minimum. Considering this property, we are forced to use, for example, standard set oriented interfaces of relational database management systems in order to apply methods of knowledge discovery in databases. We present a system for discovering data dependencies, which is build upon a set oriented interface. The point of main effort has been put on the discovery of value restrictions, unary inclusion- and functional dependencies in relational databases. The system also embodies an inference rel..