This paper introduces a geographic information system architecture based on ontologies. Ontology plays a central role in the definition of all aspects and components of an information system in the so-called ontology-driven information systems. The system presented here uses a container of interoperable geographic objects. The objects are extracted from multiple independent data sources and are derived from a strongly typed mapping of classes from multiple ontologies. This approach provides a great level of interoperability and allows partial integration of information when completeness is impossible.

Many "learning from experience" systems use information extracted from problem solving experiences to modify a performance element PE, forming a new element PE 0 that can solve these and similar problems more efficiently. However, as transformations that improve performance on one set of problems can degrade performance on other sets, the new PE 0 is not always better than the original PE; this depends on the distribution of problems. We therefore seek the performance element whose expected performance, over this distribution, is optimal. Unfortunately, the actual distribution, which is needed to determine which element is optimal, is usually not known. Moreover, the task of finding the optimal element, even knowing the distribution, is intractable for most interesting spaces of elements. This paper presents a method, palo, that side-steps these problems by using a set of samples to estimate the unknown distribution, and by using a set of transformations to hill-climb to a local o...

This paper explains the rule subsystem that is being implemented in the POSTGRES DBMS. It is novel in sev eral ways. First, it gives to users the capability of defining rules as well as data to a DBMS. Moreover, depending on the scope of each rule defined, optimization is handled differently. This leads to good performance both in the case that there are many rules each of small scope and a few rules each of large scope. In addition, rules provide either a forward chaining control flow or a backward chaining one, and the system will choose the control mechanism that optimizes performance in the cases where it is possible. Furthermore, priority rules can be defined, thereby allowing a user to specify rule systems that have conflicts. This use of exceptions seems necessary in many applications. Lastly, our rule system can provide database services such as views, protection, integrity constraints, and referential integrity simply by applying the rules system in particular ways. Consequently, no special purpose code need be included in POSTGRES to handle these tasks. 1.

An algorithm that learns from a set of examples should ideally be able to exploit the available resources of (a) abundant computing power and (b) domain-specific knowledge to improve its ability to generalize. Connectionist theory-refinement systems, which use background knowledge to select a neural network's topology and initial weights, have proven to be effective at exploiting domain-specific knowledge; however, most do not exploit available computing power. This weakness occurs because they lack the ability to refine the topology of the neural networks they produce, thereby limiting generalization, especially when given impoverished domain theories. We present the REGENT algorithm which uses (a) domain-specific knowledge to help create an initial population of knowledge-based neural networks and (b) genetic operators of crossover and mutation (specifically designed for knowledge-based networks) to continually search for better network topologies. Experiments on three real-world domains indicate that our new algorithm is able to significantly increase generalization compared to a standard connectionist theory-refinement system, as well as our previous algorithm for growing knowledge-based networks.

One of the most important numerical quantities that can be computed from a graph G is the two-variable Tutte polynomial. Specializations of the Tutte polynomial count various objects associated with G, e.g., subgraphs, spanning trees, acyclic orientations, inversions and parking functions. We show that by partitioning certain simplicial complexes related to G into intervals, one can provide combinatorial demonstrations of these results. One of the primary tools for providing such a partition is depth-first search.

This paper presents results from an experiment in reuse within the RE2 project. It shows how a particular candidature criterion for identifying abstract data types in txisting software systems can be applied both ar the theoretical and practical level. The RE2 project is concerned with the exploration of I everse engineering and re-engineering techniques to facilitate reuse re-engineering by the identification and I dassijication of appropriate candidature criteria. 1.

Many scientific and industrial problems can be better understood by learning from samples of the task at hand. For this reason, the machine learning and statistics communities devote considerable research effort on generating inductive-learning algorithms that try to learn the true "concept" of a task from a set of its examples. Often times, however, one has additional resources readily available, but largely unused, that can improve the concept that these learning algorithms generate. These resources include available computer cycles, as well as prior knowledge describing what is currently known about the domain. Effective utilization of available computer time is important since for most domains an expert is willing to wait for weeks, or even months, if a learning system can produce an improved concept. Using prior knowledge is important since it can contain information not present in the current set of training examples. In this thesis, I present three "anytime" approaches to connec...

Moreover, there are many cases where functional programs are more efficiently executed than their relational equivalents. 1 Introduction During the last years a lot of approaches have been proposed in order to amalgamate functional and logic programming languages [7] [1]. Such integrations have several advantages: 1. Functional and logic programming styles can be used in one language. 2. It extends logic programming by allowing nested expressions, i.e., it is not necessary

The NU-Prolog Debugging Environment (Nude) is a collection of integrated tools for locating bugs in both pure and non-logical NU-Prolog programs. It has static analyses and user-driven dynamic analyses including a four-port debugger and a declarative debugger. This document is a guide to using the environment. Contents 1 introduction 3 1.1 Nude at a glance : : : : : : : : : : : : : : : : : : : : : : : : : : : 3 1.2 limitations of Nude : : : : : : : : : : : : : : : : : : : : : : : : : 4 2 background on debugging 4 2.1 traditional Prolog debugging : : : : : : : : : : : : : : : : : : : : 4 2.2 declarative debugging : : : : : : : : : : : : : : : : : : : : : : : : 5 2.2.1 how does it work? : : : : : : : : : : : : : : : : : : : : : : 5 2.2.2 what problems are there? : : : : : : : : : : : : : : : : : : 5 3 components of Nude 6 3.1 static analyses : : : : : : : : : : : : : : : : : : : : : : : : : : : : 6 3.1.1 Nit : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 7 3.1.2 th...

Many learning systems search through a space of possible performance elements, seeking an element with high expected utility. As the task of finding the globally optimal element is usually intractable, many practical learning systems use hill-climbing to find a local optimum. Unfortunately, even this is difficult, as it depends on the distribution of problems, which is typically unknown. This paper addresses the task of approximating this hill-climbing search when the utility function can only be estimated by sampling. We present an algorithm that returns an element that is, with provably high probability, essentially a local optimum. We then demonstrate the generality of this algorithm by sketching three meaningful applications, that respectively find an element whose efficiency, accuracy or completeness is nearly optimal. These results suggest approaches to solving the utility problem from explanation-based learning, the multiple extension problem from nonmonotonic reasoning and the ...