30 research outputs found
Greedy Algorithm for Inference of Decision Trees from Decision Rule Systems
Decision trees and decision rule systems play important roles as classifiers,
knowledge representation tools, and algorithms. They are easily interpretable
models for data analysis, making them widely used and studied in computer
science. Understanding the relationships between these two models is an
important task in this field. There are well-known methods for converting
decision trees into systems of decision rules. In this paper, we consider the
inverse transformation problem, which is not so simple. Instead of constructing
an entire decision tree, our study focuses on a greedy polynomial time
algorithm that simulates the operation of a decision tree on a given tuple of
attribute values.Comment: arXiv admin note: substantial text overlap with arXiv:2305.01721,
arXiv:2302.0706
Representing and Reasoning on Conceptual Queries Over Image Databases
The problem of content management of multimedia data types (e.g., image, video, graphics) is becoming increasingly important with the development of advanced multimedia applications. Traditional database management systems are inadequate for the handling of such data types. They require new techniques for query formulation, retrieval, evaluation, and navigation. In this paper we develop a knowledge-based framework for modeling and retrieving image data by content. To represent the various aspects of an image object's characteristics, we propose a model which consists of three layers:
(1) Feature and Content Layer, intended to contain image visual features such as contours, shapes,etc.; (2) Object Layer, which provides the (conceptual) content dimension of images; and (3) Schema Layer, which contains the structured abstractions of images, i.e., a general schema about the classes of objects represented in the object layer. We propose two abstract languages on the basis of description logics: one for describing knowledge of the object and schema layers, and the other, more expressive, for making queries. Queries can refer to the form dimension (i.e., information of the Feature and Content Layer) or to the content dimension (i.e., information of the Object Layer). These languages employ a variable free notation, and they are well suited for the design, verification and complexity analysis of algorithms. As the amount of information contained in the previous layers may be huge and operations performed at the Feature and Content Layer are time-consuming, resorting to the use of materialized views to process and optimize queries may be extremely useful. For that, we propose a formal framework for testing containment of a query in a view expressed in our query language. The
algorithm we propose is sound and complete and relatively efficient.This is an extended version of the article in: Eleventh International Symposium on Methodologies for Intelligent Systems, Warsaw, Poland, 1999
Bounds on Depth of Decision Trees Derived from Decision Rule Systems
Systems of decision rules and decision trees are widely used as a means for
knowledge representation, as classifiers, and as algorithms. They are among the
most interpretable models for classifying and representing knowledge. The study
of relationships between these two models is an important task of computer
science. It is easy to transform a decision tree into a decision rule system.
The inverse transformation is a more difficult task. In this paper, we study
unimprovable upper and lower bounds on the minimum depth of decision trees
derived from decision rule systems depending on the various parameters of these
systems
Test: Internet Indexing Systems vs List of Known URLs: Revisited
This is a compilation of the tests done in Sept./Oct. 1997 by the author on the hen existing search engines. It was published on the web on the authors home page . As the web pages changed, this was pushed of into the old site and forgotten.
The original HTML pages were converted into PDF using LibreOffice in Aug 2022 and is placed in the Spectrum repository for the record
Learning Logic Programs by Discovering Higher-Order Abstractions
Discovering novel abstractions is important for human-level AI. We introduce
an approach to discover higher-order abstractions, such as map, filter, and
fold. We focus on inductive logic programming, which induces logic programs
from examples and background knowledge. We introduce the higher-order
refactoring problem, where the goal is to compress a logic program by
introducing higher-order abstractions. We implement our approach in STEVIE,
which formulates the higher-order refactoring problem as a constraint
optimisation problem. Our experimental results on multiple domains, including
program synthesis and visual reasoning, show that, compared to no refactoring,
STEVIE can improve predictive accuracies by 27% and reduce learning times by
47%. We also show that STEVIE can discover abstractions that transfer to
different domain
LOGIC AND CONSTRAINT PROGRAMMING FOR COMPUTATIONAL SUSTAINABILITY
Computational Sustainability is an interdisciplinary field that aims to develop computational
and mathematical models and methods for decision making concerning
the management and allocation of resources in order to help solve environmental
problems.
This thesis deals with a broad spectrum of such problems (energy efficiency, water
management, limiting greenhouse gas emissions and fuel consumption) giving
a contribution towards their solution by means of Logic Programming (LP) and
Constraint Programming (CP), declarative paradigms from Artificial Intelligence
of proven solidity.
The problems described in this thesis were proposed by experts of the respective
domains and tested on the real data instances they provided. The results are encouraging
and show the aptness of the chosen methodologies and approaches.
The overall aim of this work is twofold: both to address real world problems
in order to achieve practical results and to get, from the application of LP and
CP technologies to complex scenarios, feedback and directions useful for their
improvement