184 research outputs found
Efficient Learning and Evaluation of Complex Concepts in Inductive Logic Programming
Inductive Logic Programming (ILP) is a subfield of Machine Learning with foundations in logic
programming. In ILP, logic programming, a subset of first-order logic, is used as a uniform
representation language for the problem specification and induced theories. ILP has been
successfully applied to many real-world problems, especially in the biological domain (e.g. drug
design, protein structure prediction), where relational information is of particular importance.
The expressiveness of logic programs grants flexibility in specifying the learning task and understandability
to the induced theories. However, this flexibility comes at a high computational
cost, constraining the applicability of ILP systems. Constructing and evaluating complex concepts
remain two of the main issues that prevent ILP systems from tackling many learning
problems. These learning problems are interesting both from a research perspective, as they
raise the standards for ILP systems, and from an application perspective, where these target
concepts naturally occur in many real-world applications. Such complex concepts cannot
be constructed or evaluated by parallelizing existing top-down ILP systems or improving the
underlying Prolog engine. Novel search strategies and cover algorithms are needed.
The main focus of this thesis is on how to efficiently construct and evaluate complex hypotheses
in an ILP setting. In order to construct such hypotheses we investigate two approaches.
The first, the Top Directed Hypothesis Derivation framework, implemented in the ILP system
TopLog, involves the use of a top theory to constrain the hypothesis space. In the second approach
we revisit the bottom-up search strategy of Golem, lifting its restriction on determinate
clauses which had rendered Golem inapplicable to many key areas. These developments led to
the bottom-up ILP system ProGolem. A challenge that arises with a bottom-up approach is the
coverage computation of long, non-determinate, clauses. Prologâs SLD-resolution is no longer
adequate. We developed a new, Prolog-based, theta-subsumption engine which is significantly
more efficient than SLD-resolution in computing the coverage of such complex clauses.
We provide evidence that ProGolem achieves the goal of learning complex concepts by presenting
a protein-hexose binding prediction application. The theory ProGolem induced has
a statistically significant better predictive accuracy than that of other learners. More importantly,
the biological insights ProGolemâs theory provided were judged by domain experts to
be relevant and, in some cases, novel
Apprentissage relationnel polynomial pour la classification d'arbres
National audienceAprÚs avoir rappelé le cadre général de la programmation logique inductive, nous proposons une sous-famille des clauses de Horn nommée MQD. Visant des applications de classification de document XML, nous définissons un langage de clauses permettant de représenter des arbres et des motifs d'arbres. Ce langage nous fournit exemples et hypothÚses. Nous montrons que ce langage est inclus dans les MQD et proposons des algorithmes dédiés pour les opérations de base nécessaires à l'apprentissage, à savoir les calculs de theta-subsomption et de moindre généralisé. Nos algorithmes étant polynomiaux et non exponentiels comme dans le cas général des clauses de Horn, ils peuvent participer à la classification supervisée d'arbres, l'apprentissage relationnel devenant alors de complexité polynomiale
On the Role of Assertions for Conceptual Modeling as Enablers of Composable Simulation Solutions
This research provides a much needed systematic review of the roles that assertions play in model composability and simulation interoperability. In doing so, this research contributes a partial solution to one of the problems of model composability and simulation interoperabilityânamely, why do simulation systems fail to achieve the maximum level of interoperability possible? It demonstrates the importance of the assertions that are made during model development and simulation implementation, particularly as they reflect the unique viewpoint of each developer or user. It hypothesizes that it is possible to detect composability conflicts by means of a four-step process developed by the author for capturing and comparing assertions. It demonstrates the process using a well understood example problemâthe Falling Body Problemâdeveloping a formal model of assertion, a strategy for assertion comparison, an inventory of forces, and a catalog of significant assertions that might be made for each term in the solution to the problem. Finally, it develops a software application to implement the strategy for comparing sets of assertions. The software successfully detects potential conflicts between ontologies that were otherwise determined to be ontologically consistent, thus proving the hypothesis
Perspectives on the Neuroscience of Cognition and Consciousness
The origin and current use of the concepts of computation, representation and information in Neuroscience are examined and conceptual flaws are identified which vitiate their usefulness for addressing problems of the neural basis of Cognition and Consciousness. In contrast, a convergence of views is presented to support the characterization of the Nervous System as a complex dynamical system operating in the metastable regime, and capable of evolving to configurations and transitions in phase space with potential relevance for Cognition and Consciousness
Inductive queries for a drug designing robot scientist
It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments
A hybrid approach to simultaneous localization and mapping in indoors environment
This thesis will present SLAM in the current literature to benefit from then it will present the investigation results for a hybrid approach used where different algorithms using laser, sonar, and camera sensors were tested and compared. The contribution of this thesis is the development of a hybrid approach for SLAM that uses different sensors and where different factors are taken into consideration such as dynamic objects, and the development of a scalable grid map model with new sensors models for real time update of the map.The thesis will show the success found, difficulties faced and limitations of the algorithms developed which were simulated and experimentally tested in an indoors environment
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