Understading Black Boxes: Knowledge Induction From Models

Due to regurations and laws prohibiting uses of private data on customers and their transactions in customer data base, most customer data sets are not easily accessable even in the same organizations. A solutio for this reguatory problems can be providing statistical summary of the data or models induced from the dat, instead of providing raw data sets. The models, however, have limited information on the original raw data set. This study explores possible solutions for these problems. The study uses prediction models from data on credit information of customers provided by a local bank in Seoul, S. Korea. This study suggests approaches in figuring what is inside of the non-rules based models such as regression models or neural network models. The study proposes several rule accumulation algorithms such as (RAA) and a GA-based rule refinement algorithm (GA-RRA) as possible solutions for the problems. The experiments show the performance of the random dataset, RAA, elimination of redundant rules (ERR), and GA-RRA

A Note on the Utility of Incremental Learning

... This paper defines the notion of incrementality for learning tasks and algorithms. It then provides some motivation for incremental learning and argues in favour of the design of incremental learning algorithms for solving incremental learning tasks. A number of issues raised by such systems are outlined and the incremental learner ILA is used for illustration

Informed selection and use of training examples for knowledge refinement.

Knowledge refinement tools seek to correct faulty rule-based systems by identifying and repairing faults indicated by training examples that provide evidence of faults. This thesis proposes mechanisms that improve the effectiveness and efficiency of refinement tools by the best use and selection of training examples. The refinement task is sufficiently complex that the space of possible refinements demands a heuristic search. Refinement tools typically use hill-climbing search to identify suitable repairs but run the risk of getting caught in local optima. A novel contribution of this thesis is solving the local optima problem by converting the hill-climbing search into a best-first search that can backtrack to previous refinement states. The thesis explores how different backtracking heuristics and training example ordering heuristics affect refinement effectiveness and efficiency. Refinement tools rely on a representative set of training examples to identify faults and influence repair choices. In real environments it is often difficult to obtain a large set of training examples, since each problem-solving task must be labelled with the expert's solution. Another novel aspect introduced in this thesis is informed selection of examples for knowledge refinement, where suitable examples are selected from a set of unlabelled examples, so that only the subset requires to be labelled. Conversely, if a large set of labelled examples is available, it still makes sense to have mechanisms that can select a representative set of examples beneficial for the refinement task, thereby avoiding unnecessary example processing costs. Finally, an experimental evaluation of example utilisation and selection strategies on two artificial domains and one real application are presented. Informed backtracking is able to effectively deal with local optima by moving search to more promising areas, while informed ordering of training examples reduces search effort by ensuring that more pressing faults are dealt with early on in the search. Additionally, example selection methods achieve similar refinement accuracy with significantly fewer examples

Seventh Annual Workshop on Space Operations Applications and Research (SOAR 1993), volume 1

This document contains papers presented at the Space Operations, Applications and Research Symposium (SOAR) Symposium hosted by NASA/Johnson Space Center (JSC) on August 3-5, 1993, and held at JSC Gilruth Recreation Center. SOAR included NASA and USAF programmatic overview, plenary session, panel discussions, panel sessions, and exhibits. It invited technical papers in support of U.S. Army, U.S. Navy, Department of Energy, NASA, and USAF programs in the following areas: robotics and telepresence, automation and intelligent systems, human factors, life support, and space maintenance and servicing. SOAR was concerned with Government-sponsored research and development relevant to aerospace operations. More than 100 technical papers, 17 exhibits, a plenary session, several panel discussions, and several keynote speeches were included in SOAR '93

A knowledge-based intelligent system for surface texture (virtual surf)

The presented thesis documents the investigation and development of the mathematical foundations for a novel knowledge-based system for surface texture (VitualSurf system). This is the first time that this type of novel knowledge-based system has been tried on surface texture knowledge. It is important to realize that surface texture knowledge, based on new generation Geometrical Product Specification (GPS) system, are considered to be too theoretical, abstract, complex and over-elaborate. Also it is not easy for industry to understand and implement them efficiently in a short time. The VirtualSurf has been developed to link surface function, specification through manufacture and verification, and provide a universal platform for engineers in industry, making it easier for them to understand and use the latest surface texture knowledge. The intelligent knowledge-base should be capable of incorporating knowledge from multiple sources (standards, books, experts, etc), adding new knowledge from these sources and still remain a coherent reliable system. In this research, an object-relationship data model is developed to represent surface texture knowledge. The object-relationship data model generalises the relational and object orientated data models. It has both the flexibility of structures for entities and also good mathematical foundations, based on category theory, that ensures the knowledge-base remains a coherent and reliable system as new knowledge is added. This prototype system leaves much potential for further work. Based on the framework and data models developed in this thesis, the system will be developed into implemental software, either acting as a good training tool for new and less experienced engineers or further connecting with other analysis software, CAD software (design), surface instrument software (measurement) etc, and finally applied in production industries.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Integration of Logic and Probability in Terminological and Inductive Reasoning

This thesis deals with Statistical Relational Learning (SRL), a research area combining principles and ideas from three important subfields of Artificial Intelligence: machine learn- ing, knowledge representation and reasoning on uncertainty. Machine learning is the study of systems that improve their behavior over time with experience; the learning process typi- cally involves a search through various generalizations of the examples, in order to discover regularities or classification rules. A wide variety of machine learning techniques have been developed in the past fifty years, most of which used propositional logic as a (limited) represen- tation language. Recently, more expressive knowledge representations have been considered, to cope with a variable number of entities as well as the relationships that hold amongst them. These representations are mostly based on logic that, however, has limitations when reason- ing on uncertain domains. These limitations have been lifted allowing a multitude of different formalisms combining probabilistic reasoning with logics, databases or logic programming, where probability theory provides a formal basis for reasoning on uncertainty. In this thesis we consider in particular the proposals for integrating probability in Logic Programming, since the resulting probabilistic logic programming languages present very in- teresting computational properties. In Probabilistic Logic Programming, the so-called "dis- tribution semantics" has gained a wide popularity. This semantics was introduced for the PRISM language (1995) but is shared by many other languages: Independent Choice Logic, Stochastic Logic Programs, CP-logic, ProbLog and Logic Programs with Annotated Disjunc- tions (LPADs). A program in one of these languages defines a probability distribution over normal logic programs called worlds. This distribution is then extended to queries and the probability of a query is obtained by marginalizing the joint distribution of the query and the programs. The languages following the distribution semantics differ in the way they define the distribution over logic programs. The first part of this dissertation presents techniques for learning probabilistic logic pro- grams under the distribution semantics. Two problems are considered: parameter learning and structure learning, that is, the problems of inferring values for the parameters or both the structure and the parameters of the program from data. This work contributes an algorithm for parameter learning, EMBLEM, and two algorithms for structure learning (SLIPCASE and SLIPCOVER) of probabilistic logic programs (in particular LPADs). EMBLEM is based on the Expectation Maximization approach and computes the expectations directly on the Binary De- cision Diagrams that are built for inference. SLIPCASE performs a beam search in the space of LPADs while SLIPCOVER performs a beam search in the space of probabilistic clauses and a greedy search in the space of LPADs, improving SLIPCASE performance. All learning approaches have been evaluated in several relational real-world domains. The second part of the thesis concerns the field of Probabilistic Description Logics, where we consider a logical framework suitable for the Semantic Web. Description Logics (DL) are a family of formalisms for representing knowledge. Research in the field of knowledge repre- sentation and reasoning is usually focused on methods for providing high-level descriptions of the world that can be effectively used to build intelligent applications. Description Logics have been especially effective as the representation language for for- mal ontologies. Ontologies model a domain with the definition of concepts and their properties and relations. Ontologies are the structural frameworks for organizing information and are used in artificial intelligence, the Semantic Web, systems engineering, software engineering, biomedical informatics, etc. They should also allow to ask questions about the concepts and in- stances described, through inference procedures. Recently, the issue of representing uncertain information in these domains has led to probabilistic extensions of DLs. The contribution of this dissertation is twofold: (1) a new semantics for the Description Logic SHOIN(D) , based on the distribution semantics for probabilistic logic programs, which embeds probability; (2) a probabilistic reasoner for computing the probability of queries from uncertain knowledge bases following this semantics. The explanations of queries are encoded in Binary Decision Diagrams, with the same technique employed in the learning systems de- veloped for LPADs. This approach has been evaluated on a real-world probabilistic ontology