35 research outputs found
Discovering Knowledge from Local Patterns with Global Constraints
It is well known that local patterns are at the core of a lot of
knowledge which may be discovered from data. Nevertheless, use of local
patterns is limited by
their huge number and computational costs. Several approaches (e.g.,
condensed representations, pattern set discovery) aim at grouping or
synthesizing local patterns to provide a global view of the data. A
global pattern is a pattern which is a set or a synthesis of local
patterns coming from the data. In this paper, we propose the idea of
global constraints to write queries addressing global patterns. A key
point is the ability to bias the designing of global patterns according
to the expectation of the user. For instance, a global pattern can be
oriented towards the search of exceptions or a clustering. It requires
to write queries taking into account such biases. Open issues are to
design a generic framework to express powerful global constraints and
solvers to mine them. We think that global constraints are a promising
way to discover relevant global patterns
Projection based ensemble learning for ordinal regression
The classification of patterns into naturally ordered
labels is referred to as ordinal regression. This paper proposes
an ensemble methodology specifically adapted to this type of
problems, which is based on computing different classification
tasks through the formulation of different order hypotheses.
Every single model is trained in order to distinguish between
one given class (k) and all the remaining ones, but grouping
them in those classes with a rank lower than k, and those
with a rank higher than k. Therefore, it can be considered as
a reformulation of the well-known one-versus-all scheme. The
base algorithm for the ensemble could be any threshold (or
even probabilistic) method, such as the ones selected in this
paper: kernel discriminant analysis, support vector machines
and logistic regression (all reformulated to deal with ordinal
regression problems). The method is seen to be competitive when
compared with other state-of-the-art methodologies (both ordinal
and nominal), by using six measures and a total of fifteen ordinal
datasets. Furthermore, an additional set of experiments is used to
study the potential scalability and interpretability of the proposed
method when using logistic regression as base methodology for
the ensemble
Decision rules construction : algorithm based on EAV model
In the paper, an approach for decision rules construction is proposed. It is studied from
the point of view of the supervised machine learning task, i.e., classification, and from the point
of view of knowledge representation. Generated rules provide comparable classification results
to the dynamic programming approach for optimization of decision rules relative to length or
support. However, the proposed algorithm is based on transformation of decision table into entity–
attribute–value (EAV) format. Additionally, standard deviation function for computation of averages’
values of attributes in particular decision classes was introduced. It allows to select from the whole
set of attributes only these which provide the highest degree of information about the decision.
Construction of decision rules is performed based on idea of partitioning of a decision table into
corresponding subtables. In opposite to dynamic programming approach, not all attributes need
to be taken into account but only these with the highest values of standard deviation per decision
classes. Consequently, the proposed solution is more time efficient because of lower computational
complexity. In the framework of experimental results, support and length of decision rules were
computed and compared with the values of optimal rules. The classification error for data sets
from UCI Machine Learning Repository was also obtained and compared with the ones for dynamic
programming approach. Performed experiments show that constructed rules are not far from the
optimal ones and classification results are comparable to these obtained in the framework of the
dynamic programming extension
Applications of MRI Magnetic Susceptibility Mapping in PET-MRI Brain Studies
Magnetic susceptibility mapping (SM) uses magnetic resonance imaging (MRI) phase images to produce maps of the magnetic susceptibility (χ) of tissues. This work focuses on the applications of SM-based imaging to PET-MRI, the hybrid imaging modality which combines positron emission tomography (PET) with MRI. First, the potential of using SM to aid PET attenuation correction (AC) is explored. AC for PET-MRI is challenging as PET-MRI provides no information regarding the electron density of tissues. Recently proposed SM methods for calculating the χ in regions of no MRI signal are used to segment air, bone and soft tissue in order to create AC maps. In the head, SM methods are found to produce inferior air/bone segmentations to high-performing AC methods, but result in more accurate AC than ultrashort-echo (UTE)-based air/bone segmentations, and may be able to provide additional information in subjects with atypical anatomy. Secondly, a SM pipeline for inclusion in a PET-MRI study into biomarkers for Alzheimer’s disease (AD) is developed. In the Insight46 study 500 healthy subjects from the 1946 MRC National Survey of Health and Development are undergoing a comprehensive PET-MRI protocol at two time-points. SM processing methods are compared and optimised, and a method for processing images with oblique imaging planes is developed. The effect of using different tools for automated segmentation of regions of interest (ROIs) on reported regional χ values is analysed. The ROIs resulting from different tools are found to result in large differences in χ values. FIRST is chosen as the most appropriate ROI segmentation tool for this study based on anatomical accuracy as assessed by a neuroradiologist. Initial analysis of χ values from 100 subjects using data from the first time-point is carried out. No significant association with regional χ values is found for amyloid status, PET radiotracer uptake, or APOE genotype
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
Object-orientated planning domain engineering
The development of domain independent planners focuses on the creation of generic problem solvers. These solvers are designed to solve problems that are declaratively described to them. In order to solve arbitrary problems, the planner must possess efficient and effective algorithms; however, an often overlooked requirement is the need for a complete and correct description of the problem domain. Currently, the most common domain description language is a prepositional logic, state-based language called STRIPS. This thesis develops a new object-orientated domain description language that addresses some of the common errors made in writing STRIPS domains. This new language also features powerful semantics that are shown to gready ease the description of certain domain features. A common criticism of domain independent planning is that the requirement of being domain independent necessarily precludes the exploitation of domain specific knowledge that would increase efficiency. One technique used to address this is to recognise patterns of behaviour in domains and abstract them out into a higher-level representations that are exploitable. These higher-level representations are called generic types. This thesis investigates the ways in which generic types can be used to assist the domain engineering process. A language is developed for describing the behavioural patterns of generic types and the ways in which they can be exploited. This opens a domain independent channel for domain specific knowledge to pass from the domain engineer to the planner
Where 'Ignoring Delete Lists' Works: Local Search Topology in Planning Benchmarks
Between 1998 and 2004, the planning community has seen vast progress in terms
of the sizes of benchmark examples that domain-independent planners can tackle
successfully. The key technique behind this progress is the use of heuristic
functions based on relaxing the planning task at hand, where the relaxation is
to assume that all delete lists are empty. The unprecedented success of such
methods, in many commonly used benchmark examples, calls for an understanding
of what classes of domains these methods are well suited for. In the
investigation at hand, we derive a formal background to such an understanding.
We perform a case study covering a range of 30 commonly used STRIPS and ADL
benchmark domains, including all examples used in the first four international
planning competitions. We *prove* connections between domain structure and
local search topology -- heuristic cost surface properties -- under an
idealized version of the heuristic functions used in modern planners. The
idealized heuristic function is called h^+, and differs from the practically
used functions in that it returns the length of an *optimal* relaxed plan,
which is NP-hard to compute. We identify several key characteristics of the
topology under h^+, concerning the existence/non-existence of unrecognized dead
ends, as well as the existence/non-existence of constant upper bounds on the
difficulty of escaping local minima and benches. These distinctions divide the
(set of all) planning domains into a taxonomy of classes of varying h^+
topology. As it turns out, many of the 30 investigated domains lie in classes
with a relatively easy topology. Most particularly, 12 of the domains lie in
classes where FFs search algorithm, provided with h^+, is a polynomial solving
mechanism. We also present results relating h^+ to its approximation as
implemented in FF. The behavior regarding dead ends is provably the same. We
summarize the results of an empirical investigation showing that, in many
domains, the topological qualities of h^+ are largely inherited by the
approximation. The overall investigation gives a rare example of a successful
analysis of the connections between typical-case problem structure, and search
performance. The theoretical investigation also gives hints on how the
topological phenomena might be automatically recognizable by domain analysis
techniques. We outline some preliminary steps we made into that direction
Model based test suite minimization using metaheuristics
Software testing is one of the most widely used methods for quality assurance and fault detection purposes. However, it is one of the most expensive, tedious and time consuming activities in software development life cycle. Code-based and specification-based testing has been going on for almost four decades. Model-based testing (MBT) is a relatively new approach to software testing where the software models as opposed to other artifacts (i.e. source code) are used as primary source of test cases. Models are simplified representation of a software system and are cheaper to execute than the original or deployed system. The main objective of the research presented in this thesis is the development of a framework for improving the efficiency and effectiveness of test suites generated from UML models. It focuses on three activities: transformation of Activity Diagram (AD) model into Colored Petri Net (CPN) model, generation and evaluation of AD based test suite and optimization of AD based test suite. Unified Modeling Language (UML) is a de facto standard for software system analysis and design. UML models can be categorized into structural and behavioral models. AD is a behavioral type of UML model and since major revision in UML version 2.x it has a new Petri Nets like semantics. It has wide application scope including embedded, workflow and web-service systems. For this reason this thesis concentrates on AD models. Informal semantics of UML generally and AD specially is a major challenge in the development of UML based verification and validation tools. One solution to this challenge is transforming a UML model into an executable formal model. In the thesis, a three step transformation methodology is proposed for resolving ambiguities in an AD model and then transforming it into a CPN representation which is a well known formal language with extensive tool support. Test case generation is one of the most critical and labor intensive activities in testing processes. The flow oriented semantic of AD suits modeling both sequential and concurrent systems. The thesis presented a novel technique to generate test cases from AD using a stochastic algorithm. In order to determine if the generated test suite is adequate, two test suite adequacy analysis techniques based on structural coverage and mutation have been proposed. In terms of structural coverage, two separate coverage criteria are also proposed to evaluate the adequacy of the test suite from both perspectives, sequential and concurrent. Mutation analysis is a fault-based technique to determine if the test suite is adequate for detecting particular types of faults. Four categories of mutation operators are defined to seed specific faults into the mutant model. Another focus of thesis is to improve the test suite efficiency without compromising its effectiveness. One way of achieving this is identifying and removing the redundant test cases. It has been shown that the test suite minimization by removing redundant test cases is a combinatorial optimization problem. An evolutionary computation based test suite minimization technique is developed to address the test suite minimization problem and its performance is empirically compared with other well known heuristic algorithms. Additionally, statistical analysis is performed to characterize the fitness landscape of test suite minimization problems. The proposed test suite minimization solution is extended to include multi-objective minimization. As the redundancy is contextual, different criteria and their combination can significantly change the solution test suite. Therefore, the last part of the thesis describes an investigation into multi-objective test suite minimization and optimization algorithms. The proposed framework is demonstrated and evaluated using prototype tools and case study models. Empirical results have shown that the techniques developed within the framework are effective in model based test suite generation and optimizatio
Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994
The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments