Thesis (M.S.)--Missouri University of Science and Technology, 2008. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed June 18, 2009) Includes bibliographical references (p. 62-64).

Thesis (PgDipEng(Res)--Engineering (Research)) -- Auckland University of Technology, 2009. / Also held in print (xiii, 113 leaves, ill., 30 cm.) in the Archive at the City Campus (T 006.32 BED)

Thesis (Ph.D.)--University of Louisville, 2007. / Adviser: Adel S. Elmaghraby. Includes bibliographical references.

Thesis (M.S.)--University of Texas at El Paso, 2009. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Thesis (Ph.D.) - Carleton University, 2007. / Includes bibliographical references (p. 261-266). Also available in electronic format on the Internet.

While Artificial Intelligence (AI) is making giant steps, it is also raising concerns about its trustworthiness, due to the fact that widely-used black-box models cannot be exactly understood by humans. One of the ways to improve humans’ trust towards AI is to use interpretable AI models, i.e., models that can be thoroughly understood by humans, and thus trusted. However, interpretable AI models are not typically used in practice, as they are thought to be less performing than black-box models. This is more evident in Reinforce- ment Learning, where relatively little work addresses the problem of performing Reinforce- ment Learning with interpretable models. In this thesis, we address this gap, proposing methods for Interpretable Reinforcement Learning. For this purpose, we optimize Decision Trees by combining Reinforcement Learning with Evolutionary Computation techniques, which allows us to overcome some of the challenges tied to optimizing Decision Trees in Reinforcement Learning scenarios. The experimental results show that these approaches are competitive with the state-of-the-art score while being extremely easier to interpret. Finally, we show the practical importance of Interpretable AI by digging into the inner working of the solutions obtained.

No description available.

In many everyday tasks it is necessary to compare the performance of the individuals in a population described by two or more criteria, for example comparing products in order to decide which is the best to purchase in terms of price and quality. Other examples are the comparison of universities, countries, the infrastructure in a telecommunications network, and the candidate solutions to a multi- or many-objective problem. In all of these cases, visualising the individuals better allows a decision maker to interpret their relative performance. This thesis explores methods for understanding and visualising multi- and many-criterion populations. Since people cannot generally comprehend more than three spatial dimensions the visualisation of many-criterion populations is a non-trivial task. We address this by generating visualisations based on the dominance relation which defines a structure in the population and we introduce two novel visualisation methods. The first method explicitly illustrates the dominance relationships between individuals as a graph in which individuals are sorted into Pareto shells, and is enhanced using many-criterion ranking methods to produce a finer ordering of individuals. We extend the power index, a method for ranking according to a single criterion, into the many-criterion domain by defining individual quality in terms of tournaments. The second visualisation method uses a new dominance-based distance in conjunction with multi-dimensional scaling, and we show that dominance can be used to identify an intuitive low-dimensional mapping of individuals, placing similar individuals close together. We demonstrate that this method can visualise a population comprising a large number of criteria. Heatmaps are another common method for presenting high-dimensional data, however they suffer from a drawback of being difficult to interpret if dissimilar individuals are placed close to each other. We apply spectral seriation to produce an ordering of individuals and criteria by which the heatmap is arranged, placing similar individuals and criteria close together. A basic version, computing similarity with the Euclidean distance, is demonstrated, before rank-based alternatives are investigated. The procedure is extended to seriate both the parameter and objective spaces of a multi-objective population in two stages. Since this process describes a trade-off, favouring the ordering of individuals in one space or the other, we demonstrate methods that enhance the visualisation by using an evolutionary optimiser to tune the orderings. One way of revealing the structure of a population is by highlighting which individuals are extreme. To this end, we provide three definitions of the “edge” of a multi-criterion mutually non-dominating population. All three of the definitions are in terms of dominance, and we show that one of them can be extended to cope with many-criterion populations. Because they can be difficult to visualise, it is often difficult for a decision maker to comprehend a population consisting of a large number of criteria. We therefore consider criterion selection methods to reduce the dimensionality with a view to preserving the structure of the population as quantified by its rank order. We investigate the efficacy of greedy, hill-climber and evolutionary algorithms and cast the dimension reduction as a multi-objective problem.

This thesis studies rhythm from an evolutionary computation perspective. Rhythm is the most fundamental dimension of music and can be used as a ground to describe the evolution of music. More specifically, the main goal of the thesis is to investigate how complex rhythmic structures evolve, subject to the cultural transmission between individuals in a society. The study is developed by means of computer modelling and simulations informed by evolutionary computation and artificial life (A-Life). In this process, self-organisation plays a fundamental role. The evolutionary process is steered by the evaluation of rhythmic complexity and by the exposure to rhythmic material. In this thesis, composers and musicologists will find the description of a system named A-Rhythm, which explores the emerged behaviours in a community of artificial autonomous agents that interact in a virtual environment. The interaction between the agents takes the form of imitation games. A set of necessary criteria was established for the construction of a compositional system in which cultural transmission is observed. These criteria allowed the comparison with related work in the field of evolutionary computation and music. In the development of the system, rhythmic representation is discussed. The proposed representation enabled the development of complexity and similarity based measures, and the recombination of rhythms in a creative manner. A-Rhythm produced results in the form of simulation data which were evaluated in terms of the coherence of repertoires of the agents. The data shows how rhythmic sequences are changed and sustained in the population, displaying synchronic and diachronic diversity. Finally, this tool was used as a generative mechanism for composition and several examples are presented.

by Ngan Po Shun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 109-115). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Data Mining --- p.1 / Chapter 1.2 --- Motivation --- p.4 / Chapter 1.3 --- Contributions of the research --- p.5 / Chapter 1.4 --- Organization of the thesis --- p.6 / Chapter 2 --- Related Work in Data Mining --- p.9 / Chapter 2.1 --- Decision Tree Approach --- p.9 / Chapter 2.1.1 --- ID3 --- p.10 / Chapter 2.1.2 --- C4.5 --- p.11 / Chapter 2.2 --- Classification Rule Learning --- p.13 / Chapter 2.2.1 --- AQ algorithm --- p.13 / Chapter 2.2.2 --- CN2 --- p.14 / Chapter 2.2.3 --- C4.5RULES --- p.16 / Chapter 2.3 --- Association Rule Mining --- p.16 / Chapter 2.3.1 --- Apriori --- p.17 / Chapter 2.3.2 --- Quantitative Association Rule Mining --- p.18 / Chapter 2.4 --- Statistical Approach --- p.19 / Chapter 2.4.1 --- Chi Square Test and Bayesian Classifier --- p.19 / Chapter 2.4.2 --- FORTY-NINER --- p.21 / Chapter 2.4.3 --- EXPLORA --- p.22 / Chapter 2.5 --- Bayesian Network Learning --- p.23 / Chapter 2.5.1 --- Learning Bayesian Networks using the Minimum Descrip- tion Length (MDL) Principle --- p.24 / Chapter 2.5.2 --- Discretizating Continuous Attributes while Learning Bayesian Networks --- p.26 / Chapter 3 --- Overview of Evolutionary Computation --- p.29 / Chapter 3.1 --- Evolutionary Computation --- p.29 / Chapter 3.1.1 --- Genetic Algorithm --- p.30 / Chapter 3.1.2 --- Genetic Programming --- p.32 / Chapter 3.1.3 --- Evolutionary Programming --- p.34 / Chapter 3.1.4 --- Evolution Strategy --- p.37 / Chapter 3.1.5 --- Selection Methods --- p.38 / Chapter 3.2 --- Generic Genetic Programming --- p.39 / Chapter 3.3 --- Data mining using Evolutionary Computation --- p.43 / Chapter 4 --- Applying Generic Genetic Programming for Rule Learning --- p.45 / Chapter 4.1 --- Grammar --- p.46 / Chapter 4.2 --- Population Creation --- p.49 / Chapter 4.3 --- Genetic Operators --- p.50 / Chapter 4.4 --- Evaluation of Rules --- p.52 / Chapter 5 --- Learning Multiple Rules from Data --- p.56 / Chapter 5.1 --- Previous approaches --- p.57 / Chapter 5.1.1 --- Preselection --- p.57 / Chapter 5.1.2 --- Crowding --- p.57 / Chapter 5.1.3 --- Deterministic Crowding --- p.58 / Chapter 5.1.4 --- Fitness sharing --- p.58 / Chapter 5.2 --- Token Competition --- p.59 / Chapter 5.3 --- The Complete Rule Learning Approach --- p.61 / Chapter 5.4 --- Experiments with Machine Learning Databases --- p.64 / Chapter 5.4.1 --- Experimental results on the Iris Plant Database --- p.65 / Chapter 5.4.2 --- Experimental results on the Monk Database --- p.67 / Chapter 6 --- Bayesian Network Learning --- p.72 / Chapter 6.1 --- The MDLEP Learning Approach --- p.73 / Chapter 6.2 --- Learning of Discretization Policy by Genetic Algorithm --- p.74 / Chapter 6.2.1 --- Individual Representation --- p.76 / Chapter 6.2.2 --- Genetic Operators --- p.78 / Chapter 6.3 --- Experimental Results --- p.79 / Chapter 6.3.1 --- Experiment 1 --- p.80 / Chapter 6.3.2 --- Experiment 2 --- p.82 / Chapter 6.3.3 --- Experiment 3 --- p.83 / Chapter 6.3.4 --- Comparison between the GA approach and the greedy ap- proach --- p.91 / Chapter 7 --- Medical Data Mining System --- p.93 / Chapter 7.1 --- A Case Study on the Fracture Database --- p.95 / Chapter 7.1.1 --- Results of Causality and Structure Analysis --- p.95 / Chapter 7.1.2 --- Results of Rule Learning --- p.97 / Chapter 7.2 --- A Case Study on the Scoliosis Database --- p.100 / Chapter 7.2.1 --- Results of Causality and Structure Analysis --- p.100 / Chapter 7.2.2 --- Results of Rule Learning --- p.102 / Chapter 8 --- Conclusion and Future Work --- p.106 / Bibliography --- p.109 / Chapter A --- The Rule Sets Discovered --- p.116 / Chapter A.1 --- The Best Rule Set Learned from the Iris Database --- p.116 / Chapter A.2 --- The Best Rule Set Learned from the Monk Database --- p.116 / Chapter A.2.1 --- Monkl --- p.116 / Chapter A.2.2 --- Monk2 --- p.117 / Chapter A.2.3 --- Monk3 --- p.119 / Chapter A.3 --- The Best Rule Set Learned from the Fracture Database --- p.120 / Chapter A.3.1 --- Type I Rules: About Diagnosis --- p.120 / Chapter A.3.2 --- Type II Rules : About Operation/Surgeon --- p.120 / Chapter A.3.3 --- Type III Rules : About Stay --- p.122 / Chapter A.4 --- The Best Rule Set Learned from the Scoliosis Database --- p.123 / Chapter A.4.1 --- Rules for Classification --- p.123 / Chapter A.4.2 --- Rules for Treatment --- p.126 / Chapter B --- The Grammar used for the fracture and Scoliosis databases --- p.128 / Chapter B.1 --- The grammar for the fracture database --- p.128 / Chapter B.2 --- The grammar for the Scoliosis database --- p.128