We are free, from the shackles of the finite, and of the physical world. Sound now enjoys morphological freedom through a myriad of transformations. It is malleable to the utmost degree. We have at our disposal an astounding plethora of tools, with which we can manipulate and organise sound. This thesis project is a collection of musical materials that explore the idea of The Uncanny Valley, as it relates to music being real, fake or some strange combination of the two. This thesis project is primarily one in which I produce sound files. In a secondary capacity, I’m also producing a text file. In this text I aim to present some of my thoughts on how my work writing code and making music might be connected, in some hopefully interesting ways, to my field. I’m unlikely to be able to adequately convey my own origin myth. Instead I’ll focus on stories I’ve been told about music, throughout my life, inside and outside of academia. I have a strong suspicion that these stories have shaped my coming into being as a composer. However difficult the task of introspection, and ultimately to know one self proves to be, I at least regard these stories as a source for clues as to why I am driven to do the things that I do.

The game of Hex is a board game that belongs to the family of Shannon games, which are connection-oriented games where players must secure certain connected components in graphs. The problem of solving Hex is a decision problem complete in PSPACE, which implies that the problem is NP-Hard. Although the Hex problem is difficult to solve, there are a number of problem reduction methods that allow solutions to be found for small Hex boards within practical search limits. The present work addresses two problems, the problem of solving the game of Hex for small board sizes and the problem of creating strong artificial Hex players for larger boards. Recently, a leading Hex solving program has been shown to solve the 7x7 Hex board, but failed to solve 8x8 Hex within practical limits. This work investigates Hex-solving techniques and introduces a series of new search optimizations with the aim to develop a better Hex solver. The most significant of these new optimization techniques is a knowledge base approach that stores and reuses search information to prune Hex-solving searches. This technique involves a generalized form of transposition table that stores game features and uses such features to prove that certain board positions are winning. Experimental results demonstrate a knowledge base Hex solver that significantly speeds up the solving of 7x7 Hex. The search optimization techniques for Hex solvers given here are highly specialized. This work reports on a search algorithm for artificial Hex players, called Pattern Enhanced Alpha-Beta search that can utilize these optimization techniques. On large board positions, an artificial Hex player based on the Pattern Enhanced Alpha- Beta search can return moves in practical times if search depths are limited. Such a player can return a good move provided that the evaluated probabilities of winning on board positions at the depth cut-offs are accurate. Given a large database of Hex games, this work explores an apprenticeship learning approach that takes advantage of this database to derive board evaluation functions for strong Hex playing policies. This approach is compared against a temporal difference learning approach and local beam search approach. A contribution from this work is a method that can automatically generate good quality evaluation functions for Hex players.

Symphony No. 1 is an orchestral composition for twenty-four instrumental groups without percussion instruments. It was composed with Algorithmic Composition System software, which gives driving forces for composition to the composer through the diverse compositional methods largely based on physical phenomena. The symphony consists of three movements. It lasts about sixteen minutes and twenty-six seconds--five minutes and twenty-two seconds for the first movement, five minutes and forty seconds for the second movement, five minutes and twenty-four seconds for the third movement. Most musical components in the first movement of the symphony are considered embryos, which gradually begin developing through the second and third movements.

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Simulations are used in science and industry to predict the performance of technical systems. Adjoint derivatives of these simulations can reveal the sensitivity of the system performance to changes in design or operating conditions, and are increasingly used in shape optimisation and uncertainty quantification. Algorithmic differentiation (AD) by source-transformation is an efficient method to compute such derivatives. AD requires an analysis of the computation and its data flow to produce efficient adjoint code. One important step is the activity analysis that detects operations that need to be differentiated. An improved activity analysis is investigated in this thesis that simplifies build procedures for certain adjoint programs, and is demonstrated to improve the speed of an adjoint fluid dynamics solver. The method works by allowing a context-dependent analysis of routines. The ongoing trend towards multi- and many-core architectures such as the Intel XeonPhi is creating challenges for AD. Two novel approaches are presented that replicate the parallelisation of a program in its corresponding adjoint program. The first approach detects loops that naturally result in a parallelisable adjoint loop, while the second approach uses loop transformation and the aforementioned context-dependent analysis to enforce parallelisable data access in the adjoint loop. A case study shows that both approaches yield adjoints that are as scalable as their underlying primal programs. Adjoint computations are limited by their memory footprint, particularly in unsteady simulations, for which this work presents incomplete checkpointing as a method to reduce memory usage at the cost of a slight reduction in accuracy. Finally, convergence of iterative linear solvers is discussed, which is especially relevant on accelerator cards, where single precision floating point numbers are frequently used and the choice of solvers is limited by the small memory size. Some problems that are particular to adjoint computations are discussed.

One of the most fundamental and least understood elements of evolution is the mapping between genotype and phenotype. Recent work on genotype-phenotype (GP) maps suggests that these maps show properties which may have important evolutionary implications. These properties include a skewed distribution of genotypes over phenotypes, linear scaling between phenotype robustness and the logarithm of phenotype frequency, and a positive correlation between phenotype robustness and evolvability. However, most of these properties have only been studied for self-assembling systems, such as protein complexes or RNA folding. In this thesis, we ask ourselves if these properties are more general. First, we apply tools from algorithmic information theory to a wide class of inputoutput maps, of which GP maps are a subset. We find that these maps show a strong bias towards simple phenotypes, a pattern known as simplicity bias. We also define a matrix map of tunable complexity, with which we can study the conditions under which simplicity bias is present. Next, we investigate multiple models of GP maps for gene regulatory networks (GRNs). These include Boolean threshold networks, where we fix the strength of gene interactions, while varying the network topology, as well as systems of differential equations, where we fix the network topology while varying interaction strengths. For both modelling frameworks, the GRN GP maps exhibit all the structural properties found in the literature, as well as simplicity bias. We also find that the number of genotypes mapping to the wild-type phenotypes for various GRNs is unusually large, and argue that this is evidence that the structure of the GP map plays an important role in determining evolutionary outcomes. Finally, we return to more general input-output maps, and show that in addition to simplicity bias these maps also present randomness deficiency, that is, their output spectrum is less complex than expected. We argue that this additional property combines with simplicity bias in GP maps, and more generally, in input-output maps, and suggest a general trend towards simplicity in nature.

Seed-Turbulence-Iteration explores the aesthetic application of chaos and fractal geometry onto the musical parameters of a work constructed for chamber orchestra. Verhulst's Logistic Map and Devaney's Gingerbreadman Map are the dynamic systems from which melodic contour and temporal duration are derived. These algorithms are used to produce heterophonic and polyphonic results that iterate for a set duration before restarting. Each new beginning involves a change in density (of individual lines, as well as points of articulation in time), orchestration, register, and the pitch reservoir. All pitches are derived from a quantized spectrum that interpolates from a state of harmonicity to inharmonicity across a series of changing fundamentals. Each stage of interpolation coincides with the reseting of algorithmic iterations. Self-similarity and self-affinity are represented vertically, in the family resemblances of the lines produced within each algorithm that occur inside of a given segment, as well as horizontally, in the reiterations that occur over time. Each algorithmic reiteration and each copy within a set of iterations has varied starting or “seed” conditions that produce differentiated results of greater or lesser degrees which are presented in non-linear, strategic arrangement. Turbulence is implemented in the form of probabilistic distortions inserted into algorithmic processes that are meant to vary to some degree the amount of unpredictability of an output parameter (pitch or duration) as well as in intuitive manipulations of algorithmically generated material.

M.Sc. / Advancements in technology used in financial markets have led to substantial automation of tasks within the financial industry. Data analysis, trade execution and trade processing have been automated, reducing costs and increasing productivity. Algorithmic trading is the automated execution of trades on an electronic trading platform; it has been used to gain competitive advantage in financial markets since the early 1990s. Algorithmic trading applications, which must analyse information and determine whether to buy or sell, are well suited to the use of autonomous software agents. Multi-agent systems are better suited to the increasing complexity of algorithmic trading systems and the flexibility required by rapidly changing markets than single-agent systems. The granularity of components (agents) in multi-agent systems also promotes reuse and simplifies individual agent design. Algorithmic trading is, however, subject to challenges specifically in terms of data volume, speed of access and speed of processing. In order to utilise a multi-agent system solution the interactions between agents which allow distributed problem solving must be as efficient as possible. This dissertation investigates the use of indirect coordination to improve the efficiency of interactions between agents in multi-agent systems and to simplify agent design. Indirect coordination utilises environment abstractions known as artefacts to facilitate interaction between agents; such interaction can be simple data transfer or requests, complex coordination protocols as well as negotiation protocols. The investigation resulted in a framework that allows agents to transition between direct and indirect interaction techniques based on the specific interaction task at hand. The framework is built on two existing platforms, ii Java Agent DEvelopment Framework (JADE) and Common ARTifact Infrastructure for AGents Open environments (CARTAGO). These platforms are combined into the JADE-CARTAGO Algorithmic Trading (JCAT) framework that provides the infrastructure needed for both direct and indirect interactions. Investigations into the performance of the JCAT framework have shown that artefacts improve interaction efficiency by reducing data loss in tasks such as information publishing, and perform as well as direct communication within certain constraints for other tasks. When limiting the number of agents in an interaction to 50 agents, artefacts perform at least as well as direct communication using agent communication language messages.

Research shows that college students are largely unaware of the impact of algorithms on their everyday lives. Also, most university students are not being taught about algorithms as part of the regular curriculum. This exploratory, qualitative study aimed to explore subject-matter experts’ insights and perceptions of the knowledge components, coping behaviors, and pedagogical considerations to aid faculty in teaching algorithmic literacy to college students. Eleven individual, semi-structured interviews and one focus group were conducted with scholars and teachers of critical algorithm studies and related fields. Findings suggested three sets of knowledge components that would contribute to students’ algorithmic literacy: general characteristics and distinguishing traits of algorithms, key domains in everyday life using algorithms (including the potential benefits and risks), and ethical considerations for the use and application of algorithms. Findings also suggested five behaviors that students could use to help them better cope with algorithmic systems and nine teaching strategies to help improve students’ algorithmic literacy. Suggestions also surfaced for alternative forms of assessment, potential placement in the curriculum, and how to distinguish between basic algorithmic awareness compared to algorithmic literacy. Recommendations for expanding on the current Association of College and Research Libraries’ Framework for Information Literacy for Higher Education (2016) to more explicitly include algorithmic literacy were presented.

Designs is an algorithmic composition for small orchestra. The main compositional process used involves the realization and implementation of various musical algorithms discussed in the book Composition with Pitch-Classes by theorist/composer Robert Morris.