A Practical Hardware Implementation of Systemic Computation

It is widely accepted that natural computation, such as brain computation, is far superior to typical computational approaches addressing tasks such as learning and parallel processing. As conventional silicon-based technologies are about to reach their physical limits, researchers have drawn inspiration from nature to found new computational paradigms. Such a newly-conceived paradigm is Systemic Computation (SC). SC is a bio-inspired model of computation. It incorporates natural characteristics and defines a massively parallel non-von Neumann computer architecture that can model natural systems efficiently. This thesis investigates the viability and utility of a Systemic Computation hardware implementation, since prior software-based approaches have proved inadequate in terms of performance and flexibility. This is achieved by addressing three main research challenges regarding the level of support for the natural properties of SC, the design of its implied architecture and methods to make the implementation practical and efficient. Various hardware-based approaches to Natural Computation are reviewed and their compatibility and suitability, with respect to the SC paradigm, is investigated. FPGAs are identified as the most appropriate implementation platform through critical evaluation and the first prototype Hardware Architecture of Systemic computation (HAoS) is presented. HAoS is a novel custom digital design, which takes advantage of the inbuilt parallelism of an FPGA and the highly efficient matching capability of a Ternary Content Addressable Memory. It provides basic processing capabilities in order to minimize time-demanding data transfers, while the optional use of a CPU provides high-level processing support. It is optimized and extended to a practical hardware platform accompanied by a software framework to provide an efficient SC programming solution. The suggested platform is evaluated using three bio-inspired models and analysis shows that it satisfies the research challenges and provides an effective solution in terms of efficiency versus flexibility trade-off

Improving the Scalability of XCS-Based Learning Classifier Systems

Using evolutionary intelligence and machine learning techniques, a broad range of intelligent machines have been designed to perform different tasks. An intelligent machine learns by perceiving its environmental status and taking an action that maximizes its chances of success. Human beings have the ability to apply knowledge learned from a smaller problem to more complex, large-scale problems of the same or a related domain, but currently the vast majority of evolutionary machine learning techniques lack this ability. This lack of ability to apply the already learned knowledge of a domain results in consuming more than the necessary resources and time to solve complex, large-scale problems of the domain. As the problem increases in size, it becomes difficult and even sometimes impractical (if not impossible) to solve due to the needed resources and time. Therefore, in order to scale in a problem domain, a systemis needed that has the ability to reuse the learned knowledge of the domain and/or encapsulate the underlying patterns in the domain. To extract and reuse building blocks of knowledge or to encapsulate the underlying patterns in a problem domain, a rich encoding is needed, but the search space could then expand undesirably and cause bloat, e.g. as in some forms of genetic programming (GP). Learning classifier systems (LCSs) are a well-structured evolutionary computation based learning technique that have pressures to implicitly avoid bloat, such as fitness sharing through niche based reproduction. The proposed thesis is that an LCS can scale to complex problems in a domain by reusing the learnt knowledge from simpler problems of the domain and/or encapsulating the underlying patterns in the domain. Wilson’s XCS is used to implement and test the proposed systems, which is a well-tested, online learning and accuracy based LCS model. To extract the reusable building blocks of knowledge, GP-tree like, code-fragments are introduced, which are more than simply another representation (e.g. ternary or real-valued alphabets). This thesis is extended to capture the underlying patterns in a problemusing a cyclic representation. Hard problems are experimented to test the newly developed scalable systems and compare them with benchmark techniques. Specifically, this work develops four systems to improve the scalability of XCS-based classifier systems. (1) Building blocks of knowledge are extracted fromsmaller problems of a Boolean domain and reused in learning more complex, large-scale problems in the domain, for the first time. By utilizing the learnt knowledge from small-scale problems, the developed XCSCFC (i.e. XCS with Code-Fragment Conditions) system readily solves problems of a scale that existing LCS and GP approaches cannot, e.g. the 135-bitMUX problem. (2) The introduction of the code fragments in classifier actions in XCSCFA (i.e. XCS with Code-Fragment Actions) enables the rich representation of GP, which when couples with the divide and conquer approach of LCS, to successfully solve various complex, overlapping and niche imbalance Boolean problems that are difficult to solve using numeric action based XCS. (3) The underlying patterns in a problem domain are encapsulated in classifier rules encoded by a cyclic representation. The developed XCSSMA system produces general solutions of any scale n for a number of important Boolean problems, for the first time in the field of LCS, e.g. parity problems. (4) Optimal solutions for various real-valued problems are evolved by extending the existing real-valued XCSR system with code-fragment actions to XCSRCFA. Exploiting the combined power of GP and LCS techniques, XCSRCFA successfully learns various continuous action and function approximation problems that are difficult to learn using the base techniques. This research work has shown that LCSs can scale to complex, largescale problems through reusing learnt knowledge. The messy nature, disassociation of message to condition order, masking, feature construction, and reuse of extracted knowledge add additional abilities to the XCS family of LCSs. The ability to use rich encoding in antecedent GP-like codefragments or consequent cyclic representation leads to the evolution of accurate, maximally general and compact solutions in learning various complex Boolean as well as real-valued problems. Effectively exploiting the combined power of GP and LCS techniques, various continuous action and function approximation problems are solved in a simple and straight forward manner. The analysis of the evolved rules reveals, for the first time in XCS, that no matter how specific or general the initial classifiers are, all the optimal classifiers are converged through the mechanism ‘be specific then generalize’ near the final stages of evolution. Also that standard XCS does not use all available information or all available genetic operators to evolve optimal rules, whereas the developed code-fragment action based systems effectively use figure and ground information during the training process. Thiswork has created a platformto explore the reuse of learnt functionality, not just terminal knowledge as present, which is needed to replicate human capabilities

Characterization and evolution of artificial RNA ligases

University of Minnesota Ph.D. dissertation. June 2015. Major: Biochemistry, Molecular Bio, and Biophysics. Advisor: Burckhard Seelig. 1 computer file (PDF); viii, 142 pages.Enzymes enable biocatalysis with minimal by-products, high regio- and enantioselectivity, and can operate under mild conditions. These properties facilitate numerous applications of enzymes in both industry and research. Great progress has been made in protein engineering to modify properties such as stability and catalytic activity of an enzyme to suit specific processes. On the contrary, the generation of artificial enzymes de novo is still challenging, and only few examples have been reported. The study and characterization of artificial enzymes will not only expand our knowledge of protein chemistry and catalysis, but ultimately improve our ability to generate novel biocatalysts and engineer those found in nature. My thesis focused on the characterization of an artificial RNA ligase previously selected from a library of polypeptide variants based on a non-catalytic protein scaffold. The selection employed mRNA display, a technique to isolate de novo enzymes in vitro from large libraries of 1013 protein variants. The artificial RNA ligase catalyzes the formation of a phosphodiester bond between two RNA substrates by joining a 5'-triphospate to a 3'-hydroxyl, with the release of pyrophosphate. This activity has not been observed in nature. An initial selection carried out at 23°C yielded variants that were poorly suitable for biochemical and biophysical characterization due do their low solubility and poor folding. We hence focused our studies on a particular improved ligase variant called ligase 10C, isolated from a subsequent selection performed at 65°C. Here we report the structural and biochemical characterization of ligase 10C. We solved the three-dimensional structure of this enzyme by NMR. Unexpectedly, the original structure of the parent scaffold used for building the original library was abandoned. The enzyme instead adopted a novel dynamic fold, not previously observed in nature. The structure was stabilized by metal coordination, yet lacked secondary structural motifs entirely. We also compared the catalytic and thermodynamic properties of ligase 10C to enzyme variants previously selected at lower temperature (23°C). Ligase 10C displayed a remarkable increase in melting temperature of 35°C compared to its mesophilic counterpart. In addition, its activity at 23°C was about 10-fold higher compared to the mesophilic variants. This work was the first mRNA display selection for catalytic activity at high temperature, and further highlighted the capacity of the technique to select for proteins with rare properties. To facilitate detailed mechanistic studies of this unnatural enzyme, a crystal structure would be essential. Unfortunately, ligase 10C did not form crystals likely due to its highly dynamic regions. With the goal of identifying a truncated less flexible version of the enzyme that would be more suited for crystallization, we generated a library of random deletion variants of ligase 10C and performed an mRNA display selection to identify shorter active variants. Finally, we describe the attempted selection of an enzyme for the same RNA ligation reaction from a completely random polypeptide library. The long-term goal of the overarching project in the Seelig lab is to elucidate and compare the structure and mechanism of enzymes generated from different starting points, yet catalyzing the same reaction, to obtain insights into potential evolutionary pathways. In summary, our work revealed the unusual structural and biophysical properties of the artificial ligase 10C, and thereby demonstrated the power and flexibility of mRNA display as a technique for the selection of de novo enzymes