On forward pruning in game-tree search

Ph.DDOCTOR OF PHILOSOPH

Symbolic Search in Planning and General Game Playing

Search is an important topic in many areas of AI. Search problems often result in an immense number of states. This work addresses this by using a special datastructure, BDDs, which can represent large sets of states efficiently, often saving space compared to explicit representations. The first part is concerned with an analysis of the complexity of BDDs for some search problems, resulting in lower or upper bounds on BDD sizes for these. The second part is concerned with action planning, an area where the programmer does not know in advance what the search problem will look like. This part presents symbolic algorithms for finding optimal solutions for two different settings, classical and net-benefit planning, as well as several improvements to these algorithms. The resulting planner was able to win the International Planning Competition IPC 2008. The third part is concerned with general game playing, which is similar to planning in that the programmer does not know in advance what game will be played. This work proposes algorithms for instantiating the input and solving games symbolically. For playing, a hybrid player based on UCT and the solver is presented

PSO-based coevolutionary Game Learning

Games have been investigated as computationally complex problems since the inception of artificial intelligence in the 1950’s. Originally, search-based techniques were applied to create a competent (and sometimes even expert) game player. The search-based techniques, such as game trees, made use of human-defined knowledge to evaluate the current game state and recommend the best move to make next. Recent research has shown that neural networks can be evolved as game state evaluators, thereby removing the human intelligence factor completely. This study builds on the initial research that made use of evolutionary programming to evolve neural networks in the game learning domain. Particle Swarm Optimisation (PSO) is applied inside a coevolutionary training environment to evolve the weights of the neural network. The training technique is applied to both the zero sum and non-zero sum game domains, with specific application to Tic-Tac-Toe, Checkers and the Iterated Prisoners Dilemma (IPD). The influence of the various PSO parameters on playing performance are experimentally examined, and the overall performance of three different neighbourhood information sharing structures compared. A new coevolutionary scoring scheme and particle dispersement operator are defined, inspired by Formula One Grand Prix racing. Finally, the PSO is applied in three novel ways to evolve strategies for the IPD – the first application of its kind in the PSO field. The PSO-based coevolutionary learning technique described and examined in this study shows promise in evolving intelligent evaluators for the aforementioned games, and further study will be conducted to analyse its scalability to larger search spaces and games of varying complexity.Dissertation (MSc)--University of Pretoria, 2005.Computer Scienceunrestricte

Role of Distal Regulatory Elements in Cancer Progression and Therapy

Enhancer elements comprise of regions of DNA that are distal to gene promoters with a characteristic capacity to affect and regulate gene transcription. Enhancers are enriched in a highly context-specific manner allowing for intricate control of gene expression. Current studies endeavor to elucidate the mechanisms underlying enhancer activation and function to ultimately exploit their specificity in targeted therapeutics. Due to the reported addiction of cancer to aberrant gene transcription, targeting enhancer elements is a promising therapeutic target in various malignancies. In this project, we conduct a series of studies with the general aim of extending the knowledge about the molecular mechanisms by which enhancers drive aberrant transcription in cancer. We focus on epigenetic modulation to exploit enhancer elements as therapeutic or prognostic targets. In the first study included in our project, we evaluated the importance of the super enhancer subcategory of distal regulatory elements in a breast cancer cell line where estrogen plays an important role in driving gene expression through enhancers. Super enhancers are claimed to be a highly active subgroup of distal regulatory elements that is abundantly enriched with transcription factors, span long stretches of DNA, and exhibit preferential efficacy in driving major transcriptional programs in cancer. We identified super enhancers related to estrogen in this system using the standard algorithm and failed to observe a distinct high efficacy of super enhancers compared to typical enhancers. By varying the settings of this algorithm, we also uncovered biases in enhancer identification that extensively influence the results. On the other hand, we observed that major targets of estrogen activation showed a preference for association with super enhancers and concluded that they may indeed tend to regulate the transcription of master regulators. Accordingly, we concluded that the focused attention given to super enhancers should not lead to disregarding typical enhancers which also play a significant and important role in gene transcription regulation. Consequently, in the second study we reviewed the role of enhancers in pancreatic cancer, a malignancy with exceptionally low survival rates. We focused on the application of epigenetic modulators, such as bromodomain and extraterminal proteins inhibitors and histone deactylase inhibitors, in targeting enhancer elements and speculated about mechanisms underlying the reported synergy between these two inhibitors. Interestingly, we used publicly available data to further study the context-specificity of enhancers. Notably, we observed a tendency where the same oncogenic target gene is activated by different enhancers in various systems due to differential expression of transcription factors. Accordingly, we expanded our studies in pancreatic cancer and uncovered a group of subtype-specific super enhancers that drive the cells into a squamous phenotype which correlates with a particularly poor prognosis. Studying the general activation epigenetic profiles of different pancreatic cancer cell lines identified deltaNp63 as a major driver of the squamous molecular identity in cells and patient-derived xenografts. Moreover, extensive analysis of the role of deltaNp63 in driving a more aggressive phenotype uncovered the implication of super enhancers which are supported by a network of interconnected and differentially expressed transcription factors. This pattern resembles the reports of transcription factor regulatory circuitry driving the pluripotent molecular identity of embryonic stem cells. Identification of the same pattern governing differentiation into specific molecular subtypes in pancreatic cancer opens the door to precision-based medicine approaches targeting this circuitry in this particular subtype. Finally, we further investigated the role of enhancer elements in the context of chemotherapeutic resistance in pancreatic cancer. Interestingly, we observed that pro-inflammatory and migratory programs are activated in paclitaxel-resistant cells via activation of BET-dependent enhancers. Furthermore, we observed that BET inhibition sensitizes resistant and sensitive cells to paclitaxel. Notably, super enhancers that we observed to be enriched in resistant cells were associated with genes that correlate with poor prognosis. This study confirmed the patterns we uncovered in the other studies where enhancers and super enhancers drive aberrant transcription activation in cancer and present a promising target for patient treatment. Altogether, this project resulted in 2 peer-reviewed publications in the journals of Transcription and Epigenomes, one manuscript that has been peer-reviewed and is currently under revision in Proceedings of the National Academy of Sciences of the United States of America (PNAS), and another manuscript in preparation for submission. These publications/manuscripts join the growing body of literature investigating the role of enhancers in malignancy and aim to guide new approaches for precision-based medicine.2019-12-1

Future Computer Requirements for Computational Aerodynamics

Recent advances in computational aerodynamics are discussed as well as motivations for and potential benefits of a National Aerodynamic Simulation Facility having the capability to solve fluid dynamic equations at speeds two to three orders of magnitude faster than presently possible with general computers. Two contracted efforts to define processor architectures for such a facility are summarized

Taylor University Catalog 2022-2023

The 2022-2023 academic catalog of Taylor University in Upland, Indiana.https://pillars.taylor.edu/catalogs/1127/thumbnail.jp

Taylor University Catalog 2019-2020

The 2019-2020 academic catalog of Taylor University in Upland, Indiana.https://pillars.taylor.edu/catalogs/1123/thumbnail.jp

Taylor University Catalog 2021-2022

The 2021-2022 academic catalog of Taylor University in Upland, Indiana.https://pillars.taylor.edu/catalogs/1125/thumbnail.jp