Artificial Intelligence for Game Playing

Práce se zabývá metodami umělé inteligence aplikovanými pro hraní strategických her, ve kterých probíhá veškerá interakce v reálném čase (tzv. real-time strategic - RTS). V práci se zabývám zejména metodu strojového učení Q-learning založenou na zpětnovazebním učení a Markovovu rozhodovacím procesu. Praktická část práce je implementována pro hraní hry StarCraft: Brood War.Mnou navržené řešení, implementované v rámci pravidel soutěže SSCAIT, se učí sestavit optimální konstrukční pořadí budov dle hracího stylu oponenta. Analýza a vyhodnocení systému jsou provedeny srovnáním s ostatními účastníky soutěže a rovněž na základě sady odehraných her a porovnání počátečního chování s výsledným chováním natrénovaným právě na této sadě.The focus of this work is the use of artificial intelligence methods for a playing of real-time strategic (RTS) games, where all interactions of players are performed in real time (in parallel). The thesis deals mainly with the use of machine learning method Q-learning, which is based on reinforcement learning and Markov decision process. The practice part of this work is implemented for StarCraft: Brood War game.A proposed solution learns to make up an optimal order of buildings construction in respect to a playing style (strategy) of the opponent(s). The solution is proposed within the rules of the SSCAIT tournament. Analysis and evaluation of the proposed system are based on a comparison with other participants of the competition as well as a comparison of the system behavior before and after the playing of a set of the games.

Additional file 3: Table S3. of Epigenetic modifying enzyme expression in asthmatic airway epithelial cells and fibroblasts

Comparison of epigenetic modifier gene expression between epithelial cells and fibroblasts from healthy donors. (DOCX 17 kb

Additional file 1: Table S1. of Epigenetic modifying enzyme expression in asthmatic airway epithelial cells and fibroblasts

Epigenetic modification genes including family, full name, and alias. (DOCX 19 kb

Additional file 1: of Gene expression analysis in asthma using a targeted multiplex array

Supplementary Methods – Methods describing selection of house keeping genes and immunohistochemical staining procedure. Supplementary Tables – Tables containing clinical demographics for subjects, average counts, fold change, and p-value for all genes studied, and all differentially co-expressed genes. Supplementary Figures and Legends – Figures showing sample immunohistochemical staining for proteins of significantly altered genes, co-expression plots. (DOCX 35 kb

Patient demographics in DNA methylation cohort prior to surgery.

<p>Subjects are identified as healthy, atopic, atopic asthmatic, or non-atopic asthmatic.</p

DNA methylation profile of AECs compared to PBMCs.

<p>DNA methylation for 1027 CpG sites was assessed in AECs compared to PBMCs from all subjects. A. Volcano plot of CpG sites interrogated with red and blue points indicating significantly over- and under-methylated sites. Q-values are shown on the y-axis (−log₁₀) and z-score difference on the x-axis (log₂). Dashed lines indicate cut-offs for significance. B. Heatmap illustrating z-scores of 80 differentially methylated loci in AECs compared to PBMCs. Columns represent subjects and rows represent CpG sites while red/blue indicates more/less methylated. C. The molecular and cellular functions of the 67 genes classified by IPA. The x-axis shows functions while the y-axis shows –log(p-value).</p

Patient demographics in gene expression cohort prior to surgery.

<p>Subjects are identified as healthy, atopic, or atopic asthmatic.</p

DNA methylation heatmaps of CpG sites in PBMCs and AECs from healthy, atopic and asthmatic pediatric subjects.

<p>AECs and PBMCs were analyzed for 1027 CpG loci in 671 genes from healthy (A), atopic (B), and asthmatic (C) subjects. Heatmaps of z-scores for AECs and PBMCs are shown with individuals (columns) and differential CpG sites (rows). Increased methylation is shown in red and decreased methylation in blue. D. Venn diagram showing overlap of differentially methylated sites between healthy, atopic and asthmatic subjects. Numbers in black indicate total number of CpG sites while numbers in red/blue indicate more/less methylated in AECs (compared to PBMCs). E. The molecular and cellular functions in the 47 genes classified by IPA. The x-axis shows functions while the y-axis shows –log(p-value).</p

STAT5A and CRIP1 Gene Expression and DNA Methylation Status in AECs.

<p>AECs from atopic, healthy and asthmatic individuals were analyzed for STAT5A (A) and CRIP1 (C) mRNA expression using RT-PCR. Results are expressed as gene expression normalized to PPIA (y-axis). DNA methylation status is shown as M-values for STAT5A_E42_F (B) and CRIP1_P874_R (D) for the three phenotypes. * indicates differential methylation as detailed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0044213#pone-0044213-t003" target="_blank">Table 3</a>.</p

Differential methylation between disease phenotypes in AECs or PBMCs.

<p>Volcano plots of CpG sites interrogated with red and blue points indicating significantly over- and under-methylated sites. Q-values are shown on the y-axis (−log₁₀) and z-score difference on the x-axis (log₂). Dashed lines indicate cut-offs for significance. Within AECs, differences in DNA methylation were assessed in healthy subjects compared to atopics (A) and asthmatics (B) as well as atopic subjects compared to asthmatics (C). The same comparisons were performed in PBMCs (D, E, and F).</p