Relevanzbasierte Informationsbeschaffung für die informierte Entscheidungsfindung intelligenter Agenten

This dissertation introduces relevance-based information acquisition for intelligent software agents based on Howard s information value theory and decision networks. Active information acquisition is crucial in domains with partial observability in order to establish situation awareness of autonomous systems for deliberate decisions. The new semi-myopic approach addresses the complexity challenge of decision-theoretic relevance computation by reducing the set of variables to be evaluated in the first place. Links in a decision network encode stochastic dependencies of variables. Through utility dependency analysis using Pearl s d-separation criterion, the set of relevant variables can be efficiently reduced to a proven minimum without actually computing information value. In addition to an implementation with detailed runtime performance analysis, the applicability of the approach is shown in the domain of intelligent logistics control

Overview of the PALM model system 6.0

In this paper, we describe the PALM model system 6.0. PALM (formerly an abbreviation for Parallelized Large-eddy Simulation Model and now an independent name) is a Fortran-based code and has been applied for studying a variety of atmospheric and oceanic boundary layers for about 20 years. The model is optimized for use on massively parallel computer architectures. This is a follow-up paper to the PALM 4.0 model description in Maronga et al. (2015). During the last years, PALM has been significantly improved and now offers a variety of new components. In particular, much effort was made to enhance the model with components needed for applications in urban environments, like fully interactive land surface and radiation schemes, chemistry, and an indoor model. This paper serves as an overview paper of the PALM 6.0 model system and we describe its current model core. The individual components for urban applications, case studies, validation runs, and issues with suitable input data are presented and discussed in a series of companion papers in this special issue.Peer reviewe

Relevance-based Information Acquisition for Situation-aware Decision-making of Intelligent Agents

This dissertation introduces relevance-based information acquisition for intelligent software agents based on Howard s information value theory and decision networks. Active information acquisition is crucial in domains with partial observability in order to establish situation awareness of autonomous systems for deliberate decisions. The new semi-myopic approach addresses the complexity challenge of decision-theoretic relevance computation by reducing the set of variables to be evaluated in the first place. Links in a decision network encode stochastic dependencies of variables. Through utility dependency analysis using Pearl s d-separation criterion, the set of relevant variables can be efficiently reduced to a proven minimum without actually computing information value. In addition to an implementation with detailed runtime performance analysis, the applicability of the approach is shown in the domain of intelligent logistics control

Loss of organic cation transporter 3 (Oct3) leads to enhanced proliferation and hepatocarcinogenesis

Background: Organic cation transporters (OCT) are responsible for the uptake of a broad spectrum of endogenous and exogenous substrates. Downregulation of OCT is frequently observed in human hepatocellular carcinoma (HCC) and is associated with a poor outcome. The aim of our current study was to elucidate the impact of OCT3 on hepatocarcinogenesis. Methods: Transcriptional and functional loss of OCT was investigated in primary murine hepatocytes, derived from Oct3-knockout (Oct3(-/-); FVB. Slc22a3(tm1Dpb)) and wildtype (WT) mice. Liver tumors were induced in Oct3(-/-) and WT mice with Diethylnitrosamine and Phenobarbital over 10 months and characterized macroscopically and microscopically. Key survival pathways were investigated by Western Blot analysis. Results: Loss of Oct3(-/-) in primary hepatocytes resulted in significantly reduced OCT activity determined by [H-3] MPP+ uptake in vivo. Furthermore, tumor size and quantity were markedly enhanced in Oct3(-/-) mice (p<0.0001). Oct3(-/-) tumors showed significant higher proliferation (p<0.0001). Ki-67 and Cyclin D expression were significantly increased in primary Oct3(-/-) hepatocytes after treatment with the OCT inhibitors quinine or verapamil (p<0.05). Functional inhibition of OCT by quinine resulted in an activation of c-Jun N-terminal kinase (Jnk), especially in Oct3(-/-) hepatocytes. Conclusion: Loss of Oct3 leads to enhanced proliferation and hepatocarcinogenesis in vivo