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Robots deployed to the real world must be able to interact with other agents in their environment. Dynamic game theory provides a powerful mathematical framework for modeling scenarios in which agents have individual objectives and interactions evolve over time. However, a key limitation of such techniques is that they require a priori knowledge of all players' objectives. In this work, we address this issue by proposing a novel method for learning players' objectives in continuous dynamic games from noise-corrupted, partial state observations. Our approach learns objectives by coupling the estimation of unknown cost parameters of each player with inference of unobserved states and inputs through Nash equilibrium constraints. By coupling past state estimates with future state predictions, our approach is amenable to simultaneous online learning and prediction in receding horizon fashion. We demonstrate our method in several simulated traffic scenarios in which we recover players' preferences, for, e.g. desired travel speed and collision-avoidance behavior. Results show that our method reliably estimates game-theoretic models from noise-corrupted data that closely matches ground-truth objectives, consistently outperforming state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Summary: Small regulatory RNAs such as short interfering RNAs (siRNAs), microRNAs (miRNAs), and Piwi interacting RNAs (piRNAs) have been discovered in the past, and it is becoming more and more apparent that these small molecules have key regulatory functions. Small RNAs are found in all higher eukaryotes and play important roles in cellular processes as diverse as development, stress response, or transposon silencing. Soon after the discovery of small regulatory RNAs, members of the Argonaute protein family were identified as their major cellular protein interactors. This review focuses on the various cellular functions of mammalian Argonaute proteins in conjunction with the different small RNA species that are known today. [Copyright &y& Elsevier]

Summary: RNA silencing processes are guided by small RNAs known as siRNAs and microRNAs (miRNAs) . They reside in ribonucleoprotein complexes, which guide the cleavage of complementary mRNAs or affect stability and translation of partial complementary mRNAs . Argonaute (Ago) proteins are at the heart of silencing effector complexes and bind the single-stranded siRNA and miRNA . Our biochemical analysis revealed that Ago2 is present in a pre-miRNA processing complex that is able to transfer the miRNA into a target-mRNA cleaving complex. To gain insight into the function and composition of RNA silencing complexes, we purified Ago1- and Ago2-containing complexes from human cells. Several known Ago1- and/or Ago2-associated proteins including Dicer were identified, but also two novel factors, the putative RNA helicase MOV10, and the RNA recognition motif (RRM)-containing protein TNRC6B/KIAA1093. The new proteins localize, similar to Ago proteins, to mRNA-degrading cytoplasmic P bodies, and they are functionally required to mediate miRNA-guided mRNA cleavage. [Copyright &y& Elsevier]