Toward a One-interaction Data-driven Guide: Putting Co-speech Gesture Evidence to Work for Ambiguous Route Instructions

Interactive narrative planning offers significant potential for creating adaptive gameplay experiences. While data-driven techniques have been devised that utilize player interaction data to induce policies for interactive narrative planners, they require enormously large gameplay datasets. A promising approach to addressing this challenge is creating simulated players whose behaviors closely approximate those of human players. In this paper, we propose a novel approach to generating high-fidelity simulated players based on deep recurrent highway networks and deep convolutional networks. Empirical results demonstrate that the proposed models significantly outperform the prior state-of-the-art in generating high-fidelity simulated player models that accurately imitate human players’ narrative interactions. Using the high-fidelity simulated player models, we show the advantage of more exploratory reinforcement learning methods for deriving generalizable narrative adaptation policies.

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Protein Bioinformatics Infrastructure for the Integration and Analysis of Multiple High-Throughput “omics” Data

Advances in Bioinformatics ◽

10.1155/2010/423589 ◽

2010 ◽

Vol 2010 ◽

pp. 1-19 ◽

Cited By ~ 11

Author(s):

Chuming Chen ◽

Peter B. McGarvey ◽

Hongzhan Huang ◽

Cathy H. Wu

Keyword(s):

High Throughput ◽

Data Driven ◽

Dependent Data ◽

Biological Knowledge ◽

Protein Interaction Data ◽

Omics Data ◽

Interaction Data ◽

Data Standard ◽

Omics Data Integration

High-throughput “omics” technologies bring new opportunities for biological and biomedical researchers to ask complex questions and gain new scientific insights. However, the voluminous, complex, and context-dependent data being maintained in heterogeneous and distributed environments plus the lack of well-defined data standard and standardized nomenclature imposes a major challenge which requires advanced computational methods and bioinformatics infrastructures for integration, mining, visualization, and comparative analysis to facilitate data-driven hypothesis generation and biological knowledge discovery. In this paper, we present the challenges in high-throughput “omics” data integration and analysis, introduce a protein-centric approach for systems integration of large and heterogeneous high-throughput “omics” data including microarray, mass spectrometry, protein sequence, protein structure, and protein interaction data, and use scientific case study to illustrate how one can use varied “omics” data from different laboratories to make useful connections that could lead to new biological knowledge.

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