The GPGP/TAEMS domain-independent coordination framework for small agent groups was first described in 1992 and then more fully detailed in an ICMAS'95 paper. In this paper, we discuss the evolution of this framework which has been motivated by its use in a number of applications, including: information gathering and management, intelligent home automation, distributed situation assessment, coordination of concurrent engineering activities, hospital scheduling, travel planning, repair service coordination and supply chain management. First, we review the basic architecture of GPGP and then present extensions to the TAEMS domain-independent representation of agent activities. We next describe extensions to GPGP that permit the representation of situation-specific coordination strategies and social laws as well as making possible the use of GPGP in large agent organizations. Additionally, we discuss a more encompassing view of commitments that takes into account uncertainty in commitments. We then present new coordination mechanisms for use in resource sharing and contracting, and more complex coordination mechanisms that use a cooperative search among agents to find appropriate commitments. We conclude with a summary of the major ideas underpinning GPGP, an analysis of the applicability of the GPGP framework including performance issues, and a discussion of future research directions.

The World Wide Web has become an invaluable information resource but the explosion of available information has made web search a time consuming and complex process. The large number of information sources and their different levels of accessibility, reliability and associated costs present a complex information gathering control problem. This paper describes the rationale, architecture, and implementation of a next generation information gathering system-a system that integrates several areas of Artificial Intelligence research under a single umbrella. Our solution to the information explosion is an information gathering agent, BIG, that plans to gather information to support a decision process, reasons about the resource trade-offs of different possible gathering approaches, extracts information from both unstructured and structured documents, and uses the extracted information to refine its search and processing activities.

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Sophisticated agents operating in open environments must make decisions that efficiently trade off the use of their limited resources between dynamic deliberative actions and domain actions. This is the meta-level control problem for agents operating in resource-bounded multi-agent environments. Control activities involve decisions on when to invoke and the amount to effort to put into scheduling and coordination of domain activities. The focus of this paper is how to make effective meta-level control decisions. We show that meta-level control with bounded computational overhead allows complex agents to solve problems more efficiently than current approaches in dynamic open multi-agent environments. The meta-level control approach that we present is based on the decision-theoretic use of an abstract representation of the agent state. This abstraction concisely captures critical information necessary for decision making while bounding the cost of meta-level control and is appropriate for use in automatically learning the meta-level control policies.

In this paper, a two-dimensional photonic crystal biosensor for medical applications based on two waveguides and a nanocavity was explored with different shoulder-coupled nanocavity structures. The most important biosensor parameters, like the sensitivity and quality factor, can be significantly improved. By injecting an analyte into a sensing hole, the refractive index of the hole was changed. This refractive index biosensor senses the changes and shifts its operating wavelength accordingly. The transmission characteristics of light in the biosensor under different refractive indices that correspond to the change in the analyte concentration are analyzed by the finite-difference time-domain method. The band gap for each structure is designed and observed by the plane wave expansion method. These proposed structures are designed to obtain an analyte refractive index variation of about 1-1.5 in an optical wavelength range of 1.250-1.640 µm. Accordingly, an improved sensitivity of 136.6 nm RIU −1 and a quality factor as high as 3915 is achieved. An important feature of this structure is its very small dimensions. Such a combination of attributes makes the designed structure a promising element for label-free biosensing applications.

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BackgroundAdverse pregnancy outcomes (APOs) affect a large proportion of pregnancies and represent an important cause of morbidity and mortality worldwide. Yet, the pathophysiology of APOs is poorly understood, limiting our ability to prevent and treat these conditions.ObjectiveTo search for genetic risk markers for four APOs, we performed genome-wide association studies (GWAS) for preterm birth, preeclampsia, gestational diabetes, and pregnancy loss.Study DesignA total of 9,757 nulliparas from the nuMoM2b study were genotyped. We clustered participants by their genetic ancestry and focused our analyses on the three sub-cohorts with the largest sample sizes: European (EUR, n=6,082), African (AFR, n=1,425), and American (AMR, n=846). Association tests were carried out separately for each sub-cohort and brought together via meta-analysis. Four APOs were tested by GWAS: preeclampsia (n=7,909), gestational length (n=4,781), gestational diabetes (n=7,617), and pregnancy loss (n=7,809). Using the results of the genome-wide associations for each APO, SNP-based heritability of these traits was inferred using LDscore. Putative regulatory effects were inferred by transcriptome-wide association analysis.ResultsTwo variants were significantly associated with pregnancy loss (rs62021480: OR = 3.29, P = 7.83×10−11, and rs142795512: OR = 4.72, P = 9.64×10−9), implicating genes TRMU and RGMA in this APO. An intronic variant was significantly associated with gestational length (rs73842644: beta = -0.667, P = 4.9×10−8). Three loci were significantly associated with gestational diabetes (rs72956265: OR = 3.09, P = 2.98×10−8, rs10890563: OR = 1.88, P = 3.53×10−8, rs117689036: OR = 3.15, P = 1.46×10−8), located on or near ZBTB20, GUCY1A2, and MDGA2, respectively. Several loci previously correlated with preterm birth (in genes WNT4, EBF1, PER3, IL10, and ADCY5), gestational diabetes (in TCF7L2), and preeclampsia (in MTHFR) were found to be associated with these outcomes in our cohort as well.ConclusionOur study identified genetic associations with gestational diabetes, pregnancy loss, and gestational length. We also confirm correlations of several previously identified variants with these APOs.Disclosure StatementThe authors declare no conflict of interestSource of financial supportPrecision Health Initiative of Indiana University, National Institutes of Health award R01HD101246 to DMH and PR. Cooperative agreement funding from the National Heart, Lung, and Blood Institute and the Eunice Kennedy Shriver National Institute of Child Health and Human Development: grant U10-HL119991 to RTI International; grant U10-HL119989 to Case Western Reserve University; grants U10-HL120034 and R01LM013327 to Columbia University; grant U10-HL119990 to Indiana University; grant U10-HL120006 to the University of Pittsburgh; grant U10-HL119992 to Northwestern University; grant U10-HL120019 to the University of California, Irvine; grant U10-HL119993 to University of Pennsylvania; and grant U10-HL120018 to the University of Utah. National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health to Clinical and Translational Science Institutes at Indiana University (grant UL1TR001108) and University of California, Irvine (grant UL1TR000153).