Robo-Soar: An Integration of External Interaction, Planning, and Learning using Soar

This chapter reports progress in extending the Soar architecture to tasks that involve interaction with external environments. The tasks are performed using a Puma arm and a camera in a system called Robo-Soar. The tasks require the integration of a variety of capabilities including problem solving with incomplete knowledge, reactivity, planning, guidance from external advice, and learning to improve the efficiency and correctness of problem solving. All of these capabilities are achieved without the addition of special purpose modules or subsystems to Soar

Learning in Tele-autonomous Systems using Soar

Robo-Soar is a high-level robot arm control system implemented in Soar. Robo-Soar learns to perform simple block manipulation tasks using advice from a human. Following learning, the system is able to perform similar tasks without external guidance. Robo-Soar corrects its knowledge by accepting advice about relevance of features in its domain, using a unique integration of analytic and empirical learning techniques

Therapeutic DNA vaccine induces broad T cell responses in the gut and sustained protection from viral rebound and AIDS in SIV-infected rhesus macaques.

Immunotherapies that induce durable immune control of chronic HIV infection may eliminate the need for life-long dependence on drugs. We investigated a DNA vaccine formulated with a novel genetic adjuvant that stimulates immune responses in the blood and gut for the ability to improve therapy in rhesus macaques chronically infected with SIV. Using the SIV-macaque model for AIDS, we show that epidermal co-delivery of plasmids expressing SIV Gag, RT, Nef and Env, and the mucosal adjuvant, heat-labile E. coli enterotoxin (LT), during antiretroviral therapy (ART) induced a substantial 2-4-log fold reduction in mean virus burden in both the gut and blood when compared to unvaccinated controls and provided durable protection from viral rebound and disease progression after the drug was discontinued. This effect was associated with significant increases in IFN-γ T cell responses in both the blood and gut and SIV-specific CD8+ T cells with dual TNF-α and cytolytic effector functions in the blood. Importantly, a broader specificity in the T cell response seen in the gut, but not the blood, significantly correlated with a reduction in virus production in mucosal tissues and a lower virus burden in plasma. We conclude that immunizing with vaccines that induce immune responses in mucosal gut tissue could reduce residual viral reservoirs during drug therapy and improve long-term treatment of HIV infection in humans

US SOLAS Science Report

The article of record may be found at https://doi.org/10.1575/1912/27821The Surface Ocean – Lower Atmosphere Study (SOLAS) (http://www.solas-int.org/) is an international research initiative focused on understanding the key biogeochemical-physical interactions and feedbacks between the ocean and atmosphere that are critical elements of climate and global biogeochemical cycles. Following the release of the SOLAS Decadal Science Plan (2015-2025) (Brévière et al., 2016), the Ocean-Atmosphere Interaction Committee (OAIC) was formed as a subcommittee of the Ocean Carbon and Biogeochemistry (OCB) Scientific Steering Committee to coordinate US SOLAS efforts and activities, facilitate interactions among atmospheric and ocean scientists, and strengthen US contributions to international SOLAS. In October 2019, with support from OCB, the OAIC convened an open community workshop, Ocean-Atmosphere Interactions: Scoping directions for new research with the goal of fostering new collaborations and identifying knowledge gaps and high-priority science questions to formulate a US SOLAS Science Plan. Based on presentations and discussions at the workshop, the OAIC and workshop participants have developed this US SOLAS Science Plan. The first part of the workshop and this Science Plan were purposefully designed around the five themes of the SOLAS Decadal Science Plan (2015-2025) (Brévière et al., 2016) to provide a common set of research priorities and ensure a more cohesive US contribution to international SOLAS.This report was developed with federal support of NSF (OCE-1558412) and NASA (NNX17AB17G).This report was developed with federal support of NSF (OCE-1558412) and NASA (NNX17AB17G)

US SOLAS Science Report

The Surface Ocean – Lower Atmosphere Study (SOLAS) (http://www.solas-int.org/) is an international research initiative focused on understanding the key biogeochemical-physical interactions and feedbacks between the ocean and atmosphere that are critical elements of climate and global biogeochemical cycles. Following the release of the SOLAS Decadal Science Plan (2015-2025) (Brévière et al., 2016), the Ocean-Atmosphere Interaction Committee (OAIC) was formed as a subcommittee of the Ocean Carbon and Biogeochemistry (OCB) Scientific Steering Committee to coordinate US SOLAS efforts and activities, facilitate interactions among atmospheric and ocean scientists, and strengthen US contributions to international SOLAS. In October 2019, with support from OCB, the OAIC convened an open community workshop, Ocean-Atmosphere Interactions: Scoping directions for new research with the goal of fostering new collaborations and identifying knowledge gaps and high-priority science questions to formulate a US SOLAS Science Plan. Based on presentations and discussions at the workshop, the OAIC and workshop participants have developed this US SOLAS Science Plan. The first part of the workshop and this Science Plan were purposefully designed around the five themes of the SOLAS Decadal Science Plan (2015-2025) (Brévière et al., 2016) to provide a common set of research priorities and ensure a more cohesive US contribution to international SOLAS.This report was developed with federal support of NSF (OCE-1558412) and NASA (NNX17AB17G)

GM-CSF Increases Mucosal and Systemic Immunogenicity of an H1N1 Influenza DNA Vaccine Administered into the Epidermis of Non-Human Primates

Background: The recent H5N1 avian and H1N1 swine-origin influenza virus outbreaks reaffirm that the threat of a worldwide influenza pandemic is both real and ever-present. Vaccination is still considered the best strategy for protection against influenza virus infection but a significant challenge is to identify new vaccine approaches that offer accelerated production, broader protection against drifted and shifted strains, and the capacity to elicit anti-viral immune responses in the respiratory tract at the site of viral entry. As a safe alternative to live attenuated vaccines, the mucosal and systemic immunogenicity of an H1N1 influenza (A/New Caledonia/20/99) HA DNA vaccine administered by particle-mediated epidermal delivery (PMED or gene gun) was analyzed in rhesus macaques. Methodology/Principal Findings: Macaques were immunized at weeks 0, 8, and 16 using a disposable single-shot particlemediated delivery device designed for clinical use that delivers plasmid DNA directly into cells of the epidermis. Significant levels of hemagglutination inhibiting (HI) antibodies and cytokine-secreting HA-specific T cells were observed in the periphery of macaques following 1-3 doses of the PMED HA DNA vaccine. In addition, HA DNA vaccination induced detectable levels of HA-specific mucosal antibodies and T cells in the lung and gut-associated lymphoid tissues of vaccinated macaques. Importantly, co-delivery of a DNA encoding the rhesus macaque GM-CSF gene was found to significantly enhance both the systemic and mucosal immunogenicity of the HA DNA vaccine. Conclusions/Significance: These results provide strong support for the development of a particle-mediated epidermal DNA vaccine for protection against respiratory pathogens such as influenza and demonstrate, for the first time, the ability of skindelivered GM-CSF to serve as an effective mucosal adjuvant for vaccine induction of immune responses in the gut and respiratory tract. © 2010 Loudon et al

Physical and biogeochemical controls on the variability in surface pH and calcium carbonate saturation states in the Atlantic sectors of the Arctic and Southern Oceans

Polar oceans are particularly vulnerable to ocean acidification due to their low temperatures and reduced buffering capacity, and are expected to experience extensive low pH conditions and reduced carbonate mineral saturations states (Ω) in the near future. However, the impact of anthropogenic CO2 on pH and Ω will vary regionally between and across the Arctic and Southern Oceans. Here we investigate the carbonate chemistry in the Atlantic sector of two polar oceans, the Nordic Seas and Barents Sea in the Arctic Ocean, and the Scotia and Weddell Seas in the Southern Ocean, to determine the physical and biogeochemical processes that control surface pH and Ω. High-resolution observations showed large gradients in surface pH (0.10–0.30) and aragonite saturation state (Ωar) (0.2–1.0) over small spatial scales, and these were particularly strong in sea-ice covered areas (up to 0.45 in pH and 2.0 in Ωar). In the Arctic, sea-ice melt facilitated bloom initiation in light-limited and iron replete (dFe>0.2 nM) regions, such as the Fram Strait, resulting in high pH (8.45) and Ωar (3.0) along the sea-ice edge. In contrast, accumulation of dissolved inorganic carbon derived from organic carbon mineralisation under the ice resulted in low pH (8.05) and Ωar (1.1) in areas where thick ice persisted. In the Southern Ocean, sea-ice retreat resulted in bloom formation only where terrestrial inputs supplied sufficient iron (dFe>0.2 nM), such as in the vicinity of the South Sandwich Islands where enhanced pH (8.3) and Ωar (2.3) were primarily due to biological production. In contrast, in the adjacent Weddell Sea, weak biological uptake of CO2 due to low iron concentrations (dFe<0.2 nM) resulted in low pH (8.1) and Ωar (1.6). The large spatial variability in both polar oceans highlights the need for spatially resolved surface data of carbonate chemistry variables but also nutrients (including iron) in order to accurately elucidate the large gradients experienced by marine organisms and to understand their response to increased CO2 in the future

Resource-dependent behavior through adaptation.

An autonomous agent functioning in the real world consumes finite resources as it operates, creating resource constraints upon its execution. The dynamics of the real world cause the environment, including those resource constraints, to change in ways the agent can neither predict nor control and in ways the agent cannot always be preprogrammed to handle. Behavior that is reactive with respect to (responsive to changes in the availability of) those resources is desired in such a situation, but the agent will be able to arrive at this reactivity only through experience in its environment. The agent's success in a dynamic environment will depend upon its ability to adapt to the conditions and constraints imposed upon it by its environment--its ability to create Resource-Dependent Behavior Through Adaptation. The dynamics of the real world necessitate a view of resources, and of the agent's knowledge about its resource consumption, as being dynamic rather than static entities. In my dissertation I present RDB, an agent capable of producing behavior that is sensitive to the availability of critical resources and to its own knowledge about its resource consumption. RDB observes its own execution and monitors its critical resource consumption, allowing it to learn about its requirements from experience--RDB can both acquire new estimates for previously untested actions and can adjust existing consumption estimates that prove to be inaccurate. RDB builds new plans to meet new situations and associates global consumption estimates to the rules forming the new plan, which allows it to determine the plan's overall needs. At each step of execution RDB considers these global estimates and compares them to the available resources it is monitoring and changes its behavior accordingly. RDB is a flexible system in terms of both behavior and knowledge. Its resource knowledge is continually under development in terms of both its quality and its quantity. RDB can use resource knowledge at any stage of its development to help guide planning and execution. Successful applications of RDB demonstrate the high degree of adaptivity to dynamic environments that can be achieved by systems combining learning, planning, and reactivity.Ph.D.Computer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/103343/1/9308485.pdfDescription of 9308485.pdf : Restricted to UM users only

Robo-Soar: An integration of external interaction, planning, and learning using Soar

This paper reports progress in extending the Soar architecture to tasks that involve interaction with external environments. The tasks are performed using a Puma arm and a camera in a system called Robo-Soar. The tasks require the integration of a variety of capabilities including problem solving with incomplete knowledge, reactivity, planning, guidance from external advice, and learning to improve the efficiency and correctness of problem solving. All of these capabilities are achieved without the addition of special purpose modules or subsystems to Soar

Correcting and extending domain knowledge using outside guidance

Analytic learning techniques, such as explanation- based learning (EBL), can be powerful methods for acquiring knowledge about a domain where there is a pre-existing theory of the domain. One application of EBL has been to learning apprentice systems where the solution to a problem generated by a human is used as input to the learning process. The learning system analyzes the example and is then able to solve similar problems without outside assistance. One limitation of EBL is that the domain theory must be complete and correct. In this paper we present a general technique for learning from outside guidance that can correct and extend a domain theory. In contrast to hybrid systems that use both analytic and empirical techniques, our approach is completely analytic, using the chunking learning mechanism in the Soar architecture. This technique is demonstrated for a block manipulation task that uses real blocks, a Puma robot arm and a camera vision system