Making friends on the fly : advances in ad hoc teamwork

textGiven the continuing improvements in design and manufacturing processes in addition to improvements in artificial intelligence, robots are being deployed in an increasing variety of environments for longer periods of time. As the number of robots grows, it is expected that they will encounter and interact with other robots. Additionally, the number of companies and research laboratories producing these robots is increasing, leading to the situation where these robots may not share a common communication or coordination protocol. While standards for coordination and communication may be created, we expect that any standards will lag behind the state-of-the-art protocols and robots will need to additionally reason intelligently about their teammates with limited information. This problem motivates the area of ad hoc teamwork in which an agent may potentially cooperate with a variety of teammates in order to achieve a shared goal. We argue that agents that effectively reason about ad hoc teamwork need to exhibit three capabilities: 1) robustness to teammate variety, 2) robustness to diverse tasks, and 3) fast adaptation. This thesis focuses on addressing all three of these challenges. In particular, this thesis introduces algorithms for quickly adapting to unknown teammates that enable agents to react to new teammates without extensive observations. The majority of existing multiagent algorithms focus on scenarios where all agents share coordination and communication protocols. While previous research on ad hoc teamwork considers some of these three challenges, this thesis introduces a new algorithm, PLASTIC, that is the first to address all three challenges in a single algorithm. PLASTIC adapts quickly to unknown teammates by reusing knowledge it learns about previous teammates and exploiting any expert knowledge available. Given this knowledge, PLASTIC selects which previous teammates are most similar to the current ones online and uses this information to adapt to their behaviors. This thesis introduces two instantiations of PLASTIC. The first is a model-based approach, PLASTIC-Model, that builds models of previous teammates' behaviors and plans online to determine the best course of action. The second uses a policy-based approach, PLASTIC-Policy, in which it learns policies for cooperating with past teammates and selects from among these policies online. Furthermore, we introduce a new transfer learning algorithm, TwoStageTransfer, that allows transferring knowledge from many past teammates while considering how similar each teammate is to the current ones. We theoretically analyze the computational tractability of PLASTIC-Model in a number of scenarios with unknown teammates. Additionally, we empirically evaluate PLASTIC in three domains that cover a spread of possible settings. Our evaluations show that PLASTIC can learn to communicate with unknown teammates using a limited set of messages, coordinate with externally-created teammates that do not reason about ad hoc teams, and act intelligently in domains with continuous states and actions. Furthermore, these evaluations show that TwoStageTransfer outperforms existing transfer learning algorithms and enables PLASTIC to adapt even better to new teammates. We also identify three dimensions that we argue best describe ad hoc teamwork scenarios. We hypothesize that these dimensions are useful for analyzing similarities among domains and determining which can be tackled by similar algorithms in addition to identifying avenues for future research. The work presented in this thesis represents an important step towards enabling agents to adapt to unknown teammates in the real world. PLASTIC significantly broadens the robustness of robots to their teammates and allows them to quickly adapt to new teammates by reusing previously learned knowledge.Computer Science

The Impact of Immunocompromise on Outcomes of COVID-19 in Children and Young People - A Systematic Review and Meta-Analysis

Background: Despite children and young people (CYP) having a low risk for severe Coronavirus disease 2019 (COVID-19) outcomes, there is still a degree of uncertainty related to their risk in the context of immunodeficiency or immunosuppression, primarily due to significant reporting bias in most studies, as CYP characteristically experience milder or asymptomatic COVID-19 infection and the severe outcomes tend to be overestimated. Methods: A comprehensive systematic review to identify globally relevant studies in immunosuppressed CYP and CYP in general population (defined as younger than 25 years of age) up to 31st October 2021 (to exclude vaccinated populations), was performed. Studies were included if they reported the two primary outcomes of our study, admission to intensive therapy unit (ITU) and mortality, while data on other outcomes, such as hospitalisation and need for mechanical ventilation were also collected. A meta-analysis estimated the pooled proportion for each severe COVID-19 outcome, using the inverse variance method. Random effects models were used to account for interstudy heterogeneity. Findings: The systematic review identified 30 eligible studies for each of the two populations investigated: immunosuppressed CYP (n=793) and CYP in general population (n=102,022). Our meta-analysis found higher estimated prevalence for hospitalization (46% vs. 16%), ITU admission (12% vs. 2%), mechanical ventilation (8% vs. 1%) and increased mortality due to severe COVID-19 infection (6.5% vs. 0.2%) in immunocompromised CYP compared to CYP in general population. This shows an overall trend for more severe outcomes of COVID-19 infection in immunocompromised CYP, similar to adult studies. Interpretation: This is the only up to date meta-analysis in immunocompromised CYP with high global relevance, which excluded reports from hospitalised cohorts alone and included 35% studies from low- and medium-income countries. Future research is required to characterise individual subgroups of immunocompromised patients, as well as impact of vaccination on severe COVID-19 outcomes. Funding: There was no funding source specifically dedicated for this study. CC is supported by a National Institute of Health Research (NIHR) Biomedical Research Centre (BRC) at University College London Hospital (UCLH). The study was performed within the Centre for Adolescent Rheumatology Versus Arthritis at University College London (UCL), UCL Hospital and Great Ormond Street Hospital (GOSH) supported by grants from Versus Arthritis (21593 and 20164), Great Ormond Street Children’s Charity, and the NIHR-BRC at both GOSH and UCLH

Event Tables for Efficient Experience Replay

Experience replay (ER) is a crucial component of many deep reinforcement learning (RL) systems. However, uniform sampling from an ER buffer can lead to slow convergence and unstable asymptotic behaviors. This paper introduces Stratified Sampling from Event Tables (SSET), which partitions an ER buffer into Event Tables, each capturing important subsequences of optimal behavior. We prove a theoretical advantage over the traditional monolithic buffer approach and combine SSET with an existing prioritized sampling strategy to further improve learning speed and stability. Empirical results in challenging MiniGrid domains, benchmark RL environments, and a high-fidelity car racing simulator demonstrate the advantages and versatility of SSET over existing ER buffer sampling approaches

The PAAFID project:exploring the perspectives of autism in adult females among intellectual disability healthcare professionals

Purpose: The purpose of this paper is to explore the perspectives of healthcare professionals on autism in adult females with intellectual disability (ID), including regarding the gender ratio of autism, the clinical manifestation of autism in females, and the recognition, screening and diagnosis of autism. Design/methodology/approach: The questionnaire was developed following a review of the relevant literature and distributed to professionals within three healthcare trusts as well as members of two clinical research groups. The questionnaire was completed by 80 ID healthcare professionals. Data were aggregated and analysed using Microsoft Excel. Findings: ID healthcare professionals had a lack of recognition of the smaller gender ratio of autism in patients with ID as compared to those without ID. Most respondents reported believing that autism manifests differently in females; with women demonstrating a greater ability to mask their symptoms. A considerable proportion of participants reported feeling less confident in recognising, screening and diagnosing autism in female patients, with many endorsing a wish for additional training in this area. Practical implications: These findings suggest that ID healthcare professionals are keen to improve their skills in providing services for women with autism. Training programmes at all levels should incorporate the specific needs of women with ASD, and individual professionals and services should actively seek to address these training needs in order to promote best practice and better outcomes for women with autism. Originality/value: This is the first published questionnaire exploring the perspectives of healthcare professionals regarding autism in adult females with ID

Spin Networks for Non-Compact Groups

Spin networks are natural generalization of Wilson loops functionals. They have been extensively studied in the case where the gauge group is compact and it has been shown that they naturally form a basis of gauge invariant observables. Physically the restriction to compact gauge group is enough for the study of Yang-mills theories, however it is well known that non-compact groups naturally arise as internal gauge groups for Lorentzian gravity models. In this context a proper construction of gauge invariant observables is needed. The purpose of this work is to define the notion of spin network states for non-compact groups. We first built, by a careful gauge fixing procedure, a natural measure and a Hilbert space structure on the space of gauge invariant graph connection. Spin networks are then defined as generalized eigenvectors of a complete set of hermitic commuting operators. We show how the delicate issue of taking the quotient of a space by non compact groups can be address in term of algebraic geometry. We finally construct the full Hilbert space containing all spin network states. Having in mind application to gravity we illustrate our results for the groups SL(2,R), SL(2,C).Comment: 43pages, many figures, some comments adde

KSU Chorale and Men\u27s Ensemble

The KSU Chorale and Men\u27s Ensemble, under the direction of Dr. Reid Masters, present their fall concert featuring the works of Clemons non Papa and Peter Hamlin.https://digitalcommons.kennesaw.edu/musicprograms/2317/thumbnail.jp

River Dolphins Can Act as Population Trend Indicators in Degraded Freshwater Systems

Conservation attention on charismatic large vertebrates such as dolphins is often supported by the suggestion that these species represent surrogates for wider biodiversity, or act as indicators of ecosystem health. However, their capacity to act as indicators of patterns or trends in regional biodiversity has rarely been tested. An extensive new dataset of >300 last-sighting records for the Yangtze River dolphin or baiji and two formerly economically important fishes, the Yangtze paddlefish and Reeves’ shad, all of which are probably now extinct in the Yangtze, was collected during an interview survey of fishing communities across the middle-lower Yangtze drainage. Untransformed last-sighting date frequency distributions for these species show similar decline curves over time, and the linear gradients of transformed last-sighting date series are not significantly different from each other, demonstrating that these species experienced correlated population declines in both timing and rate of decline. Whereas species may be expected to respond differently at the population level even in highly degraded ecosystems, highly vulnerable (e.g. migratory) species can therefore display very similar responses to extrinsic threats, even if they represent otherwise very different taxonomic, biological and ecological groupings. Monitoring the status of river dolphins or other megafauna therefore has the potential to provide wider information on the status of other threatened components of sympatric freshwater biotas, and so represents a potentially important monitoring tool for conservation management. We also show that interview surveys can provide robust quantitative data on relative population dynamics of different species