35,979 research outputs found
CompILE: Compositional Imitation Learning and Execution
We introduce Compositional Imitation Learning and Execution (CompILE): a
framework for learning reusable, variable-length segments of
hierarchically-structured behavior from demonstration data. CompILE uses a
novel unsupervised, fully-differentiable sequence segmentation module to learn
latent encodings of sequential data that can be re-composed and executed to
perform new tasks. Once trained, our model generalizes to sequences of longer
length and from environment instances not seen during training. We evaluate
CompILE in a challenging 2D multi-task environment and a continuous control
task, and show that it can find correct task boundaries and event encodings in
an unsupervised manner. Latent codes and associated behavior policies
discovered by CompILE can be used by a hierarchical agent, where the high-level
policy selects actions in the latent code space, and the low-level,
task-specific policies are simply the learned decoders. We found that our
CompILE-based agent could learn given only sparse rewards, where agents without
task-specific policies struggle.Comment: ICML (2019
Formal certification and compliance for run-time service environments
With the increased awareness of security and safety of services in on-demand distributed service provisioning (such
as the recent adoption of Cloud infrastructures), certification and compliance checking of services is becoming a key element for service engineering. Existing certification techniques tend to support mainly design-time checking of service properties and tend not to support the run-time monitoring and progressive certification in the service execution environment. In this paper we discuss an approach which provides both design-time and runtime behavioural compliance checking for a services architecture, through enabling a progressive event-driven model-checking technique. Providing an integrated approach to certification and compliance is a challenge however using analysis and monitoring techniques we present such an approach for on-going compliance checking
Rich Interfaces for Dependability: Compositional Methods for Dynamic Fault Trees and Arcade models
This paper discusses two behavioural interfaces for reliability analysis: dynamic fault trees, which model the system reliability in terms of the reliability of its components and Arcade, which models the system reliability at an architectural level. For both formalisms, the reliability is analyzed by transforming the DFT or Arcade model to a set of input-output Markov Chains. By using compositional aggregation techniques based on weak bisimilarity, significant reductions in the state space can be obtained
Cox regression survival analysis with compositional covariates: application to modelling mortality risk from 24-h physical activity patterns
Survival analysis is commonly conducted in medical and public health research to assess the association of an exposure or intervention with a hard end outcome such as mortality. The Cox (proportional hazards) regression model is probably the most popular statistical tool used in this context. However, when the exposure includes compositional covariables (that is, variables representing a relative makeup such as a nutritional or physical activity behaviour composition), some basic assumptions of the Cox regression model and associated significance tests are violated. Compositional variables involve an intrinsic interplay between one another which precludes results and conclusions based on considering them in isolation as is ordinarily done. In this work, we introduce a formulation of the Cox regression model in terms of log-ratio coordinates which suitably deals with the constraints of compositional covariates, facilitates the use of common statistical inference methods, and allows for scientifically meaningful interpretations. We illustrate its practical application to a public health problem: the estimation of the mortality hazard associated with the composition of daily activity behaviour (physical activity, sitting time and sleep) using data from the U.S. National Health and Nutrition Examination Survey (NHANES)
State-space based mass event-history model I: many decision-making agents with one target
A dynamic decision-making system that includes a mass of indistinguishable
agents could manifest impressive heterogeneity. This kind of nonhomogeneity is
postulated to result from macroscopic behavioral tactics employed by almost all
involved agents. A State-Space Based (SSB) mass event-history model is
developed here to explore the potential existence of such macroscopic
behaviors. By imposing an unobserved internal state-space variable into the
system, each individual's event-history is made into a composition of a common
state duration and an individual specific time to action. With the common state
modeling of the macroscopic behavior, parametric statistical inferences are
derived under the current-status data structure and conditional independence
assumptions. Identifiability and computation related problems are also
addressed. From the dynamic perspectives of system-wise heterogeneity, this SSB
mass event-history model is shown to be very distinct from a random effect
model via the Principle Component Analysis (PCA) in a numerical experiment.
Real data showing the mass invasion by two species of parasitic nematode into
two species of host larvae are also analyzed. The analysis results not only are
found coherent in the context of the biology of the nematode as a parasite, but
also include new quantitative interpretations.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS189 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Multivariate Decomposition for Hazard Rate Models
We develop a regression decomposition technique for hazard rate models, where the difference in observed rates is decomposed into components attributable to group differences in characteristics and group differences in effects. The baseline hazard is specified using a piecewise constant exponential model, which leads to convenient estimation based on a Poisson regression model fit to person-period, or split-episode data. This specification allows for a flexible representation of the baseline hazard and provides a straightforward way to introduce time-varying covariates and time-varying effects. We provide computational details underlying the method and apply the technique to the decomposition of the black-white difference in first premarital birth rates into components reflecting characteristics and effect contributions of several predictors, as well as the effect contribution attributable to race differences in the baseline hazard.Poisson regression, hazard rates, decomposition, piecewise constant exponential model
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