14 research outputs found
Circa: The Cooperatice Intelligent Real-Time Control Architecture
The Cooperative Intelligent Real-time Control Architecture (CIRCA)
is a novel architecture for intelligent real-time control that can
guarantee to meet hard deadlines while still using unpredictable,
unrestricted AI methods. CIRCA includes a real-time subsystem used to
execute reactive control plans that are guaranteed to meet the domain's
real-time deadlines, keeping the system safe. At the same time, CIRCA's AI
subsystem performs higher-level reasoning about the domain and the
system's goals and capabilities, to develop future reactive control plans.
CIRCA thus aims to be intelligent about real-time: rather than requiring
the system's AI methods to meet deadlines, CIRCA isolates its reasoning
about which time-critical reactions to guarantee from the actual execution
of the se ected reactions.
The formal basis for CIRCA's performance guarantees is a
state-based world model of agent/environment interactions. Borrowing
approaches from real-time systems research, the world model provides the
information required to make real-time performance guarantees, but avoids
unnecessary complexity. Using the world model, the AI subsystem develops
reactive control plans that restrict the world to a limited set of safe
and desirable states, by guaranteeing the timely performance of actions to
preempt transitions that lead out of the set of states. By executing such
"safe" and "stable" plans, CIRCA's real-time subsystem is able to keep the
system safe (in the world as modeled) for an indeterminate amount of time,
while the parallel AI subsystem develops the next appropriate plan.
We have applied a prototype CIRCA implementation to a simulated
Puma robot arm performing multiple tasks with real-time deadlines, such as
packing parts off a conveyor belt and responding to asynchronous
interrupts. Our experimental results show how the system can guarantee to
accomplish these tasks under a given set of domain conditions (e.g.,
conveyor belt speed) and resource limitations (e.g., robot arm speed).
Furthermore, because CIRCA reasons explicitly about its own predictable,
guaranteed behaviors, the system can recognize when its resources are
insufficient for the desired behaviors (e.g., parts are arriving too
quickly to be packed carefully), and can then make principled
modifications to its performance (e.g., temporarily stacking parts on a
table) to maintain system safety.
(Also cross-referenced as UMIACS-TR-93-104
The Challenges of Real-Time AI
The research agendas of two major areas of computer science are
converging: Artificial Intelligence (AI) methods are moving towards
more realistic domains requiring real-time responses, and real-time
systems are moving towards more complex applications requiring
intelligent behavior. Together, they meet at the crossroads of
interest in "real-time intelligent control," or "real-time AI." This
subfield is still being defined by the common interests of researchers
from both real-time and AI systems. As a result, the precise goals for
various real-time AI systems are still in flux. This paper describes
an organizing conceptual structure for current real-time AI research,
clarifying the different meanings this term has acquired for various
researchers. Having identified the various goals of real-time AI
research, we then specify some of the necessary steps towards reaching
those goals. This in turn enables us to identify promising areas for
future research in both AI and real-time systems techniques.
(Also cross-referenced as UMIACS-TR-94-69
CIRCA: The Cooperative Intelligent Real-time Control Architecture.
The Cooperative Intelligent Real-time Control Architecture (CIRCA) is a novel architecture for intelligent real-time control that can guarantee to meet hard deadlines while still using unpredictable, unrestricted AI methods. CIRCA includes a real-time subsystem used to execute reactive control plans that are guaranteed to meet the domain's real-time deadlines, keeping the system safe. At the same time, CIRCA's AI subsystem performs higher-level reasoning about the domain and the system's goals and capabilities, to develop future reactive control plans. CIRCA thus aims to be intelligent real-time: rather than requiring the system's AI methods to meet deadlines, CIRCA isolates its reasoning about which time-critical reactions to guarantee from the actual execution of the selected reactions. The formal basis for CIRCA's performance guarantees is a state-based world model of agent/environment interactions. Borrowing approaches from real-time systems research, the world model provides the information required to make real-time performance guarantees, but avoids unnecessary complexity. Using the world model, the AI subsystem develops reactive control plans that restrict the world to a limited set of safe and desirable states, by guaranteeing the timely performance of actions to preempt transitions that lead out of the set of states. By executing such "safe" and "stable" plans, CIRCA's real-time subsystem is able to keep the system safe (in the world as modeled) for an indeterminate amount of time, while the parallel AI subsystem develops the next appropriate plan. We have applied a prototype CIRCA implementation to a simulated Puma robot arm performing multiple tasks with real-time deadlines, such as packing parts off a conveyor belt and responding to asynchronous interrupts. Our experimental results show how the system can guarantee to accomplish these tasks under a given set of domain conditions (e.g., conveyor belt speed) and resource limitations (e.g., robot arm speed). Furthermore, because CIRCA reasons explicitly about its own predictable, guaranteed behaviors, the system can recognize when its resources are insufficient for the desired behaviors (e.g., parts are arriving too quickly to be packed carefully), and can then make principled modifications to its performance (e.g., temporarily stacking parts on a table) to maintain system safety.Ph.D.Computer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/103819/1/9409771.pdfDescription of 9409771.pdf : Restricted to UM users only
Any-dimension algorithms and real-time AI
http://deepblue.lib.umich.edu/bitstream/2027.42/6741/5/bac3197.0001.001.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/6741/4/bac3197.0001.001.tx
Absolute risk of major depression associated with the polygenic risk score for depression, parental socio-economic status and history of mental disorders: a Danish population-based study
50 ABSOLUTE RISK OF MAJOR DEPRESSION ASSOCIATED WITH THE POLYGENIC RISK SCORE FOR DEPRESSION, PARENTAL SOCIO-ECONOMIC STATUS AND HISTORY OF MENTAL DISORDERS: A DANISH POPULATION-BASED STUDY
Biochemical and radiologic improvement in Paget's disease of bone treated with alendronate: A randomized, placebo-controlled trial
ADuLT: An efficient and robust time-to-event GWAS
Abstract Proportional hazards models have been proposed to analyse time-to-event phenotypes in genome-wide association studies (GWAS). However, little is known about the ability of proportional hazards models to identify genetic associations under different generative models and when ascertainment is present. Here we propose the age-dependent liability threshold (ADuLT) model as an alternative to a Cox regression based GWAS, here represented by SPACox. We compare ADuLT, SPACox, and standard case-control GWAS in simulations under two generative models and with varying degrees of ascertainment as well as in the iPSYCH cohort. We find Cox regression GWAS to be underpowered when cases are strongly ascertained (cases are oversampled by a factor 5), regardless of the generative model used. ADuLT is robust to ascertainment in all simulated scenarios. Then, we analyse four psychiatric disorders in iPSYCH, ADHD, Autism, Depression, and Schizophrenia, with a strong case-ascertainment. Across these psychiatric disorders, ADuLT identifies 20 independent genome-wide significant associations, case-control GWAS finds 17, and SPACox finds 8, which is consistent with simulation results. As more genetic data are being linked to electronic health records, robust GWAS methods that can make use of age-of-onset information will help increase power in analyses for common health outcomes
Risk of Early-Onset Depression Associated With Polygenic Liability, Parental Psychiatric History, and Socioeconomic Status
IMPORTANCE: Combining information on polygenic risk scores (PRSs) with other known risk factors could potentially improve the identification of risk of depression in the general population. However, to our knowledge, no study has estimated the association of PRS with the absolute risk of depression, and few have examined combinations of the PRS and other important risk factors, including parental history of psychiatric disorders and socioeconomic status (SES), in the identification of depression risk. OBJECTIVE: To assess the individual and joint associations of PRS, parental history, and SES with relative and absolute risk of early-onset depression. DESIGN, SETTING, AND PARTICIPANTS: This case-cohort study included participants from the iPSYCH2012 sample, a case-cohort sample of all singletons born in Denmark between May 1, 1981, and December 31, 2005. Hazard ratios (HRs) and absolute risks were estimated using Cox proportional hazards regression for case-cohort designs. EXPOSURES: The PRS for depression; SES measured using maternal educational level, maternal marital status, and paternal employment; and parental history of psychiatric disorders (major depression, bipolar disorder, other mood or psychotic disorders, and other psychiatric diagnoses). MAIN OUTCOMES AND MEASURES: Hospital-based diagnosis of depression from inpatient, outpatient, or emergency settings. RESULTS: Participants included 17 098 patients with depression (11 748 [68.7%] female) and 18 582 (9429 [50.7%] male) individuals randomly selected from the base population. The PRS, parental history, and lower SES were all significantly associated with increased risk of depression, with HRs ranging from 1.32 (95% CI, 1.29-1.35) per 1-SD increase in PRS to 2.23 (95% CI, 1.81-2.64) for maternal history of mood or psychotic disorders. Fully adjusted models had similar effect sizes, suggesting that these risk factors do not confound one another. Absolute risk of depression by the age of 30 years differed substantially, depending on an individual’s combination of risk factors, ranging from 1.0% (95% CI, 0.1%-2.0%) among men with high SES in the bottom 2% of the PRS distribution to 23.7% (95% CI, 16.6%-30.2%) among women in the top 2% of PRS distribution with a parental history of psychiatric disorders. CONCLUSIONS AND RELEVANCE: This study suggests that current PRSs for depression are not more likely to be associated with major depressive disorder than are other known risk factors; however, they may be useful for the identification of risk in conjunction with other risk factors