21,218 research outputs found
Detecting execution failures using learned action models
Planners reason with abstracted models of the behaviours they use to construct plans. When plans are turned into the instructions that drive an executive, the real behaviours interacting with the unpredictable uncertainties of the environment can lead to failure. One of the challenges for intelligent autonomy is to recognise when the actual execution of a behaviour has diverged so far from the expected behaviour that it can be considered to be a failure. In this paper we present an approach by which a trace of the execution of a behaviour is monitored by tracking its most likely explanation through a learned model of how the behaviour is normally executed. In this way, possible failures are identified as deviations from common patterns of the execution of the behaviour. We perform an experiment in which we inject errors into the behaviour of a robot performing a particular task, and explore how well a learned model of the task can detect where these errors occur
Using learned action models in execution monitoring
Planners reason with abstracted models of the behaviours they use to construct plans. When plans are turned into the instructions that drive an executive, the real behaviours interacting with the unpredictable uncertainties of the environment can lead to failure. One of the challenges for intelligent autonomy is to recognise when the actual execution of a behaviour has diverged so far from the expected behaviour that it can be considered to be a failure. In this paper we present further developments of the work described in (Fox et al. 2006), where models of behaviours were learned as Hidden Markov Models. Execution of behaviours is monitored by tracking the most likely trajectory through such a learned model, while possible failures in execution are identified as deviations from common patterns of trajectories within the learned models. We present results for our experiments with a model learned for a robot behaviour
Autonomic care platform for optimizing query performance
Background: As the amount of information in electronic health care systems increases, data operations get more complicated and time-consuming. Intensive Care platforms require a timely processing of data retrievals to guarantee the continuous display of recent data of patients. Physicians and nurses rely on this data for their decision making. Manual optimization of query executions has become difficult to handle due to the increased amount of queries across multiple sources. Hence, a more automated management is necessary to increase the performance of database queries. The autonomic computing paradigm promises an approach in which the system adapts itself and acts as self-managing entity, thereby limiting human interventions and taking actions. Despite the usage of autonomic control loops in network and software systems, this approach has not been applied so far for health information systems.
Methods: We extend the COSARA architecture, an infection surveillance and antibiotic management service platform for the Intensive Care Unit (ICU), with self-managed components to increase the performance of data retrievals. We used real-life ICU COSARA queries to analyse slow performance and measure the impact of optimizations. Each day more than 2 million COSARA queries are executed. Three control loops, which monitor the executions and take action, have been proposed: reactive, deliberative and reflective control loops. We focus on improvements of the execution time of microbiology queries directly related to the visual displays of patients' data on the bedside screens.
Results: The results show that autonomic control loops are beneficial for the optimizations in the data executions in the ICU. The application of reactive control loop results in a reduction of 8.61% of the average execution time of microbiology results. The combined application of the reactive and deliberative control loop results in an average query time reduction of 10.92% and the combination of reactive, deliberative and reflective control loops provides a reduction of 13.04%.
Conclusions: We found that by controlled reduction of queries' executions the performance for the end-user can be improved. The implementation of autonomic control loops in an existing health platform, COSARA, has a positive effect on the timely data visualization for the physician and nurse
Vision-based deep execution monitoring
Execution monitor of high-level robot actions can be effectively improved by
visual monitoring the state of the world in terms of preconditions and
postconditions that hold before and after the execution of an action.
Furthermore a policy for searching where to look at, either for verifying the
relations that specify the pre and postconditions or to refocus in case of a
failure, can tremendously improve the robot execution in an uncharted
environment. It is now possible to strongly rely on visual perception in order
to make the assumption that the environment is observable, by the amazing
results of deep learning. In this work we present visual execution monitoring
for a robot executing tasks in an uncharted Lab environment. The execution
monitor interacts with the environment via a visual stream that uses two DCNN
for recognizing the objects the robot has to deal with and manipulate, and a
non-parametric Bayes estimation to discover the relations out of the DCNN
features. To recover from lack of focus and failures due to missed objects we
resort to visual search policies via deep reinforcement learning
Automatic Software Repair: a Bibliography
This article presents a survey on automatic software repair. Automatic
software repair consists of automatically finding a solution to software bugs
without human intervention. This article considers all kinds of repairs. First,
it discusses behavioral repair where test suites, contracts, models, and
crashing inputs are taken as oracle. Second, it discusses state repair, also
known as runtime repair or runtime recovery, with techniques such as checkpoint
and restart, reconfiguration, and invariant restoration. The uniqueness of this
article is that it spans the research communities that contribute to this body
of knowledge: software engineering, dependability, operating systems,
programming languages, and security. It provides a novel and structured
overview of the diversity of bug oracles and repair operators used in the
literature
Prototype of Fault Adaptive Embedded Software for Large-Scale Real-Time Systems
This paper describes a comprehensive prototype of large-scale fault adaptive
embedded software developed for the proposed Fermilab BTeV high energy physics
experiment. Lightweight self-optimizing agents embedded within Level 1 of the
prototype are responsible for proactive and reactive monitoring and mitigation
based on specified layers of competence. The agents are self-protecting,
detecting cascading failures using a distributed approach. Adaptive,
reconfigurable, and mobile objects for reliablility are designed to be
self-configuring to adapt automatically to dynamically changing environments.
These objects provide a self-healing layer with the ability to discover,
diagnose, and react to discontinuities in real-time processing. A generic
modeling environment was developed to facilitate design and implementation of
hardware resource specifications, application data flow, and failure mitigation
strategies. Level 1 of the planned BTeV trigger system alone will consist of
2500 DSPs, so the number of components and intractable fault scenarios involved
make it impossible to design an `expert system' that applies traditional
centralized mitigative strategies based on rules capturing every possible
system state. Instead, a distributed reactive approach is implemented using the
tools and methodologies developed by the Real-Time Embedded Systems group.Comment: 2nd Workshop on Engineering of Autonomic Systems (EASe), in the 12th
Annual IEEE International Conference and Workshop on the Engineering of
Computer Based Systems (ECBS), Washington, DC, April, 200
Adaptive Process Management in Cyber-Physical Domains
The increasing application of process-oriented approaches in new challenging cyber-physical domains beyond business computing (e.g., personalized healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex processes in such domains. A cyber-physical domain is characterized by the presence of a cyber-physical system coordinating heterogeneous ICT components (PCs, smartphones, sensors, actuators) and involving real world entities (humans, machines, agents, robots, etc.) that perform complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and processes enacted in cyber-physical domains must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this chapter, we tackle the above issue and we propose a general approach, a concrete framework and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events. The adaptation mechanism provided by SmartPM is based on declarative task specifications, execution monitoring for detecting failures and context changes at run-time, and automated planning techniques to self-repair the running process, without requiring to predefine any specific adaptation policy or exception handler at design-time
- …