22,191 research outputs found
Proscriptive Bayesian Programming Application for Collision Avoidance
Evolve safely in an unchanged environment
and possibly following an optimal trajectory is one big
challenge presented by situated robotics research field. Collision
avoidance is a basic security requirement and this
paper proposes a solution based on a probabilistic approach
called Bayesian Programming. This approach aims to deal
with the uncertainty, imprecision and incompleteness of the
information handled. Some examples illustrate the process
of embodying the programmer preliminary knowledge into
a Bayesian program and experimental results of these examples
implementation in an electrical vehicle are described
and commented. Some videos illustrating these experiments
can be found at http://www-laplace.imag.fr
Obstacle Avoidance and Proscriptive Bayesian Programming
Unexpected events and not modeled properties of the robot environment are some of
the challenges presented by situated robotics research field. Collision avoidance is a basic security
requirement and this paper proposes a probabilistic approach called Bayesian Programming, which
aims to deal with the uncertainty, imprecision and incompleteness of the information handled to
solve the obstacle avoidance problem. Some examples illustrate the process of embodying the
programmer preliminary knowledge into a Bayesian program and experimental results of these
examples implementation in an electrical vehicle are described and commented. A video illustration
of the developed experiments can be found at http://www.inrialpes.fr/sharp/pub/laplac
A model-driven approach to broaden the detection of software performance antipatterns at runtime
Performance antipatterns document bad design patterns that have negative
influence on system performance. In our previous work we formalized such
antipatterns as logical predicates that predicate on four views: (i) the static
view that captures the software elements (e.g. classes, components) and the
static relationships among them; (ii) the dynamic view that represents the
interaction (e.g. messages) that occurs between the software entities elements
to provide the system functionalities; (iii) the deployment view that describes
the hardware elements (e.g. processing nodes) and the mapping of the software
entities onto the hardware platform; (iv) the performance view that collects
specific performance indices. In this paper we present a lightweight
infrastructure that is able to detect performance antipatterns at runtime
through monitoring. The proposed approach precalculates such predicates and
identifies antipatterns whose static, dynamic and deployment sub-predicates are
validated by the current system configuration and brings at runtime the
verification of performance sub-predicates. The proposed infrastructure
leverages model-driven techniques to generate probes for monitoring the
performance sub-predicates and detecting antipatterns at runtime.Comment: In Proceedings FESCA 2014, arXiv:1404.043
Towards Design Principles for Data-Driven Decision Making: An Action Design Research Project in the Maritime Industry
Data-driven decision making (DDD) refers to organizational decision-making practices that emphasize the use of data and statistical analysis instead of relying on human judgment only. Various empirical studies provide evidence for the value of DDD, both on individual decision maker level and the organizational level. Yet, the path from data to value is not always an easy one and various organizational and psychological factors mediate and moderate the translation of data-driven insights into better decisions and, subsequently, effective business actions. The current body of academic literature on DDD lacks prescriptive knowledge on how to successfully employ DDD in complex organizational settings. Against this background, this paper reports on an action design research study aimed at designing and implementing IT artifacts for DDD at one of the largest ship engine manufacturers in the world. Our main contribution is a set of design principles highlighting, besides decision quality, the importance of model comprehensibility, domain knowledge, and actionability of results
Fusion of Information and Analytics: A Discussion on Potential Methods to Cope with Uncertainty in Complex Environments (Big Data and IoT)
International audienceInformation overload and complexity are core problems to most organizations of today. The advances in networking capabilities have created the conditions of complexity by enabling richer, real-time interactions between and among individuals, objects, systems and organizations. Fusion of Information and Analytics Technologies (FIAT) are key enablers for the design of current and future decision support systems to support prognosis, diagnosis, and prescriptive tasks in such complex environments. Hundreds of methods and technologies exist, and several books have been dedicated to either analytics or information fusion so far. However, very few have discussed the methodological aspects and the need of integrating frameworks for these techniques coming from multiple disciplines. This paper presents a discussion of potential integrating frameworks as well as the development of a computational model to evolve FIAT-based systems capable of meeting the challenges of complex environments such as in Big Data and Internet of Things (IoT)
An introduction to STRIKE : STRuctured Interpretation of the Knowledge Environment
Knowledge forms a critical part of the income generation of the system and the complex environment in which actors participate in the creation of knowledge assets merits robust, eclectic consideration. STRIKE - STRuctured Interpretation of the Knowledge Environment affords an unobtrusive and systematic framework to observe, record, evaluate and articulate concrete and abstract elements of a setting, across internal and external dimensions. Inter-relationships between actor and environment are preserved.
STRIKE is supported by underlying techniques to enrich data and enhance the authenticity of its representation. Adoption of photography and videography tools provides illustrative and interpretive benefits and facilitates researcher reflexivity. This structured approach to data analysis and evaluation mitigates criticisms of methodological rigour in observational research and affords standardisation potential, germane for application in a verification or longitudinal capacity.
Advancing exploratory validation studies, the method is employed to evaluate the knowledge environments of two enterprises in the UK creative sector. These occupy a critical role in fostering entrepreneurial innovation alongside participant self-efficacy. Access Space in Sheffield and the Bristol Hackspace are committed to open software, open knowledge and open participation; sharing peer learning, creativity and socio-technical aims to address broadly similar community needs.
Drawing on Wittgenstein’s Picture Theory of Meaning, the knowledge management perspective is abstracted from the STRIKE assessment. It is argued that the tiered analytical approach which considers a breadth of dimensions enhances representation and interpretation of the knowledge environment and presents a diagnostic and prescriptive capability to actualise change. The paper concludes by evaluating framework effectiveness, findings application and future direction
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