470,136 research outputs found
Requirements Problem and Solution Concepts for Adaptive Systems Engineering, and their Relationship to Mathematical Optimisation, Decision Analysis, and Expected Utility Theory
Requirements Engineering (RE) focuses on eliciting, modelling, and analyzing
the requirements and environment of a system-to-be in order to design its
specification. The design of the specification, usually called the Requirements
Problem (RP), is a complex problem solving task, as it involves, for each new
system-to-be, the discovery and exploration of, and decision making in, new and
ill-defined problem and solution spaces. The default RP in RE is to design a
specification of the system-to-be which (i) is consistent with given
requirements and conditions of its environment, and (ii) together with
environment conditions satisfies requirements. This paper (i) shows that the
Requirements Problem for Adaptive Systems (RPAS) is different from, and is not
a subclass of the default RP, (ii) gives a formal definition of RPAS, and (iii)
discusses implications for future research
Slow Adaptive OFDMA Systems Through Chance Constrained Programming
Adaptive OFDMA has recently been recognized as a promising technique for
providing high spectral efficiency in future broadband wireless systems. The
research over the last decade on adaptive OFDMA systems has focused on adapting
the allocation of radio resources, such as subcarriers and power, to the
instantaneous channel conditions of all users. However, such "fast" adaptation
requires high computational complexity and excessive signaling overhead. This
hinders the deployment of adaptive OFDMA systems worldwide. This paper proposes
a slow adaptive OFDMA scheme, in which the subcarrier allocation is updated on
a much slower timescale than that of the fluctuation of instantaneous channel
conditions. Meanwhile, the data rate requirements of individual users are
accommodated on the fast timescale with high probability, thereby meeting the
requirements except occasional outage. Such an objective has a natural chance
constrained programming formulation, which is known to be intractable. To
circumvent this difficulty, we formulate safe tractable constraints for the
problem based on recent advances in chance constrained programming. We then
develop a polynomial-time algorithm for computing an optimal solution to the
reformulated problem. Our results show that the proposed slow adaptation scheme
drastically reduces both computational cost and control signaling overhead when
compared with the conventional fast adaptive OFDMA. Our work can be viewed as
an initial attempt to apply the chance constrained programming methodology to
wireless system designs. Given that most wireless systems can tolerate an
occasional dip in the quality of service, we hope that the proposed methodology
will find further applications in wireless communications
Runtime Requirements Monitoring Framework for Adaptive e-Learning Systems
International audienceAs academic learners and companies are turning to e-learning courses to achieve their personal and professional goals, it becomes more and more important to handle service quality in this sector. Despite scientific research conducted to personalize the learning process and meet learner's requirements under adaptive e-learning systems, however, the specification and management of quality attribute is particularly challenging due to problems arising from environmental variability. In our view, a detailed and high-level specification of requirements supported through the whole system lifecycle is needed for a comprehensive management of adaptive e-learning systems, especially in continuously changing environmental conditions. In this paper, we propose a runtime requirements monitoring to check the conformity of adaptive e-learning systems to their requirements and ensure that the activities offered by these learning environments can achieve the desired learning outcomes. As a result, when deviations (i.e., not satisfied requirements) occur, they are identified and then notified during system operation. With our approach, the requirements are supported during the whole system lifecycle. First, we specify system's requirements in the form of a dynamic software product line. This specification applies a novel requirements engineering language that combines goal-driven requirements with features and claims and avoid the enumeration of all desired adaptation strategies (i.e. when an adaptation should be applied) at the design time. Second, the specification is automatically transformed into a constraint satisfaction problem that reduces the requirements monitoring into a constraint program at runtime
Final Report - Regulatory Considerations for Adaptive Systems
This report documents the findings of a preliminary research study into new approaches to the software design assurance of adaptive systems. We suggest a methodology to overcome the software validation and verification difficulties posed by the underlying assumption of non-adaptive software in the requirementsbased- testing verification methods in RTCA/DO-178B and C. An analysis of the relevant RTCA/DO-178B and C objectives is presented showing the reasons for the difficulties that arise in showing satisfaction of the objectives and suggested additional means by which they could be satisfied. We suggest that the software design assurance problem for adaptive systems is principally one of developing correct and complete high level requirements and system level constraints that define the necessary system functional and safety properties to assure the safe use of adaptive systems. We show how analytical techniques such as model based design, mathematical modeling and formal or formal-like methods can be used to both validate the high level functional and safety requirements, establish necessary constraints and provide the verification evidence for the satisfaction of requirements and constraints that supplements conventional testing. Finally the report identifies the follow-on research topics needed to implement this methodology
Artificial Intelligence (AI), Operations Research (OR), and Decision Support Systems (DSS): A conceptual framework
In recent years there has been increasing interest in applying the computer based problem solving techniques of Artificial Intelligence (AI), Operations Research (OR), and Decision Support Systems (DSS) to analyze extremely complex problems. A conceptual framework is developed for successfully integrating these three techniques. First, the fields of AI, OR, and DSS are defined and the relationships among the three fields are explored. Next, a comprehensive adaptive design methodology for AI and OR modeling within the context of a DSS is described. These observations are made: (1) the solution of extremely complex knowledge problems with ill-defined, changing requirements can benefit greatly from the use of the adaptive design process, (2) the field of DSS provides the focus on the decision making process essential for tailoring solutions to these complex problems, (3) the characteristics of AI, OR, and DSS tools appears to be converging rapidly, and (4) there is a growing need for an interdisciplinary AI/OR/DSS education
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