Designing and modeling of a multi-agent adaptive learning system (MAALS) using incremental hybrid case-based reasoning (IHCBR)

Several researches in the field of adaptive learning systems has developed systems and techniques to guide the learner and reduce cognitive overload, making learning adaptation essential to better understand preferences, the constraints and learning habits of the learner. Thus, it is particularly advisable to propose online learning systems that are able to collect and detect information describing the learning process in an automatic and deductive way, and to rely on this information to follow the learner in real time and offer him training according to his dynamic learning pace. This article proposes a multi-agent adaptive learning system to make a real decision based on a current learning situation. This decision will be made by performing a hypride cycle of the Case-Based Reasonning approach in order to follow the learner and provide him with an individualized learning path according to Felder Silverman learning style model and his learning traces to predict his future learning status. To ensure this decision, we assign at each stage of the Incremental Hybrid Case-Based Reasoning at least one active agent performing a particular task and a broker agent that collaborates between the different agents in the system

Inside the brain of an elite athlete: The neural processes that support high achievement in sports

Events like the World Championships in athletics and the Olympic Games raise the public profile of competitive sports. They may also leave us wondering what sets the competitors in these events apart from those of us who simply watch. Here we attempt to link neural and cognitive processes that have been found to be important for elite performance with computational and physiological theories inspired by much simpler laboratory tasks. In this way we hope to inspire neuroscientists to consider how their basic research might help to explain sporting skill at the highest levels of performance

Engineering Crowdsourced Stream Processing Systems

A crowdsourced stream processing system (CSP) is a system that incorporates crowdsourced tasks in the processing of a data stream. This can be seen as enabling crowdsourcing work to be applied on a sample of large-scale data at high speed, or equivalently, enabling stream processing to employ human intelligence. It also leads to a substantial expansion of the capabilities of data processing systems. Engineering a CSP system requires the combination of human and machine computation elements. From a general systems theory perspective, this means taking into account inherited as well as emerging properties from both these elements. In this paper, we position CSP systems within a broader taxonomy, outline a series of design principles and evaluation metrics, present an extensible framework for their design, and describe several design patterns. We showcase the capabilities of CSP systems by performing a case study that applies our proposed framework to the design and analysis of a real system (AIDR) that classifies social media messages during time-critical crisis events. Results show that compared to a pure stream processing system, AIDR can achieve a higher data classification accuracy, while compared to a pure crowdsourcing solution, the system makes better use of human workers by requiring much less manual work effort

A Mimetic Strategy to Engage Voluntary Physical Activity In Interactive Entertainment

We describe the design and implementation of a vision based interactive entertainment system that makes use of both involuntary and voluntary control paradigms. Unintentional input to the system from a potential viewer is used to drive attention-getting output and encourage the transition to voluntary interactive behaviour. The iMime system consists of a character animation engine based on the interaction metaphor of a mime performer that simulates non-verbal communication strategies, without spoken dialogue, to capture and hold the attention of a viewer. The system was developed in the context of a project studying care of dementia sufferers. Care for a dementia sufferer can place unreasonable demands on the time and attentional resources of their caregivers or family members. Our study contributes to the eventual development of a system aimed at providing relief to dementia caregivers, while at the same time serving as a source of pleasant interactive entertainment for viewers. The work reported here is also aimed at a more general study of the design of interactive entertainment systems involving a mixture of voluntary and involuntary control.Comment: 6 pages, 7 figures, ECAG08 worksho

Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution

Cloud controllers aim at responding to application demands by automatically scaling the compute resources at runtime to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of adaptation rules. However, for a cloud provider, applications running on top of the cloud infrastructure are more or less black-boxes, making it difficult at design time to define optimal or pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions often is delegated to the cloud application. Yet, in most cases, application developers in turn have limited knowledge of the cloud infrastructure. In this paper, we propose learning adaptation rules during runtime. To this end, we introduce FQL4KE, a self-learning fuzzy cloud controller. In particular, FQL4KE learns and modifies fuzzy rules at runtime. The benefit is that for designing cloud controllers, we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire. FQL4KE empowers users to specify cloud controllers by simply adjusting weights representing priorities in system goals instead of specifying complex adaptation rules. The applicability of FQL4KE has been experimentally assessed as part of the cloud application framework ElasticBench. The experimental results indicate that FQL4KE outperforms our previously developed fuzzy controller without learning mechanisms and the native Azure auto-scaling

Context-aware adaptation in DySCAS

DySCAS is a dynamically self-configuring middleware for automotive control systems. The addition of autonomic, context-aware dynamic configuration to automotive control systems brings a potential for a wide range of benefits in terms of robustness, flexibility, upgrading etc. However, the automotive systems represent a particularly challenging domain for the deployment of autonomics concepts, having a combination of real-time performance constraints, severe resource limitations, safety-critical aspects and cost pressures. For these reasons current systems are statically configured. This paper describes the dynamic run-time configuration aspects of DySCAS and focuses on the extent to which context-aware adaptation has been achieved in DySCAS, and the ways in which the various design and implementation challenges are met