1,204 research outputs found
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
Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R
This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud
To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud
A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems
Autonomy in the real real-world: A behaviour based view of autonomous systems control in an industrial product inspection system
The thesis presented in this dissertation appears in two sequential parts that arose from an exploration of the use of Behaviour Based Artificial Intelligence (BBAI) techniques in a domain outside that of robotics, where BBAI is most frequently used. The work details a real-world physical implementation of the control and interactions of an industrial product inspection system from a BBAI perspective. It concentrates particularly on the control of a number of active laser scanning sensor systems (each a subsystem of a larger main inspection system), using a subsumption architecture. This industrial implementation is in itself a new direction for BBAI control and an important aspect of this thesis. However, the work has also led on to the development of a number of key ideas which contribute to the field of BBAI in general. The second part of the thesis concerns the nature of physical and temporal constraints on a distributed control system and the desirability of utilising mechanisms to provide continuous, low-level learning and adaptation of domain knowledge on a sub-behavioural basis. Techniques used include artificial neural networks and hill-climbing state-space search algorithms. Discussion is supported with examples from experiments with the laser scanning inspection system.
Encouraging results suggest that concerted design effort at this low level of activity will benefit the whole system in terms of behavioural robustness and reliability. Relevant aspects of the design process that should be of value in similar real-world projects are identified and emphasised. These issues are particularly important in providing a firm foundation for artificial intelligence based control systems
Reinforcement Learning
Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field
A brief review of neural networks based learning and control and their applications for robots
As an imitation of the biological nervous systems, neural networks (NN), which are characterized with powerful learning ability, have been employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification and patterns recognition etc. This article aims to bring a brief review of the state-of-art NN for the complex nonlinear systems. Recent progresses of NNs in both theoretical developments and practical applications are investigated and surveyed. Specifically, NN based robot learning and control applications were further reviewed, including NN based robot manipulator control, NN based human robot interaction and NN based behavior recognition and generation
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