971957
DAML-QoS ontology for Web services
As more and more Web services are deployed, Web service's discovery mechanisms become essential. Similar services can have quite different QoS levels. For service selection and management purpose, it is necessary to explicitly, precisely, and unambiguously specify various constraints and QoS metrics for Web services descriptions. This paper provides a novel DAML-QoS ontology as a complement for DAML-S ontology to provide a better QoS metrics model. Three layers are defined together with clear role descriptions for developments. Cardinality constraints are utilized to describe the QoS property constraints. Basic profile is presented for general Web service's description and the speed startup of ontology definition. Matchmaking algorithm for QoS property constraints is presented and different matching degrees are described. When incorporated with DAML-S, multiple service levels can be described through attaching multiple QoS profiles to one service profile. Well-defined Metrics can be further utilized by measurement organizations to guarantee the promised service level.
1982773
QoS measurement issues with DAML-QoS ontology
With the industry's efforts in promoting the use of Web services, a huge number of Web services are being developed for the Web. To improve the service provider's competition advantage, it is necessary to support QoS-aware service discovery mechanism as well as the corresponding measurement system. In this paper we present a QoS measurement framework based on the DAML-QoS ontology, together with the measurement code generator. The DAML-QoS ontology is designed to specify QoS constraints for Web services. It works as a complement to DAML-S to provide QoS-aware service discovery. This paper focuses on its measurement framework to check the service provider's runtime compliance with the advertised service level. The code generator helps to generate the measurement code according to the DAML-QoS specification. This ontology level specification tries to reduce the service's integration complexity and achieve better interoperability, automation and extensibility
9173348
Particle Swarm Optimization for Simultaneous Optimization of Design and Machining Tolerances
Tolerance assignment is an important issue in product design and manufacturing. However, this problem is commonly formulated as nonlinear, multi-variable and high constrained model. Most of the heuristics for this problem are based on penalty function strategy which unfortunately suffers from inherent drawbacks. To overcome these drawbacks, this paper presented a new powerful tool-Particle Swarm Optimization algorithm (PSO) and meanwhile proposed a sophisticated constraints handling scheme suitable for the optimization mechanism of PSO. An example involving simultaneously assigning both design and machining tolerances based on optimum total machining cost is employed to demonstrate the efficiency and effectiveness of the proposed approach. The experimental result based on the comparison between PSO and GA show that the new PSO model is a powerful tool.
15559973
Robo-Erectus: a low-cost autonomous humanoid soccer robot
The humanoid soccer robot league is a new international initiative to foster robotics and AI technologies using soccer games [1]. This paper provides a brief description of a low-cost autonomous humanoid soccer robot called Robo-Erectus (RE), which has been developed in the Center for Advanced Robotics and Intelligent Control (ARICC) at Singapore Polytechnic since 2001. To develop a low-cost humanoid platform which is affordable for many researchers, our RE humanoid robots have been intentionally designed using low-torque servo motors and low-precision mechanical structures so that the cost can be significantly reduced. We demonstrate that the humanoid biped locomotion can be formulated as an optimization problem with consideration of some constraints. In the frame of gait synthesis tied with constraint functions, computational learning methods, e.g. fuzzy reinforcement learning (FRL), can be incorporated to further improve the gait [2]. The proposed control and learning methods have been successfully tested on our RE humanoid soccer robots which finished second in the RoboCup 2002 Humanoid Walk competition and first in the RoboCup 2003 Humanoid Free Performance competition.
1882154
Providing Quality of Service in IEEE 802.11 WLAN
This paper aims to provide quality of service (QoS) for IEEE 802.11 wireless local area network (WLAN). Specifically, we develop a new scheduling algorithm in link layer to support multimedia services with guaranteed QoS in WLAN. The proposed scheduling algorithm makes use of the idle system time to reduce average packet loss of real-time (RT) variable-bit-rate (VBR) services, while satisfying required transmission delay and bandwidth requirements of all RT services. Detailed analysis is given by taking into account several key statistical factors, such as service traffic model, the ratio between the polling period and the contention period (CP), burst generation probabilities. Performance is evaluated through OPNET simulation. Simulation results demonstrate that our proposed joint scheme is effective in terms of both system resource utilization and QoS provisioning
45131393
Reinforcement learning with fuzzy evaluative feedback for a biped robot
Proposes a fuzzy reinforcement learning algorithm for biped gait synthesis. It is based on a modified GARIC (generalized approximate reasoning for intelligent control) architecture that can accept fuzzy evaluative feedback rather than a numerical one. The proposed gait synthesizer forms the initial gait from intuitive balancing knowledge, and it is then trained by the fuzzy reinforcement learning algorithm that uses a fuzzy critical signal to evaluate the degree of success for the biped dynamic walking by means of the zero moment point. The performance and applicability of the proposed method are illustrated through biped simulation.
12769414
Robot learning with GA-based fuzzy reinforcement learning agents
Abstract How to learn from both expert knowledge and measurement-based information for a robot to acquire perception and motor skills is a challenging research topic in the field of autonomous robotic systems. For this reason, a general GA (genetic algorithm)-based fuzzy reinforcement learning (GAFRL) agent is proposed in this paper. We first characterize the robot learning problem and point out some major issues that need to be addressed in conjunction with reinforcement learning. Based on a neural fuzzy network architecture of the GAFRL agent, we then discuss how different kinds of expert knowledge and measurement-based information can be incorporated in the GAFRL agent so as to accelerate its learning. By making use of the global optimization capability of GAs, the GAFRL can solve the local minima problem in traditional actor-critic reinforcement learning. On the other hand, with the prediction capability of the critic network, GAs can evaluate the candidate solutions regularly even during the periods without external feedback from the environment. This can guide GAs to perform a more effective global search. Finally, different types of GAFRL agents are constructed and verified using the simulation model of a physical biped robot.