8503144
Information Integration for Robot Learning Using Neural Fuzzy Systems
How to learn from both sensory data (numerical) and a prior knowledge (linguistic) for a robot to acquire perception and motor skills is a challenging problem in the field of autonomous robotic systems. To make the most use of the information available for robot learning, linguistic and numerical heterogeneous dada (LNHD) integration is firstly investigated in the frame of the fuzzy data fusion theory. With neural systems' unique capabilities of dealing with both linguistic information and numerical data, the LNHD can be translated into an initial structure and parameters and then robots start from this configuration to further improve their behaviours. A neural-fuzzy-architecture-based reinforcement learning agent is finally constructed and verified using the simulation model of a physical biped robot. It shows that by incorporation of various kids of LNHD on human gait synthesis and walking evaluation the biped learning rate for gait synthesis can be tremendously improved.
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.
7697882
Incorporating Perception-Based Information in Reinforcement Learning Using Computing with Words
In this paper, a general fuzzy reinforcement learning (FRL) agent that can utilise not only measurement-based information but also perception-based information by means of computing with words (CW) is proposed. By introducing fuzzy numbers and their arithmetic operations and fuzzy Lyapunov synthesis in the domain of CW, a set of stable fuzzy control rules can be derived from perception-based information. Moreover, based on a neurofuzzy network architecture, the fuzzy rules can be incorporated in the FRL agent to initialise its action network, critic network and evaluation feedback module so as to improve the learning. The performance and applicability of the proposed approach are illustrated through the practical implementation of learning control of an autonomous pole-balancing mobile robot.
9275481
RoboCup 2004 Competitions and Symposium: A Small Kick for Robots, a Giant Score for Science
RoboCup is an international initiative with the main goals of fostering research and education in artificial intelligence and robotics, as well as of promoting science and technology to world citizens. The idea behind RoboCup is to provide a standard problem for which a wide range of technologies can be integrated and examined, as well as being used for project-oriented education, and to organize annual events open to the general public, at which different solutions to the problem are compared. The eighth annual RoboCup -- RoboCup 2004 -- was held in Lisbon, Portugal, from 27 June to 5 July. In this article, a general description of RoboCup 2004 is presented, including summaries concerning teams, participants, distribution into leagues, main research advances, as well as detailed descriptions for each league.
33132446
Fuzzy control rules extraction from perception-based information using computing with words
In this paper, a novel approach to extract fuzzy control rules from perception-based information using computing with words (CW) is firstly presented. The approach is based on applying fuzzy numbers and their arithmetic operations. The fuzzy Lyapunov synthesis, which gives a linguistic description on the plant and the control objective, is used to design a stable fuzzy controller. Secondly, that the traditional fuzzy-control rules can be derived systematically, rather than heuristically, and their stability can be guaranteed is demonstrated. Moreover, by introducing fuzzy numbers and their arithmetic operations, the "words" represented by fuzzy numbers could be efficiently manipulated for fuzzy controller design. The performance and applicability of the proposed method are illustrated through the practical implementation of fuzzy control of a pole-balancing vehicle.
40603933
Integration of linguistic and numerical information for biped control
Bipedal locomotion is an important hallmark of human evolution. Despite of complex control systems, human locomotion is characterized by smooth, regular, and repeating movements. Therefore, there is the potential for applying human locomotion strategies and any knowledge available to the biped control. In order to make the most use of the information available, a linguistic-numerical integration-based biped control method is proposed in this paper. The numerical data from biped measuring instruments, and the linguistic rules obtained from intuitive walking knowledge and biomechanics study have been classified into four categories: direct rules, direct data, indirect rules, and indirect data. Based on inverse learning and data fusion theory, two simple and intuitive integration schemes are proposed to integrate linguistic and numerical information with various forms, such as direct and indirect. One is neurofuzzy-based integration, and another is fuzzy rules extraction-based integration. The simulation results show that the biped gait and joint control performance can be significantly improved by the prescribed synergy method-based neurofuzzy gait synthesis and fuzzy rules extraction-based joint control strategies using linguistic and numerical integrated information.
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
16619155
QoS-Aware and Federated Enhancement for UDDI
With more and more Web services appearing on the Web, Web serviceâ€™s discovery mechanisms become essential. UDDI is an online registry standard to facilitate the discovery of business partners and services. Service requesters can choose good performance Web services by manual test and comparison. However, this method of choosing is inefficient and costly in terms of time and money. The ability to predict the quality of service is missing in UDDI. Furthermore, UDDI registries in federated organizations should be able to share more about the service requesterâ€™s connection information to make better predictions for servicesâ€™ performances. To address these problems, we propose UX (UDDI eXtension), a system that facilitates requesters to discover services with good performance. In each enterprise domain, the requestersâ€™ QoS feedback is received and then stored in a local UX server. By sharing these experiences from all the requesters in the local domain, the system can predict the serviceâ€™s future performance. To support the discovery between different cooperation domains, we design a general federated service and then enhance the UX systemâ€™s federated discovery ability based on this federated service. The system handles the federated inquiry and provides a simple view over the whole federation. Meanwhile the UX serverâ€™s inquiry interface will still conform to the UDDI Specification.
2612705
Credit Scoring Model Based on Neural Network with Particle Swarm Optimization
Credit scoring has gained more and more attentions both in academic world and the business community today. Many modeling techniques have been developed to tackle the credit scoring tasks. This paper presents a Structure-tuning Particle Swarm Optimization (SPSO) approach for training feed-forward neural networks (NNs). The algorithm is successfully applied to a real credit problem. By simultaneously tuning the structure and connection weights of NNs, the proposed algorithm generates optimized NNs with problem-matched information processing capacity and it also eliminates some ill effects introduced by redundant input features and the corresponding redundant structure. Compared with BP and GA, SPSO can improve the pattern classification accuracy of NNs while speeding up the convergence of training process.
1105765
Service discovery and measurement based on DAML-QoS ontology
As more and more Web services are deployed, Web service's discovery mechanisms become essential. Similar services can have quite different QoS behaviors. For service selection and management purpose, it is necessary to clearly specify QoS constraints and metrics definitions for Web services. We investigate on the semantic QoS specification and introduce our design principles on it. Based on the specification refinement and conformance, we introduce the QoS matchmaking algorithm with multiple matching degrees. The matchmaking prototype is designed to prove the feasibility. Well-defined Metrics can be further utilized by measurement organizations to monitor and evaluate the promised service level objectives.