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.
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.
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.
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.
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
17546380
Web Services Discovery with DAML-QoS Ontology
As more and more Web services are deployed, Web serviceâ€™s discovery mechanisms become essential. Similar services from different sources often exhibit quite different Quality of Service (QoS) levels. For service selection and management purpose, it is necessary to explicitly, precisely, and flexibly specify various constraints and QoS metrics for Web services descriptions. This paper provides a novel ontology as a complement to DARPA Agent Markup Languageâ€”Service (DAML-S) Ontology to provide a better QoS metrics model. Three layers are defined, together with clear role descriptions for development. Cardinality constraints are utilized to describe the QoS property constraints. A matchmaking algorithm with multiple matching degrees is provided accordingly. When incorporated with DAML-S, the multiple service level objectives can be described by assigning multiple QoS profiles to one service profile. A prototype system was developed, which demonstrates the feasibility for small to medium-sized service advertisement repository.
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.
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.
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.