30064492
Genetic reinforcement learning approach to the machine scheduling problem
This paper focuses on the development of a learning-based heuristic for the machine scheduling problem, which automatically captures the search control knowledge or the common features of good schedules while generating a number of schedules. Defining states and actions of the machine shop, the machine scheduling problem is transformed into a problem of reinforcement learning (RL) in which a learner or a scheduler will learn to select the right action at each state of the machine shop, using the reward from a schedule evaluator for executing the action. Implementing the proposed reinforcement learning with a genetic algorithm results in the genetic reinforcement learning (GRL) approach to the machine scheduling problem. Although the learning-based heuristic has the overhead of acquiring knowledge on the problem, it can be easily adapted for a wide variety of machine scheduling problems due to the weak dependence on the problem structures and objectives. A GRL-based scheduler, called EVIS (Evolutionary Intracell Scheduler), has been developed and applied to various classes of machine scheduling problems, such as the job-shop scheduling, the flow-shop scheduling and the open-shop scheduling problems, and even the processor scheduling problem, the performance evaluation of EVIS with a number of different problem instances has shown that the learning-based heuristic is robust and its performance is comparable with that of other problem-specific heuristics or search-oriented heuristics in the quality of solutions.
1173830
A genetic-algorithm-based neighbor-selection strategy for hybrid peer-to-peer networks
BitTorrent is a popular, open-source, hybrid peer-to-peer content distribution system that is conducive for distribution of large-volume contents. In this paper, we propose a genetic-algorithm-based neighbor-selection strategy for hybrid peer-to-peer networks, which enhances the decision process performed at the tracker for transfer coordination. We also investigate how the strategy affects system throughput and distribution efficiency as well as peer contributions. We show through computer simulations that by increasing content availability to the clients from their immediate neighbors, it can significantly improve the system performance without trading off users' satisfaction. The proposed strategy can significantly improve the efficiency of distribution, especially for low-connectivity peers, and it is suitable to deploy for online decisions
17815023
Experimental study on adaptive control of underwater robots
The control of underwater robots presents a number of unique and formidable challenges. Underwater robot dynamics are highly nonlinear, coupled, and time-varying, and subject to hydrodynamic uncertainties and external disturbances such as current. Unlike land mobile robots, underwater robots cannot use GPS. Most popular underwater positioning sensors are sonar-based, such as long-base line. However, autonomous processing of sonar measurements is plagued by noise, drop-outs, missed detection, false reading, poor resolution, etc. The paper presents a new adaptive control of underwater robots with sonar-based position measurements. Experimental results show robustness of the control system in the presence of unmodelled dynamics and various noise.
41633235
Automatic generation of error recovery knowledge through learned reactivity
Develops a new method for automatic generation of error recovery procedures in the assembly workcell. Current reactive systems have integrated planning and learning capabilities to allow them to augment their store of reactive behaviors whenever they are forced to plan. The cycle of evaluation and reaction used by these systems implicitly detects and corrects any errors made in prior behavior execution. By capitalizing on this analogy to error recovery, the authors are able to gain the benefits of a reactive system for use in error recovery while minimizing the perception and evaluation burden that is inherent in those approaches. The authors present a system that uses the new learning reactive paradigm to quickly correct known errors and autonomously learn to recover new errors without human intervention.<<ETX>>
12503943
Model-based visual feedback control for a hand-eye coordinated robotic system
The integration of a single camera into a robotic system to control the relative position and orientation between the robot's end-effector and a moving part in real time is discussed. Only monocular vision techniques are considered because of current limitations in the speed of computer vision analysis. The approach uses geometric models of both the part and the camera, as well as the extracted image features, to generate the appropriate robot control signals for tracking. Part and camera models are also used during the teaching stage to predict important image features that appear during task completion.<<ETX>>
33340433
An evolutionary approach to the job-shop scheduling problem
This paper focuses on the heuristic hybridization and the genetic search as a methodology to develop a computationally efficient heuristic for the job-shop scheduling problem (JSP). In order to adapt the JSP to a genetic algorithm (GA), the ASGPL (Active-Schedule Generation with a Priority-List) algorithm with a hopping scheme was proposed, and using a GA, an iterative schedule improvement procedure called EVIS (Evolutionary Intracell Scheduler) was designed. The genetic search in EVIS was parallelized with a model of subpopulations and migration. Without implementing any problem-tailored heuristic for the job-shop scheduling problem, EVIS was able to find optimal solutions to a number of different problem instances in reasonable computation time.<<ETX>>
33337266
A genetic fuzzy agent using ontology model for meeting scheduling system
A Genetic Fuzzy Agent (GFA) using the ontology model for Meeting Scheduling System (MSS) is presented in this paper. The ontology model includes the Fuzzy Meeting Scheduling Ontology (FMSO) and the Fuzzy Personal Ontology (FPO) that can support to construct the knowledge base of the GFA. The FMSO is utilized to record and describe the meeting scheduling domain knowledge for the GFA. In addition, we implement a FMSO editor for generating the Web Ontology Language, OWL, that will be utilized by the GFA. Furthermore, the GFA will infer the suitable meeting time slots based on the ontology model. Moreover, it also adjusts the FMSO and FPO based on the results of the genetic learning mechanism for the next meeting. The experimental results show that our approach can effectively work for MSS.
32239506
An intelligent fuzzy agent for meeting scheduling decision support system
Abstract An Intelligent Fuzzy Agent for Meeting Scheduling Decision Support System is proposed in this paper. The proposed Intelligent Fuzzy Agent including Meeting Negotiation Agent, Fuzzy Inference Agent and Genetic Learning Agent can search and decide the suitable meeting time for the specified meeting in an organization. When a meeting host requests a meeting, the Meeting Negotiation Agent immediately sends the invitees’ names to Meeting Scheduling Decision Support System for retrieving their schedules from Group Calendar Data Base, then Meeting Scheduling Decision Support System will compute the possible meeting time and respond the results to Meeting Negotiation Agent. Moreover, the Fuzzy Inference Agent infers the adequate meeting time based on the information provided by Meeting Negotiation Agent and Personalized Knowledge Base, and sends the computing result back to Meeting Scheduling Decision Support System. The meeting host will decide the final meeting time based on Meeting Scheduling Decision Support System and announce the meeting information by various devices including PDA, WAP, FAX, or E-mail. Furthermore, the invitees’ decisions for attending the meeting or not will be stored into Meeting Information Knowledge Base, then the Genetic Learning Agent will adjust the Personalized Knowledge Base for the next meeting. By the experimental results, the proposed Intelligent Fuzzy Agent can work efficiently and effectively for Meeting Scheduling Decision Support System.
7718842
Averaging attack resilient video fingerprinting
This paper addresses an effective technique for digital fingerprinting for video. The code modulation technique, that uses an averaging collusion resilient fingerprint code derived from BIBD, is a robust method against averaging attacks. In this paper, we propose a new fingerprint code based on GD-PBIBD theory that is efficient in designing the fingerprint code and describe an effective method to embed and detect the fingerprint code, in which the merits of orthogonal modulation and coded modulation techniques are combined. To analyze the performance, we apply our method to video applications using a spread-spectrum watermarking technique in the spatial domain and the results show the proposed method is effective for fingerprint code embedding and detection.
20308502
Object signatures for supporting efficient navigation in object-oriented databases
Most methods that have been proposed to improve query processing in object-oriented databases are designed for the aggregation hierarchy. Although the structure of the generalization hierarchy is similar to the aggregation hierarchy, techniques developed for the aggregation hierarchy cannot fully use the characteristics of the generalization hierarchy to improve performance. We devise a new object signature for the generalization hierarchy. The object signature consists of two parts: a reference signature and a structure signature. The former can be used to eliminate objects that do not match the nested predicates specified in the query, and the latter can be used to eliminate objects that do not belong to the target classes. We propose some processing methods using the object signatures for queries that involve generalization hierarchies. In addition, we define cost models of the object signature and then compare the performance of the traditional query processing method with the object signature method. The results show that using the object signatures can effectively reduce the number of disk accesses in a query.
12313444
Real time guarantee of aperiodic packets in single hop ad hoc wireless networks
Building real-time applications on 802.11 wireless networks is challenging because the medium access protocol is distributed and nodes contend for the channel nondeterministically. So far all attempts at providing real-time services in 802.11 require restrictive traffic assumptions such as periodicity. In this paper we describe distributed admission control, a modification to 802.11 that, when used with distributed prioritization, provides real-time guarantees for aperiodic packets. We show that barring external errors such as interference on the channel or incorrect priority scheduling this protocol guarantees deadlines will be met.