26853065
Support for Integrated Value-Based Maintenance Planning
The paper considers how IRTMM intelligent real-time maintenance management system helps process-plant engineers and owners perform value-based plant maintenance. With IRTMM, they can inspect subsystems, identify component operating parameters, and review and make notes regarding component performance or operational and maintenance history.
16768615
The virtual design team
The long range goal of the Virtual Design Team" (VDT) research program is to develop computational tools to analyze decision making and communication behavior and thereby to support true organizational (re)engineering. This article introduces the underlying theory, the implementation of the theory as a computational model, and results from industrial test cases. Organization theory traditionally describes organizations only at an aggregate-level, describing and predicting the behavior of entire organizations in terms of general qualitative predictions. We define and implement a "micro" theory of the structure and behavior of components of organizations, explicitly representing activities, groups of people called "actors," and organizational structure and policies for project teams. A VDT model can be "run" by a discrete event simulation. Emergent aggregate model output behaviors include the predicted time to complete a project, the total effort to do the project, and a measure of process quality. More detailed model behaviors include the time-varying backlog of individual actors and the "exceptions" associated with activities. The results are detailed and specific, so they can guide specific managerial interventions in a project team and can support sensitivity studies of the relative impact of different organizational changes. We conclude that such a theory is tractable and predictive for complex but relatively routine, project-oriented design tasks. The application for which VDT offers unique new kinds of insights is where an organization is striving to shrink time to market dramatically for a product that is similar to ones it has previously developed. Reducing time to market dramatically almost always requires that previously sequential activities are executed more concurrently. In this situation, experienced managers can still correctly identify the required activities and estimate their durations and skill requirements; but they almost always underestimate the increased workload arising from exponentially higher coordination needs and the propagation of rework between the now highly concurrent activities. The VDT framework, which explicitly models information dependency and failure propagation between concurrent activities, has proven to be far more accurate, and to incorporate a wider range of parameters, than CPM/PERT process models for these fast-paced development projects."
875369
Reducing Fitness Evaluations Using Clustering Techniques and Neural Network Ensembles
In many real-world applications of evolutionary computation, it is essential to reduce the number of fitness evaluations. To this end, computationally efficient models can be constructed for fitness evaluations to assist the evolutionary algorithms. When approximate models are involved in evolution, it is very important to determine which individuals should be re-evaluated using the original fitness function to guarantee a faster and correct convergence of the evolutionary algorithm. In this paper, the k-means method is applied to group the individuals of a population into a number of clusters. For each cluster, only the individual that is closest to the cluster center will be evaluated using the expensive original fitness function. The fitness of other individuals are estimated using a neural network ensemble, which is also used to detect possible serious prediction errors. Simulation results from three test functions show that the proposed method exhibits better performance than the strategy where only the best individuals according to the approximate model are re-evaluated.
5139043
Representing Beliefs in the Fluent Calculus
Action formalisms like the fluent calculus have been developed to endow logic-based agents with the abilities to reason about the effects of actions, to execute high-level strategies, and to plan. In this paper we extend the fluent calculus by a method for belief change, which allows agents to revise their internal model upon making observations that contradict this model. Unlike the existing combination of the situation calculus with belief revision [16], our formalism satisfies all of the standard postulates for (iterated) belief change. Furthermore, we have extended the high-level action programming language FLUX by a computational approach to belief change which is provably equivalent to the axiomatic characterization in the fluent calculus.
10367277
OntoWebber: Model-Driven Ontology-Based Web Site Management
Building data-intensive Web sites and especially Web portals is a costly task, which requires considerable effort for data integration and maintenance and usually does not result in many reusable components. This is mainly because most of the design is hard-coded in static or dynamic Web pages. In this paper we integrate three different approaches to create a comprehensive solution to Web site and Web portal creation dubbed OntoWebber. OntoWebber integrates (1) the explicit modeling of different aspects of Web sites, (2) the use of ontologies as the foundation for Web portal design and (3) semi-structured data technology for data integration and Web site modeling. The resulting system and methodology supports the creation of reusable specifications of Web sites. OntoWebber is the basis for creating the Semantic Web Community Portal as part of the OntoAgents project, which will help the Semantic Web research community (distributed on the Web) to exchange and share knowledge conveniently and effectively.
13229821
Relational subscription middleware for Internet-scale publish-subscribe
We present a design of a distributed publish-subscribe system that extends the functionality of messaging middleware with "relational subscriptions", to support timely updates to state derived from published messages while preserving high throughput, scalability, and reliability.Critical to our design is our service guarantee of "eventual correctness". Eventual correctness is a weaker guarantee than the ACID properties of conventional databases, yet is useful enough to deliver state that is "just consistent enough".A key component of our design is a monotonic type system. All states delivered to clients represent facts that are permanently true, that may be refined by future updates, but will never become false. The monotonic type system is used both to formalize eventual correctness, and as a basis for our implementation, which generalizes the "Guaranteed Delivery" protocol previously implemented in the Gryphon system.We discuss: (1) our monotonic type system and relational subscription language; (2) eventual correctness; (3) the architecture of our implementation; (4) potential optimizations that form a basis for future studies.
16713800
On Evolutionary Optimization with Approximate Fitness Functions
The evaluation of the quality of solutions is usually very time-consuming in design optimization. Therefore, time-efficient approximate models can be particularly beneficial for the evaluation when evolutionary algorithms are applied. In this paper, the convergence property of an evolution strategy (ES) with neural network based fitness evaluations is investigated. It is found that the evolutionary algorithm will converge incorrectly if the approximate model has false optima. To address this problem, two strategies to control the evolution process are introduced. In addition, methods to eliminate false minima in neural network training are proposed. The effectiveness of the methods are shown with simulation studies on the Ackley function and the Rosenbrock function.