A common way of dynamically scheduling jobs in a flexible manufacturing system (FMS) is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no single rule exists that is better than the rest in all the possible states that the system may be in. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning can be used. Analyzing the previous performance of the system (training examples) by means of this technique, knowledge is obtained that can be used to decide which is the most appropriate dispatching rule at each moment in time. In this paper, a review of the main machine learning-based scheduling approaches described in the literature is presented.

Searching for information is an ubiquitous need in today's data-oriented environments. However, a request for search often entails the service and cooperation of tools managing a diversified set of tasks. In this article, we explore how tools in the form of cooperating agents can be deployed for information management. We describe an agent framework called GAME (goal-oriented, agent-managed environment), and focus on how GAME agents search cooperatively for information requested by a user. Cooperative search entails several issues such as coordinating agent activities, maintaining transparency to agent heterogeneity, and designing information formats to be shared among the agents that require examination. This article analyzes these issues and describes how they are handled in the GAME framework. Cooperative search effectively supports collaboration and information sharing not only among agents in a domain, but also among GAME agents developed across domains. We illustrate the application of cooperative search in task-oriented domains such as Manufacturing and Front Office showing how GAME promotes intradomain and interdomain collaboration in a Factory environment.

In equipment monitoring and diagnostics, it is very important to distinguish between a sensor failure and a system failure. In this paper, we develop a comprehensive methodology based on a hybrid system of AI and statistical techniques. The methodology is designed for monitoring complex equipment systems, which validates the sensor data, associates a degree of validity with each measurement, isolates faulty sensors, estimates the actual values despite faulty measurements, and detects incipient sensor failures. The methodology consists of four steps: redundancy creation, state prediction, sensor measurement validation and fusion, and fault detection through residue change detection. Through these four steps we use the information that can be obtained by looking at: information from a sensor individually, information from the sensor as part of a group of sensors, and the immediate history of the process that is being monitored. The advantage of this methodology is that it can detect multiple sensor failures, both abrupt as well as incipient. It can also detect subtle sensor failures such as drift in calibration and degradation of the sensor. The four-step methodology is applied to data from a gas turbine power plant.

This article presents the results of research concerning possibilities of applying multilayer perceptron type of neural network for fault diagnosis, state estimation, and prediction in the gas pipeline transmission network. The influence of several factors on accuracy of the multilayer perceptron was considered. The emphasis was put on the multilayer perceptrons' function as a state estimator. The choice of the most informative features, the amount and sampling period of training data sets, as well as different configurations of multilayer perceptrons were analyzed.

This paper presents a framework and a prototype for designing Integrated Construction Management (ICM) software applications using reusable components. The framework supports the collaborative development of ICM software applications by a group of ICM application developers from a library of software components. The framework focuses on the use of an explicit software development process to capture and disseminate specialized knowledge that augments the description of the ICM software application components in a library. The importance of preserving and using this knowledge has become apparent with the recent trend of combining the software development process with the software application code. There are three main components in the framework: design patterns, design rationale model, and intelligent search algorithms. Design patterns have been chosen to represent, record, and reuse the recurring design structures and associated design experience in object-oriented software development. The Design Recommendation and Intent Model (DRIM) was extended in the current research effort to capture the specific implementation of reusable software components. DRIM provides a method by which design rationale from multiple ICM application designers can be partially generated, stored, and later retrieved by a computer system. To address the issues of retrieval, the paper presents a unique representation of a software component, and a search mechanism based on Reggia's setcover algorithm to retrieve a set of components that can be combined to get the required functionality is presented. This paper also details an initial, proof-of-concept prototype based on the framework. By supporting nonobtrusive capture as well as effective access of vital design rationale information regarding the ICM application development process, the framework described in this paper is expected to provide a strong information base for designing ICM software.

This article is an attempt to improve the efficiency of procedures for compositional synthesis of design solutions using building blocks. These procedures have found use in a wide range of applications, and are one of the most substantial outcomes of research into automated synthesis of design solutions. Due to their combinatorial nature, these procedures are highly inefficient in solving problems, especially when the database of building blocks for synthesis or the problem size is large. Previous literature often focuses on improving only the algorithm part of a procedure, although it is both its algorithm and database which together determine the overall efficiency of the procedure. This article reports the construction and analysis of an improved algorithm, based on bidirectional search, for efficient compositional synthesis of design solutions using a set of building blocks.

The paper describes the task of performing efficient decision-theoretic troubleshooting of electromechanical devices. In general, this task is NP-complete, but under fairly strict assumptions, a greedy approach will yield an optimal sequence of actions, as discussed in the paper. This set of assumptions is weaker than the set proposed by Heckerman et al. (1995). However, the printing system domain, which motivated the research and which is described in detail in the paper, does not meet the requirements for the greedy approach, and a heuristic method is used. The method takes value of identification of the fault into account and it also performs a partial two-step look-ahead analysis. We compare the results of the heuristic method with optimal sequences of actions, and find only minor differences between the two.

We present a new representation that allows a rigid-body dynamic simulation to be described as a set of “causal-processes.” A causal-process is an interval of time during which both the behavior and the causes of the behavior remain qualitatively uniform. The representation consists of acyclic, directed graphs that are isomorphic to the flow of causality through the kinematic chain. Forces are the carriers of causality in this domain; thus they are central to the representation. We use this representation to compute the purposes of the geometric features on the parts of a device. To compute the purpose of a particular feature, we simulate the behavior of the device with and without the feature present. We then re-represent the two simulations as causal-processes and identify any causal-processes that exist in one simulation but not the other. Such processes are indicative of the feature's purpose. Because they are already causal descriptions of behavior, they can be directly translated into natural language descriptions of the feature's purpose. We have implemented our approach in a computer program called ExplainIT II.

Creative Stimulator (CreaStim) is an intelligent interface for pattern design that behaves as a semiactive partner to human designers rather than as a passive graphical or computational tool. By making adjustments to psychological differentials and/or design parameters, CreaStim is able to help designers to explore innovative pattern designs and to get inspiration, producing different types of novel designs. In this article, the mechanism, the technique, the implementation, and the testing of CreaStim are described. The basic principle of CreaStim is the catastrophe theory, which implies that sudden realization in the thinking process of design may lead to creativity. CreaStim tries to stimulate and/or impact designers' creativity in design process using the output of it, rather than to simulate the sudden realization. The core of the CreaStim is a neural network-based imagining engine, a data repository, and its learning strategies considering psychological factors. The psychological factors, which are thought one of the key influences to creative design, are based on the questionnaires completed by designers about the existing successful designs. The repository contains not only a traditional database storing functional attributes, economic attributes, graphic description, structural description, and psychological attributes, but also methods, rule-based knowledge, and pattern-type knowledge. And it is managed by an application program called Design Template Group (DTG) manager. Trained with 12 pieces of successful pattern designs and 528 pieces of pseudo-examples produced and evaluated by the authors, CreaStim is implemented for a PC and an evaluation poll from five designers shows that designers may most likely get some inspiration from the produced patterns and some of them can even be adopted as the design alternatives directly.

The purpose of this study is to investigate and optimize the dynamics of state transitions during a process of multistage fuzzy control systems. A variation of the methodology for solving the Vehicle Routing Problem, known as the Savings Method, is applied to finding a sequence of controls to maximize the overall performance of multistage control processes. The Savings Method has been modified into a heuristic to traverse state precedence when transitioning from one known system state to another. In addition, the procedure allows for imposing of two different types of precedences (mandatory and optional) among transition states, as well as a pair of fuzzy constraints and goals throughout each stage of the process. As the control of the system progresses, fuzzy goals may be preset or introduced interactively.

This paper introduces an architecture for aggregation of output from different diagnostic tools. The diagnostic fusion tool deals with conflict resolution, where diagnostic tools disagree; temporal information discord, where the estimate of different tools is separated in time; differences in information updates, where the classifiers are updated at different rates; fault coverage discrepancies; and integration of a priori performance specifications. To this end, a hierarchical weight manipulation approach is introduced which creates and successively refines a fused output. The performance of the fusion tool is evaluated throughout its design. This allows impact assessment of adding heuristics and enables early tuning of parameters. Results obtained from diagnosing on-board faults from aircraft engines are shown which demonstrate the fusion tool's operation.

Modern industry is concerned about extending the lifetime of its critical processes and maintaining them only when required. Significant aspects of these trends include the ability to diagnose impending failures, prognosticate the remaining useful lifetime of the process and schedule maintenance operations so that uptime is maximized. Prognosis is probably the most difficult of the three issues leading to condition-based maintenance (CBM). This paper attempts to address this challenging problem with intelligence-oriented techniques, specifically dynamic wavelet neural networks (DWNNs). DWNNs incorporate temporal information and storage capacity into their functionality so that they can predict into the future, carrying out fault prognostic tasks. Such fundamental issues as the network structure, learning algorithms, stability analysis, uncertainty management, and performance assessment are studied in a theoretical framework. An example is presented in which a trained DWNN successfully prognoses a defective bearing with a crack in its inner race.