Perspectives on the use of rule-based control

Issues regarding the application of artificial intelligence techniques to real-time control are discussed. Advantages associated with knowledge-based programming are discussed. A proposed rule-based control technique is summarized and applied to the problem of automated aircraft emergency procedure execution. Although emergency procedures are by definition predominately procedural, their numerous evaluation and decision points make a declarative representation of the knowledge they encode highly attractive, resulting in an organized and easily maintained software hierarchy. Simulation results demonstrate that real-time performance can be obtained using a microprocessor-based controller. It is concluded that a rule-based control system design approach may prove more useful than conventional methods under certain circumstances, and that declarative rules with embedded procedural code provide a sound basis for the construction of complex, yet economical, control systems

Control Strategy Influence on the Efficiency of a Hybrid Photovoltaic-Battery-Fuel Cell System Distributed Generation System for Domestic Applications☆

AbstractThe full exploitation of locally available renewable resources together with the reduction of system installation and management costs are key issues of diffused Distributed Generation (DG). In the given context, hybrid systems are already at an advanced stage of development which typically integrate several sub-systems. In such hybrid systems, Renewable Energy Sources generation systems (e.g. photovoltaic panels) are coupled to energy storage devices (electric batteries) and with programmable generators (a diesel generator or, more recently, with a sub-system based on fuel cells) allowing stable operations under a wide range of conditions. In this paper a solution which uses hydrogen and fuel cells as a programmable source is presented and is studied by means of a mixed experimental and numerical: a Hardware-In-Loop test bench designed and realized at the Department lab, able to reproduce the behavior of a hybrid system for domestic applications. The system is controlled by means of a rule-based control strategy acting on the common DC-bus whose optimization has a significant influence both on system design and on its overall system energy performances. Results show that Rule-Based strategy have a great potential towards cost reduction and components lifetime increase, while energy efficiency mainly depends on correct system sizing

TARGET: Rapid Capture of Process Knowledge

TARGET (Task Analysis/Rule Generation Tool) represents a new breed of tool that blends graphical process flow modeling capabilities with the function of a top-down reporting facility. Since NASA personnel frequently perform tasks that are primarily procedural in nature, TARGET models mission or task procedures and generates hierarchical reports as part of the process capture and analysis effort. Historically, capturing knowledge has proven to be one of the greatest barriers to the development of intelligent systems. Current practice generally requires lengthy interactions between the expert whose knowledge is to be captured and the knowledge engineer whose responsibility is to acquire and represent the expert's knowledge in a useful form. Although much research has been devoted to the development of methodologies and computer software to aid in the capture and representation of some types of knowledge, procedural knowledge has received relatively little attention. In essence, TARGET is one of the first tools of its kind, commercial or institutional, that is designed to support this type of knowledge capture undertaking. This paper will describe the design and development of TARGET for the acquisition and representation of procedural knowledge. The strategies employed by TARGET to support use by knowledge engineers, subject matter experts, programmers and managers will be discussed. This discussion includes the method by which the tool employs its graphical user interface to generate a task hierarchy report. Next, the approach to generate production rules for incorporation in and development of a CLIPS based expert system will be elaborated. TARGET also permits experts to visually describe procedural tasks as a common medium for knowledge refinement by the expert community and knowledge engineer making knowledge consensus possible. The paper briefly touches on the verification and validation issues facing the CLIPS rule generation aspects of TARGET. A description of efforts to support TARGET's interoperability issues on PCs, Macintoshes and UNIX workstations concludes the paper

Learning information extraction patterns

The rapid growth of online texts call for systems that can extract relevant information. Many information extraction systems have been developed using the knowledge engineering approach, which is often time-consuming, laborious, and of no portability. A more promising direction is to apply machine learning techniques to information extraction. A complete Information Extraction (IE) system, IEPlus, has been developed for exploring various design issues. Fine-grained semantic units were defined, and a strategy for semantic resolution was proposed in IEPlus. An enhancement for rule evaluation based on case frame matching was implemented in IEPlus. A rule firing strategy was also presented in IEPlus, which prioritizes the most specific rule in terms of the number of terms matched. Experiments on the Rental Ads domain demonstrated the effectiveness of the IEPlus system. IEPlus is highly flexible resulting from its object-oriented design, and has the capability of exploring various issues in information extraction system design

Incorporating a rule-based model of judgment into a wastewater treatment plant design optimization model

The use of a rule-based modeling technique for the formal consideration of poorly modeled issues in a water quality management problem is illustrated in the context of wastewater treatment plant design. Sludge bulking is a poorly understood problem in activated sludge wastewater treatment plants. An engineer must use judgement gained from experience when he designs an activated sludge plant to prevent bulking from causing the plant to fail. An attempt was made to use fuzzy logic in order to model that judgement. Results from research were taken from the literature and used independently as constraints to an activated sludge wastewater plant design optimization model to see their effect on the optimal design. Some of the research results were then formulated as rules in a rule-based system which relates design variable values to the likelihood of a design experiencing bulking problems. The weights of association of those rules to the conclusion that a given design would experience bulking problems and the logical interaction of those rules were calibrated using an experienced engineer's evaluation of a set of 15 plant designs. The consistency of the engineer's and the judgement model's evaluations were then checked with a second set of 15 designs. The model of judgement could be used to evaluate the bulking potential of any design. In the particular example developed, the judgement model was incorporated into a wastewater treatment plant design optimization model so that the cost effectiveness of constraint combinations could be examined. The tradeoff between cost and the likelihood of experiencing bulking problems was examined for a typical plant design problem.U.S. Department of the InteriorU.S. Geological SurveyOpe