On knowledge-based industrial scheduling systems

Dutch research on knowledge-based instructional systems: Introduction to the special issue

A causal and temporal reasoning model and its use in drug therapy applications

Artificial intelligence can assist in industrial control in many ways, but this paper concentrates on the role of knowledge-based systems in the areas of control, detection and diagnosis of faults, scheduling and planning. The paper argues that the application of knowledge-based techniques can provide a unification of these and other activities, and outlines the requirements of knowledge representation and inference techniques that are needed to make such systems a reality. The advances in hardware, including sensors as well as computers, that will be driven by and in turn stimulate the introduction of the knowledge-based industrial control systems are also considered.

Knowledge-based system for railway scheduling

Task Scheduling is the establishment of specific guidelines for the consecutive execution of activities. Within the domain of parallel computing, the process of scheduling becomes complex, requiring the use of several processors and the allocation of resources to each individual processor. The goal is to effectively distribute particular tasks across processors and computers to accomplish predetermined objectives. It is essential to focus on work scheduling and resource management, as these are the main problems in numerical comparison. This study employs a metaheuristic hybrid strategy to address the inherent challenges of task scheduling. The primary objective is to enhance the efficiency of resource allocation and work scheduling, which is achieved by utilizing a Hybrid Genetic Algorithm (HGA). The results obtained from implementing the HGA algorithm on 40 operational tasks demonstrate its higher performance in comparison to other algorithms. The approach is very effective in tackling task scheduling problems in multiprocessor computing systems. This approach is notable for its faster convergence time and more efficient utilization of resources compared to other algorithms. The HGA technique is a reliable solution for improving work scheduling and resource management in parallel computing environments.

This chapter explains how a mission critical Knowledge-Based System (KBS) has been designed and implemented within a real case study of a governamental organization. Moreover, the KBS has been developed using a novel agile software development methodology. Due to fast changing operational scenarios and volatile requirements, traditional procedural development methodologies perform poorly. Thus, an Agile-like methodology has been exploited, called iAgile. The KBS is based on an ontology used to merge different mental models of users and developers. Moreover, the ontology of the system is useful for interoperability and knowledge representation. Mission critical functionalities have been developed in 5-week cycles, along with the ontology. So, the KBS serves for three main activities: (i) requirement disambiguation, (ii) interoperability with other legacy systems, and (iii) information retrieval and display of different informative sources.

A fundamental shift in the preferred approach to building applied artificial intelligence (AI) systems has taken place since the late 1960s. Previous work focused on the construction of general-purpose intelligent systems; the emphasis was on powerful inference methods that could function efficiently even when the available domain-specific knowledge was relatively meager. Today the emphasis is on the role of specific and detailed knowledge, rather than on reasoning methods. The first successful application of this method, which goes by the name of knowledge-based or expert-system research, was the DENDRAL program at Stanford, a long-term collaboration between chemists and computer scientists for automating the determination of molecular structure from empirical formulas and mass spectral data. The key idea is that knowledge is power, for experts, be they human or machine, are often those who know more facts and heuristics about a domain than lesser problem solvers. The task of building an expert system, therefore, is predominantly one of “teaching” a system enough of these facts and heuristics to enable it to perform competently in a particular problem-solving context. Such a collection of facts and heuristics is commonly called a knowledge base. Knowledge-based systems are still dependent on inference methods that perform reasoning on the knowledge base, but experience has shown that simple inference methods like generate and test, backward-chaining, and forward-chaining are very effective in a wide variety of problem domains when they are coupled with powerful knowledge bases. If this methodology remains preeminent, then the task of constructing knowledge bases becomes the rate-limiting factor in expert-system development. Indeed, a major portion of the applied AI research in the last decade has been directed at developing techniques and tools for knowledge representation. We are now in the third generation of such efforts. The first generation was marked by the development of enhanced AI languages like Interlisp and PROLOG. The second generation saw the development of knowledge representation tools at AI research institutions; Stanford, for instance, produced EMYCIN, The Unit System, and MRS. The third generation is now producing fully supported commercial tools like KEE and S.1. Each generation has seen a substantial decrease in the amount of time needed to build significant expert systems. Ten years ago prototype systems commonly took on the order of two years to show proof of concept; today such systems are routinely built in a few months. Three basic methodologies—frames, rules, and logic—have emerged to support the complex task of storing human knowledge in an expert system. Each of the articles in this Special Section describes and illustrates one of these methodologies. “The Role of Frame-Based Representation in Reasoning,” by Richard Fikes and Tom Kehler, describes an object-centered view of knowledge representation, whereby all knowldge is partitioned into discrete structures (frames) having individual properties (slots). Frames can be used to represent broad concepts, classes of objects, or individual instances or components of objects. They are joined together in an inheritance hierarchy that provides for the transmission of common properties among the frames without multiple specification of those properties. The authors use the KEE knowledge representation and manipulation tool to illustrate the characteristics of frame-based representation for a variety of domain examples. They also show how frame-based systems can be used to incorporate a range of inference methods common to both logic and rule-based systems. "Rule-Based Systems,” by Frederick Hayes-Roth, chronicles the history and describes the implementation of production rules as a framework for knowledge representation. In essence, production rules use IF conditions THEN conclusions and IF conditions THEN actions structures to construct a knowledge base. The autor catalogs a wide range of applications for which this methodology has proved natural and (at least partially) successful for replicating intelligent behavior. The article also surveys some already-available computational tools for facilitating the construction of rule-based knowledge bases and discusses the inference methods (particularly backward- and forward-chaining) that are provided as part of these tools. The article concludes with a consideration of the future improvement and expansion of such tools. The third article, “Logic Programming, ” by Michael Genesereth and Matthew Ginsberg, provides a tutorial introduction to the formal method of programming by description in the predicate calculus. Unlike traditional programming, which emphasizes how computations are to be performed, logic programming focuses on the what of objects and their behavior. The article illustrates the ease with which incremental additions can be made to a logic-oriented knowledge base, as well as the automatic facilities for inference (through theorem proving) and explanation that result from such formal descriptions. A practical example of diagnosis of digital device malfunctions is used to show how significantand complex problems can be represented in the formalism. A note to the reader who may infer that the AI community is being split into competing camps by these three methodologies: Although each provides advantages in certain specific domains (logic where the domain can be readily axiomatized and where complete causal models are available, rules where most of the knowledge can be conveniently expressed as experiential heuristics, and frames where complex structural descriptions are necessary to adequately describe the domain), the current view is one of synthesis rather than exclusivity. Both logic and rule-based systems commonly incorporate frame-like structures to facilitate the representation of large amounts of factual information, and frame-based systems like KEE allow both production rules and predicate calculus statements to be stored within and activated from frames to do inference. The next generation of knowledge representation tools may even help users to select appropriate methodologies for each particular class of knowledge, and then automatically integrate the various methodologies so selected into a consistent framework for knowledge.

SUMMARY In this paper we develop an interactive, real time, knowledge-based approach for dynamic scheduling. Blackboard concept has been utilized to organize and maintain the dynamic data base. The major knowledge representation schemes used in the system include frame structures, relational tables, and production rules. The system design is highly modular in nature thereby providing the ease for further development and enhancement. Based on this system, a design philosophy of an AI/OR integrated approach is also presented.

This chapter describes briefly the concept of hybrid (multi-model) systems and presents two implementations of such systems. The first one is a hybrid system for job-shop scheduling. It consists of a job-shop simulation model used to build a schedule and calculate its quality and an expert system optimising the schedule. A hypothetical job-shop (inspired from a real industrial shop) containing seven machine tools processing parts of ten types with complex processing routes is used to illustrate the possibilities of the system. The second implementation is a multimodel system for scheduling in the chemical industry. It also combines simulation, rules-based decision making, object-oriented representation of the system state as well as a set of well-known algorithms for solving particular problems used together for scheduling discrete-continuous processes.

The current knowledge about biochemical networks is largely incomplete. Thus biologists constantly need to revise or extend existing knowledge. These revision or extension are first formulated as theoretical hypotheses, then verified experimentally. Recently, biological data have been produced in great volumes and in diverse formats. It is a major challenge for biologists to process these data to reason about hypotheses. Many computer-aided systems have been developed to assist biologists in undertaking this challenge. The majority of the systems help in finding “pattern” in data and leave the reasoning to biologists. Few systems have tried to automate the reasoning process of hypothesis formation. These systems generate hypotheses from a knowledge base and given observations. A main drawback of these knowledge-based systems is the knowledge representation formalism they use. These formalisms are mostly monotonic and are now known to be not quite suitable for knowledge representation, especially in dealing with incomplete knowledge, which is often the case with respect to biochemical networks. We present a knowledge based framework for the general problem of hypothesis formation. The framework has been implemented by extending BioSigNet-RR. BioSigNet-RR is a knowledge based system that supports elaboration tolerant representation and non-monotonic reasoning. The main features of the extended system include: (1) seamless integration of hypothesis formation with knowledge representation and reasoning; (2) use of various resources of biological data as well as human expertise to intelligently generate hypotheses. The extended system can be considered as a prototype of an intelligent research assistant of molecular biologists. The system is available at http://www.biosignet.org.

Describes a work carried out in the frame of the European Prometheus project, and concerns the realization of an electronic copilot using a knowledge-based system approach. A flexible software architecture is required to allow problem solving in dynamic real-time situations, including rapidly changing data, asynchronous data management, interrupt handling and temporal reasoning. This paper describes such an architecture based an a knowledge-based development system (SUPER) and a real-time multi-tasking kernel (VXWORKS). >

To solve the problem of advanced digital manufacturing technology in the practical application, a knowledge engineering technology was introduced into the computer numerical control (CNC) programming. The knowledge acquisition, knowledge representation and reasoning used in CNC programming were researched. The CNC programming system functional architecture of impeller parts based on knowledge based engineering (KBE) was constructed. The structural model of the general knowledge-based system (KBS) was also constructed. The KBS of CNC programming system was established through synthesizing database technology and knowledge base theory. And in the context of corporate needs, based on the knowledge-driven manufacturing platform (i.e. UG CAD/CAM), VC++6.0 and UG/Open, the KBS and UG CAD/CAM were integrated seamlessly and the intelligent CNC programming KBE system for the impeller parts was developed by integrating KBE and UG CAD/CAM system. A method to establish standard process templates was proposed, so as to develop the intelligent CNC programming system in which CNC machining process and process parameters were standardized by using this KBE system. For the impeller parts processing, the method applied in the development of the prototype system is proven to be viable, feasible and practical.

Knowledge Representation Methods for Dairy Decision Support Systems

A scheme for fuzzy temporal knowledge representation and reasoning is proposed. The scheme is based on the modification of the Petri nets called fuzzy time Petri nets and fuzzy temporal relations. The reasoning process uses modified Petri nets theory, possibility theory and a fuzzified version of Allen's (1983) temporal relations. The temporal linguistic expressions are modelled by primitives derived from the knowledge representation scheme. By combining and connecting these primitives the complex forms of situations from fuzzy temporal rich domains are modelled. An example of the use of the proposed scheme is given.

A hybrid granular-evolutionary computing method for cooperative scheduling optimization on integrated energy system in steel industry