Truth Maintenance in Blackboard Environment

Constraint satisfaction and search problems generally fall into the class of problems for which a direct algorithmic solution does not exist. The solution of these problems requires the examination of state spaces. A problem solver (inference engine) alone is not able to organize and maintain state space consistently. For this purpose a so-called truth maintenance system is required. Our truth maintenance system (MEKON) organizes data within a data abstraction called a context . Each context corresponds to one problem state and contains currently believed data. The truth maintenance system provides believed data retrieval, belief revision, contradiction handling and non-monotonicity handling, the features that help a problem solver to examine state spaces. MEKON represents an ATMS-like system implemented within BEST (Blackboard-based Expert System Toolkit). However, some special MEKON features such as state variables, context sensitive generation of assumptions and explicit context generation, that are not present in standard ATMSs, facilitate not only the solution of constraint satisfaction problems, but also the solution of search problems (not provided by standard ATMSs). Being deeply integrated with Prolog/Rex, BEST's knowledge representation language, and BEST's inference engine, MEKON provides a simple and efficient means for the examination of state spaces. Facts, hypotheses (assumptions), and concepts (frames) are used to describe a problem state, contexts are used to represent points in the state spaces, while rules are used to perform state transitions. MEKON is the only truth maintenacne system that provides truth maintenance capabilities on local blackboards thus enabling the solution of complex problems which include different kinds of real constraint satisfaction and search problems concerning diagnosis, allocation tasks, classification tasks, planning or scenario making.     

An expert system for choosing the best combination of options in a general purpose program for Automated Design Synthesis

An expert system called EXADS has been developed to aid users of the Automated Design Synthesis (ADS) general purpose optimization program. ADS has approximately 100 combinations of strategy, optimizer, and one-dimensional search options from which to choose. It is difficult for a nonexpert to make this choice. This expert system aids the user in choosing the best combination of options based on the users knowledge of the problem and the expert knowledge stored in the knowledge base. The knowledge base is divided into three categories; constrained problems, unconstrained problems, and constrained problems being treated as unconstrained problems. The inference engine and rules are written in LISP, contains about 200 rules, and executes on DEC-VAX (with Franz-LISP) and IBM PC (with IQ-LISP) computers.     

An architecture for workflow scheduling under resource allocation constraints

Research on specification and scheduling of workflows has concentrated on temporal and causality constraints, which specify existence and order dependencies among tasks. However, another set of constraints that specify resource allocation is also equally important. The resources in a workflow environment are agents such as person, machine, software, etc. that execute the task. Execution of a task has a cost and this may vary depending on the resources allocated in order to execute that task. Resource allocation constraints define restrictions on how to allocate resources, and scheduling under resource allocation constraints provide proper resource allocation to tasks. In this work, we provide an architecture to specify and to schedule workflows under resource allocation constraints as well as under the temporal and causality constraints. A specification language with the ability to express resources and resource allocation constraints and a scheduler module that contains a constraint solver in order to find correct resource assignments are core and novel parts of this architecture.     

Specifying an expert system

The success of numerous expert systems in practical applications warrants a more formal approach to their development and evaluation. Reliability assurance of expert systems requires a methodology for the specification and evaluation of these systems. Expert systems are a new class of software system, but some traditional techniques of software development may be adapted to their construction. However, the specification of an expert system differs from that of a more traditional software program in that parts of the specification are permitted to be only partially described when development starts.

Specifications have two important purposes: as contracts between suppliers and clients, and as blueprints for implementation. A specification consists of a problem specification and a solution specification. The problem specification plays the role of contract and states explicitly what the problem to be solved is, and the constraints that the final product must satisfy. The solution specification plays the role of blueprint and has two major aspects: analyzing how a human expert solves the problem, and proposing an equivalent automated solution. We propose an approach to the specification of expert systems that is flexible, yet rigorous enough to cover the important features of a wide range of potential expert system applications. We describe fully each of the components of an expert system specification and we relate specification to the issues of evaluation and maintenance of expert systems.     

A fuzzy expert system for a service centre of spare parts

This paper presents the design of an approximate reasoning framework for an expert system prototype for a service centre of spare parts, to which customers bring failed items for repair. The design development is fundamentally based upon an analysis of a queuing model associated with the service centre system problem. This queuing model provides a prerequisite insight and the knowledge about such a service system. The building process for the framework is described in a case study utilizing the queuing model, namely, M/M/c repair systems with spares. The objective here is to aid management in determining certain decision policies and the capacities which are critical to them. Within the approximate reasoning framework, the identification and the construction of the basic rules that contain uncertain (vague, ambiguous, fuzzy) linguistic terms are described, as well as the specification of the membership functions that represent the meaning of such linguistic terms. Consistency of rules is studied in accordance with the internal relationships between system variables. Approximate Analogical Reasoning with a tree search is used as the inference engine of the expert system. Approximate reasoning results are compared with analytical results.     

基于本体的应急救援知识库构建及其应用

针对专家系统在应急救援领域应用中存在的知识表示及推理等问题,采用基于本体的知识表示方法与基于Jena的规则推理引擎,参考简单知识工程方法论与Jena规则语法建立一个高速公路应急救援本体与推理规则,实现本体知识库的推理。将该知识库应用于高速公路应急救援系统中,结果表明其具备解决实际问题的能力;有利于领域知识的共享与重用;促进了专家系统在高速公路应急救援领域的发展。     

Limited-resource scheduling by generalized rule-based system

A general paradigm for solving resource allocation, time-tabling, and scheduling problems is presented. The paradigm is based on an expert system approach, which looks for a feasible solution that satisfies the problem's real-life constraints. The new paradigm includes generic concepts for resources, activities, constraints, and allocations. The general control strategy of the new paradigm is suitable for a large family of resource allocation and time-tabling problems. This control strategy includes three parts that deal with allocation, constraint checking, and changes to allocations.

The proposed paradigm was tested on three real-world problems: crew assignment to air force missions, class scheduling for a university department, and time-tabling of final examinations for the faculty of natural sciences. All cases were solved well in a few minutes of central processing unit time, by Prolog-based systems that implemented the proposed paradigm. These case studies are described in the paper in some detail, and an overall evaluation of the methodology is given.     

面向问题分析与决策的专家系统

专家系统的根本目标在于为实际应用问题提供强有力的分析与决策能力。以人类通过长期实践活动总结的复杂问题分析与决策方法为指导思想,建立了以问题对象为核心、相关对象为问题主体、问题现象为表现形式、因果关系为问题变化驱动力、过程知识和原理知识为参考对象的面向问题分析与决策的专家系统。这种专家系统围绕应用领域中的问题构建知识库,而不是使用规则,所以得到的知识系统比较合理、清晰,不容易产生知识矛盾与冲突,有利于大型知识库的构建;同时,采用基于问题的推理,与人类的思维习惯相符合,可以大大提高推理效率;此外,开发这种专家     

Intelligent support for specifications transformation

The authors describe an expert system, the Specification-Transformation Expert System (STES), which is to translate requirements specifications into design specifications automatically during the development phase of the software life cycle. STES accepts as input a software-requirements specification expressed in terms of dataflow diagrams. Using rules that embody a structured design methodology, STES translates this specification into a template describing a structure chart. STES consist of a knowledge base and an inference engine. The knowledge base contains information on the structured-design methodology and heuristic guidelines to help determine when certain methods should be applied. Given a target software system's requirements specification, the STES inference engine can perform intelligent decision-making and determine a suitable architectural design specification for the software system being designed. STES was originally implemented in OPS5 on a VAX11/780 computer. It has since been ported to an Apollo DN 3000 workstation and integrated with a commercial CASE tool     

MAPS: a language for multi-agent system design

Abstract: A language for multi-agent system design (MAPS) is presented and discussed in this paper. Any agent in MAPS is thought of as an expert system standing on its own. It can communicate through synchronous and asynchronous message sending. Dedicated behaviours are provided which specify how incoming messages are processed. Inter-agent cooperation is controlled via production rules. Two pre-defined agent classes are provided, which are given specific problem-solving roles: Knowledge Server (KS) agents are meant to maintain and transmit knowledge about problem-solving states, while Knowledge Processor (KP) agents are meant to process these elements in order to progress towards a solution. The duality between agent and resource modelling levels on the one hand and between KS and KP modelling styles on the other is shown to allow the specification of various control strategies. The environment is currently running on HP, SUN and DEC workstations.     

AN INTELLIGENT DECISIONMAKER FOR OPTIMAL CONTROL

We present an expert system that can handle various complicated decision-making problems of optimal control that are hardly solvable manually or even by computer-aided design techniques. The expert system is the first implementation of solving decision-making problems of optimal control using a computer, which paves the way for us to develop the real-time intelligent optimal control environment. Through a user-friendly interface, the expert system can receive the needed information from the user, perform heuristic search, and provide the results of a decisionmaker quickly both on the screen and from a printer. The important features of this expert system are that it () makes a decision on the problem-solving strategies for optimal control, that is, provides the solution structure and transversality conditions as well as types of some key equations; (2) processes symbolic information; (3) breaks down the whole search into three reasoning levels such that the problem can be solved easily and the search routine can be simplified; (4) utilizes “filter rules” to reduce production rules and enhance the program efficiency; (5) modifies the knowledge base and creates new rules in production rule memory; and (6) applies a “certainty factor” to represent imprecise knowledge. The expert system has been implemented using the AI tool OPS5 on a VAX 11/780 computer running under VMS. An example is also used to illustrate our expert system.     

School bus routing and scheduling: An expert system approach

This paper proposes an expert system approach to routing and scheduling school buses for a rural school system. The expert system is programmed in TURBO PROLOG for use on an IBM/XT and is applied to rural county school systems in Alabama.

The busing problem considered here consists of two components: routing and scheduling. The routing problem is concerned with the determination of a stop-to-stop route to be traversed to each school by each bus whereas the scheduling problem, the determination of times at all bus stops for each bus. A bus may be used for multiple runs. Each route is designed in such a way that the bus capacity, student riding time, school time window and road condition constraints are satisfied while attempting to minimize the number of buses required in operation, minimize the fleet travel time and balance the bus loads.

Conventionally, a predetermined algorithm is coded into computer programs for generating efficient routes and schedules. The user or route designer neither has any knowledge about the algorithm nor has any input of personal expertise into the solution process. As a result, a veteran designer is skeptical of the computer-generated routes and schedules. Moreover, non-quantifiable factors such as safety, preference and judgment are not taken into consideration in the traditional approach.

To alleviate these deficiencies, an expert system approach, which enables the expert knowledge to be kept separately from its execution, is utilized. This knowledge base contains factual knowledge such as road map, school locations, but capacities, stop locations, number of students at each stop, and drivers' homes. It also contains procedural knowledge such as heuristics for finding a route and scheduling multiple runs for a bus subject to various constraints. The inference engine or control program chooses the appropriate heuristics used in constructing efficient routes and schedules with respect to various objectives or goals. A user interface includes the graphic display of road maps and determined routes.     

Intelligent approach to a CAD system for the layout design of a ship engine room

The current layout design of a ship engine room not only depends on a skilled designer but also has many design constraints and regulations to be considered. At preliminary design stage CAD systems need to be intelligent in the sense that they must be able to use knowledge to achieve the designer’s goal. In this paper, we present an approach to implement a practical knowledge-based system for the machinery layout design of a ship engine room. The knowledge-base is implemented and verified in the actual CAD environment of a ship engine room, named MADES (Machinery ArrangementDesign Expert System), which we develop in this study. Design knowledge is represented byobject-oriented concept, and nonmonotonic reasoning enables the values of closely related objects to be consistent. The approaches presented in this paper provide a practical example of a knowledge-based system for complex design problems, and can also provide guidance on implementing an integrateddesign expert system that extends the capability of existing CAD systems.     

Visual knowledge specification for conceptual design: Definition and tool support

Current CAD tools are not able to support the conceptual design phase, and none of them provides a consistency analysis for sketches produced by architects. This phase is fundamental and crucial for the whole design and construction process of a building. To give architects a better support, we developed a CAD tool for conceptual design and a knowledge specification tool. The knowledge is specific to one class of buildings and it can be reused. Based on a dynamic and domain-specific knowledge ontology, different types of design rules formalize this knowledge in a graph-based form. An expressive visual language provides a user-friendly, human readable representation. Finally, a consistency analysis tool enables conceptual designs to be checked against this formal conceptual knowledge.In this article, we concentrate on the knowledge specification part. For that, we introduce the concepts and usage of a novel visual language and describe its semantics. To demonstrate the usability of our approach, two graph-based visual tools for knowledge specification and conceptual design are explained.     

基于CLIPS的卫星任务规划专家系统设计

本文提出了基于CLIPS的卫星任务规划专家系统的设计方法,详细分析了系统的结构和功能,重点讨论了中文产生式系统的BNF范式、基于上下文的推理机制和集合运算符。中文产生式系统的BNF范式基于CLIPS标准BNF范式定义,并依据BNF范式进行规则表示和规则自定义获取;推理机采用上下文限制的规则控制策略,依据不同的上下文加载相关的事实和规则,提高推理机的运行效率;利用规则中的对象逻辑子式进行了集合运算符的设计,并对极值运算符、属性差值运算符和均值运算符等三类集合运算符进行了探讨。该系统解决了卫星任务规划中知识表示和知识获取问题,提高了卫星任务规划推理效率,为卫星任务规划人员提供有效的辅助决策功能。     

Scheduling with flexible resources in parallel workcenters to minimize maximum completion time

In this paper we consider the parallel machine scheduling problem with dual resource constraints with the objective of minimizing the maximum completion time. We develop heuristics that combine list-scheduling and bin-packing approaches with rules to iteratively modify the flexible resource allocation. A lower bound is presented and the previous and proposed solution approaches to the problem are analyzed under a variety of experimental conditions, demonstrating that there are advantages to the proposed heuristics.