Design has been extensively studied by artificial intelligence researchers for many years. These studies have resulted in a large number of design tools that perform interesting tasks. Understanding the capabilities of these tools is, however, very difficult, which seriously impedes progress in the field. A better understanding of different tools can be achieved by analyzing the knowledge use of existing tools. Such an analysis of six configuration design tools is presented. This results in a model of configuration design that shows significant similarity in the tasks performed by these tools.

A system that integrates design and planning for mechanical assemblies is presented. The system integrates neural network computing that captures designer's design concept and rule-based system to generate a task-level assembly plan automatically. The design concept is expressed by a standard pattern format representing qualitative assembly information. A neural network model together with feature-based model translates the input pattern into a preliminary boundary representation (B-rep). Based on a refinement B-rep assembly representation, assembly plans are generated for practical use in a single-robot assembly workcell. A feasible assembly plan that minimizes tool changes and subassembly reorientations is generated from the system. A robust part collision detection algorithm to generate the precedence relationships among the assembly's components is included in the system. By contrast with many assembly planning systems that used a prolonged question-and-answering session or required knowledge beyond what is typically available in the design database, an automated assembly planning system presented here draws input relationships directly from the conceptual design and the geometry of the assembly. The system developed under this study extracts all reasoning information from the product model and permits the components to be assembled in a multitude of directions. Several experiments illustrate the effectiveness of the designed assembly planning system.

In this paper, we show how a temporal constraint propagation technique can be embedded in the machine sequencing approach for solving the job shop scheduling problem. The temporal constraint propagation algorithm propagates the precedence constraints and machine interference constraints to reduce the search space generated by the machine sequencing approach. Further, by making use of the temporal nature of the job shop scheduling, efficient algorithms to propagate precedence constraints and machine interference constraints are developed. Experimental results reveal that embedding constraint propagation in the machine sequencing approach significantly reduces the computation time more than by just using the machine sequencing approach alone. Further, the proposed temporal constraint propagation algorithms provide an order of magnitude improvement on the computation time over the conventional constraint propagation algorithm.

We describe ACDS, an automatic diagnostic system. ACDS is capable of diagnosing faults on analog circuits in dynamic conditions. The circuit's dynamic behavior is studied by means of a series of intrastate simulations during which the qualitative state of the circuit does not change. An acquistion board collects the value of a set of quantities corresponding to accessible test points. These measurements are converted into qualitative values and are used for two purposes: first, to determine the state of the circuit components; second, to trigger the diagnostic procedure whenever a discrepancy between observed and predicted behavior is found. The main difficulty in this phase of measurement interpretation is in obtaining meaningful numerical-qualitative data conversion for values of quantities approaching a boundary between two different qualitative intervals. System performance has been verified through a number of simulations, which have shown the proposed approach to be efficient both in terms of localized faults and of flexibility in adapting to different circuits.

This paper describes an intelligent alarm processing expert system which is integrated in a large Supervisory Control and Data Acquisition system for power distribution networks. The expert system works as an operator support tool by diagnosing network disturbances and device malfunctions. The expert system is based on a hierarchic, multi-level problem-solving architecture, integrating both model-based and heuristic techniques acting upon an object-oriented data structure. Several enhancements have been designed and implemented to enable the system to perform its task online and real-time. The expert system covers online processing of real-time data and intelligent alarm processing, as well as the automatic creation and update of the knowledge base. It consists of approximately 25000 objects (units) and 190 rules. The system uses the expert system tool KEE, runs on SUN workstations, and is integrated in the Supervisory Control and Data Acquisition system via LAN. The expert system was implemented for the Public Utilities Board Singapore controlling its 22 kV distribution network and has been online since November 1990.