This paper develops a philosophy for the use of Artificial Intelligence (AI) techniques as aids in engineering project management.

First, we propose that traditional domain-independent, ‘means–and’ planners, may be valuable aids for planning detailed subtasks on projects, but that domain-specific planning tools are needed for work package or executive level project planning. Next, we propose that hybrid computer systems, using knowledge processing techniques in conjunction with procedural techniques such as decision analysis and network-based scheduling, can provide valuable new kinds of decision support for project objective-setting and project control, respectively. Finally we suggest that knowledge-based interactive graphics, developed for providing graphical explanations and user control in advanced knowledge processing environments, can provide powerful new kinds of decision support for project management.

The first claim is supported by a review and analysis of previous work in the area of automated AI planning techniques. Our experience with PLATFORM I, II and III, a series of prototype AI-leveraged project management systems built using the IntelliCorp Knowledge Engineering Environment (KEE™), provides the justification for the latter two claims.

I discuss how the capabilities of the Edinburgh Designer System can be extended and used to support symbolic computation for robotics. I conclude that the Algebra Engine requires to handle temporal constructs, groups and tolerances, that the taxonomy can support activity modules and that automatic plan formation would require the creation of a specialist.

This article discusses the issues that arise in the design and implementation of expert systems. These issues include: task selection; the stages of development of expert system projects; knowledge acquisition; languages and tools; development and run-time environments; and organizational and institutional issues. The article closes with some speculation about the future development of expert systems.

Modifiable combining functions are a synthesis of two common approaches to combining evidence. They offer many of the advantages of these approaches and avoid some disadvantages. Because they facilitate the acquisition, representation, explanation, and modification of knowledge about combinations of evidence, they are proposed as a tool for knowledge engineers who build systems that reason under uncertainty, not as a normative theory of evidence.

Engineering design involves the evaluation and satisfaction of a wide variety of constraints. The ability to represent and process these constraints in a computer is important for the verification of the output produced by computer-aided design programs. Constraints need not only check designs but can also be used to derive design solution s that satisfy constraints. The paper discusses how to represent the dual nature of constraints so that design consistency is maintained as the design evolves.

Assumptions and rules of thumb are used frequently in design to propose initial solutions. We represent the logic behind the derivation of these assumptions as heuristic procedures and maintain the dependencies between these assumptions and their consequents as an aid to the management of design consistency. We also propose a simple scheme, involving the partitioning of the design modules, to effect design changes when constraint violations occur. An example from structural design illustrates the methodology.