SOAR: an architecture for general intelligence
Artificial Intelligence
Dual algorithms for pure network problems
Operations Research
Unified theories of cognition
A knowledge-based mathematical model formulation system
Communications of the ACM - Special issue on analysis and modeling in software development
Working Knowledge: How Organizations Manage What They Know
Working Knowledge: How Organizations Manage What They Know
Using AI in Knowledge Management: Knowledge Bases and Ontologies
IEEE Intelligent Systems
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The need to develop schedules for projects with resource constraints and cash flows arises in organizational settings ranging from construction planning to research and development. Given the intractable nature of the problem, a variety of knowledge sources relevant to the project scheduling task have been identified in the Operations Management literature. These include a large number of heuristic procedures that can be used to generate feasible project schedules as well as recent neural network-based approaches that can select appropriate heuristic procedures to apply to a specific instance of the project scheduling problem. While integrated application of these knowledge sources is required to effectively support scheduling, previous work has focussed on developing and implementing them in isolation. The problem space computational model presented in this paper addresses this shortcoming by integrating these various knowledge sources, thus enabling the development of decision support systems for resource constrained project scheduling. More generally, the modeling approach used in this paper can be applied to create systems to assist knowledge intensive tasks that arise in many organizational settings.