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The role of hierarchical knowledge representation in decisionmaking and system management
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1985
Year
Hierarchical Knowledge RepresentationEngineeringAbstraction HierarchyModel-based ReasoningFunctional HierarchyComplex SystemsCognitionIntelligent SystemsKnowledge-based ReasoningKnowledge TechnologySocial SciencesManagementKnowledge EngineeringSystems EngineeringAutonomous Decision-makingDecision TheoryKnowledge RepresentationCognitive ScienceDesignReasoning About ActionDecision Support SystemsSystem ManagementExperimental PsychologyKnowledge StructuringKnowledge ModelingAutomated ReasoningAutomationIntelligent Decision MakingKnowledge ManagementKnowledge Architecture
Decision‑maker knowledge representation in complex system control can be organized into hierarchical levels of abstraction, a structure that aligns with classical problem‑solving research such as Selz (1922). The paper reviews how an abstraction hierarchy shapes supervisory control strategies, contrasting causal and intentional systems and formal games, and discusses its implications for designing decision‑support systems. The authors examine the role of abstraction hierarchies in supervisory system control, highlighting differences between causal and intentional systems and formal games. An explicit abstraction hierarchy of a system’s functional properties is essential for consistent database and display design in decision‑support systems, and must also be considered when planning experiments on human decision‑making.
The knowledge representation of a decision-maker in control of a complex system can be structured in several levels of abstraction in a functional hierarchy. The role of such an abstraction hierarchy in supervisory systems control is reviewed, and the difference between causal and intentional systems and formal games is discussed in terms of the role of an abstraction hierarchy in the related decision strategies. This relationship is then discussed with reference to the classical psychological problem-solving research of O. Selz (1922) and others. Finally, the implications for the design of decision-support systems are discussed. It is argued that an explicit description of the functional properties of the system to be controlled in terms of an abstraction hierarchy is necessary for a consistent design of databases and display formats for decision-support systems. Also, it is necessary to consider the role of the abstraction hierarchy in reasoning when planning experiments on human decision-making.