Case-based situation awareness

Situation-aware case-based decision support (SACBDS) systems comprise two distinct parts: situation awareness (SA) and case-based reasoning (CBR). The SA part keeps a finite history of the time space information of the domain and uses rules to interpret cues from the environment with respect to an individual user´s context, and then anticipates future situations by performing statistical inference over historical data. The CBR part is the part that seeks to accomplish a particular task with knowledge of the environment from the SA component. This paper discusses the fusion of the CBR model and the SA model into a case-based situation awareness (CBSA) model for situation awareness based on experience rather than rule, similarity assessment and problem solving prediction. The CBSA system perceives the users´ context and the environment and uses them to understand the current situation by retrieving similar past situations. Every past situation has a history. The future of a new situation (case) is predicted through knowledge of the history of a similar past situation. The paper evaluates the concept in the flow assurance control domain to predict the formation of hydrate in sub-sea oil and gas pipelines. The results provided the CBSA system with greater number of accurate predictions than the SACBDS system.