KEYWORDS: Information security, Biology, Fuzzy logic, Cognitive modeling, Computer security, Data acquisition, Data modeling, Information fusion, Microorganisms, Sensors
In this paper, a technique will be presented for establishing the value of acquiring data on attributes
unavailable at the time an initial inference is made from a fuzzy cognitive map. The technique involves
three steps. In the first, an assessment is made of the reachability of unavailable attributes to the final
outcomes. This involves determining whether a chain of causality from the attribute of interest to the
outcome is present. If not, the attribute of concern can not affect the outcome and can be eliminated from
further consideration. For those nodes that can affect the outcome dominance in the chains of causality are
determined within the map. This is the second step in the process. If other paths dominate the chain of
interest such that the attribute can not affect the outcome regardless of its value, then it can also be
eliminated from further consideration. In the final step, assuming that the cost of acquiring the required
information has been incorporated into the map, a determination is made of the value of having the
additional data.
Fuzzy cognitive maps are an emerging technique for knowledge elicitation and data synthesis. The technique can capture
the cause and effect relationships that subject matter experts believe to exist about a problem. A chief advantage of this
method is that a common metric for different attributes does not need to be determined because states of attributes are
compared to states of attributes. This is also a disadvantage because the map can only infer a qualitative state for a node
of interest, not a numerical value. To overcome this limitation, nodes are modeled using fuzzy sets that are then
propagated through the map. Borrowing techniques used in fuzzy control systems, the scaled fuzzy sets can then be used
to yield a crisp numerical value for the attribute represented by the node.
In this paper, the techniques of using fuzzy cognitive maps will be outlined, and demonstrated with an example. Fuzzy cognitive maps will be used as a way to model the causal process in a cognitive system. With such a model interventions to change the dynamics of the system can be developed. In the particular example, the information on a display needed to be improved to support group situation awareness within an AWACS team. A fuzzy cognitive map was developed of the chain of causality that led from the current information structure of the AWACS display to the loss of situation awareness. The map could then be examined to identify ways in which the linkages could be altered to improve situation awareness, and points at which technology could be applied. From this a set of design changes could be recommended.
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