Models Based on Belief Networks

With respect to a belief network approach to constructing situation awareness models, recall from Chapter 7 that the situation assessor could be implemented as a set of belief nets (Pearl, 1986) that represent knowledge as nodes and links. Nodes represent particular features and events, intermediate results, and final overall situation types. Links represent causal and correlational relations between events and situations represented by the nodes and are associated with conditional probability tables. Stressor/moderator parameters of this model could include the following:

  • Content parameters—content of belief network nodes, number of nodes, network topology, belief network conditional probability tables, number of distinct belief networks

  • Process parameters—speed of propagation of probabilities along belief network links, speed of node belief updating, affective valence associated with particular nodes (e.g., a value associated with a particular situation or feature indicating whether it is desirable or undesirable)

All categories of individual difference variables identified earlier in this chapter can be represented in a belief network-based situation assessor. For example, varying levels of skill and training could be represented by varying numbers and types of belief network nodes and more finely tuned link probabilities, with higher skill levels being characterized by more refined nodes allowing greater accuracy, speed, and completeness of situation assessment. Individual history could be represented by weighting certain links more heavily, thus increasing the likelihood of their activation during processing and the consequent derivation of the corresponding specific situations. Personality/affective factors could be represented by associating a specific affective value (positive or negative) with previous situations. Depending on the current affective state of the agent, certain situations might then be recalled preferentially as a result of mood-congruent recall (Bower, 1981; Blaney, 1986), increasing the likelihood of their recognition, while others might be avoided, decreasing the likelihood that they would be selected during situation assessment. Finally, differences in cognitive and decision-making styles could be represented by the setting of processing biases to favor activation of nodes with certain properties (e.g., a recency bias being reflected by the most recently activated node or the ability to favor goal-directed versus data-directed reasoning) and by node number and content (e.g., a larger number of more refined nodes representing an accommodating preference and a smaller number of more generic nodes representing an assimilating preference, or the node level of abstraction representing case-based vs. first-principles situation assessment).

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