In the AlphaACT system, observed event characteristics are used to identify a similar scenario from a knowledge base. A key characteristic of the method is the search for the first available “good enough” solution, rather than more traditional normative decision processes that search all options for the optimal answer.

The core of each AlphaACT application is an extensive database of decision scenarios: combinations of events, key observable indicators, and resulting decisions. The graphical user interface employs a pattern recognition algorithm to rapidly walk the decision maker through a limited set of questions to an appropriate scenario. The automated interview will end as soon as a scenario is found that adequately matches the limited information available to the decision maker.

The AlphaACT application then allows the user to adapt the base scenario to event conditions known at decision time, so the decision can be tuned for the specific event. Finally, the AlphaACT application identifies additional information that significantly refines the decision, allowing the decision maker to extract critical information from chaos.

Related Links:

>> AlphaACT Decision Process

>> Knowledge Acquisition

>> Future Applications

>> AlphaACT: HazMat Application

>> AlphaACT: Combat Patrol Application

>> AlphaACT: Fire Application

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