Abstract: A machine-controlled method may include a data store storing textual information, numerical information, belief information, estimates, or any combination thereof, and a machine executing a user-initiated or group-initiated query against the stored information. The processor may apply preferences, targets and thresholds by asserting a user-specified query conditions against the information. Responsive to multiple results of the querying, the processor may apply an importance by asserting a user-defined importance condition against the results of the query and may provide an indication of a ranking corresponding to at least one of the results.

Abstract: A machine-controlled electronic commerce (e-commerce) method may include receiving from a user an importance condition and a preference condition, target condition, or both. A data store may store textual information, numerical information, belief information, estimation data, or any combination thereof. A machine may execute a query against the stored information. A processor may apply an importance by asserting the importance condition against the stored information. The processor may apply a preference probability by asserting the preference condition against the stored information. Alternatively or in addition thereto, the processor may apply the target condition against the stored information. The machine may perform a search operation that incorporates at least one result of the querying and provide at least one search result based on the search operation. Responsive to multiple results of the querying, the machine may provide an indication of a ranking corresponding to at least one of the results.

Abstract: A machine-controlled method may include a data store storing textual information, numerical information, belief information, estimates, or any combination thereof, and a machine executing a user-initiated or group-initiated query against the stored information. A processor may apply an importance by asserting a user-defined importance condition against the information. The processor may apply a preference probability by asserting a user-specified preference condition against the information. The processor may assert a user-established target condition against the information. Responsive to a search request, filtering request, collaborative decision making (CDM request), matching request, or situation awareness activity, the machine may perform a search operation, a filtering operation, a CDM operation, a matching operation, or an actionable intelligence operation, respectively.

Abstract: A machine-controlled method may include receiving from a user an importance condition and a preference condition, target condition, or both. A data store may store textual information, numerical information, belief information, estimation data, or any combination thereof. A machine may execute a query against the stored information. A processor may apply an importance by asserting the importance condition against the stored information. The processor may apply a preference probability by asserting the preference condition against the stored information. The processor may apply the target condition against the stored information. The machine may perform a filtering operation that incorporates at least one result of the querying and provide at least one filtering result based on the filtering operation.

Abstract: A machine-controlled method may include a data store storing textual information, numerical information, belief information, estimates, or any combination thereof, and a machine executing a user-initiated or group-initiated query against the stored information. A processor may apply an importance by asserting a user-defined importance condition against the information. The processor may apply a preference probability by asserting a user-specified preference condition against the information. The processor may assert a user-established target condition against the information. Responsive to a situation awareness activity, the machine may perform an actionable intelligence operation. Responsive to multiple results of the querying, the machine may provide an indication of a ranking corresponding to at least one of the results.

Abstract: A machine-controlled method may include receiving from a user an importance condition and a preference condition, target condition, or both. A data store may store textual information, numerical information, belief information, estimation data, or any combination thereof. A machine may execute a query against the stored information. A processor may apply an importance by asserting the importance condition against the stored information. The processor may apply a preference probability by asserting the preference condition against the stored information. Alternatively or in addition thereto, the processor may apply the target condition against the stored information. The machine may perform a matching operation that incorporates at least one result of the querying and provide at least one matching result based on the matching operation. Responsive to multiple results of the querying, the machine may provide an indication of a ranking corresponding to at least one of the results.

Abstract: A machine-controlled method may include receiving from a user an importance condition and a preference condition, target condition, or both. A data store may store textual information, numerical information, belief information, estimation data, or any combination thereof. A machine may execute a query against the stored information. A processor may apply an importance by asserting the importance condition against the stored information. The processor may apply a preference probability by asserting the preference condition against the stored information. Alternatively or in addition thereto, the processor may apply the target condition against the stored information. The machine may perform a collaborative decision making (CDM) operation that incorporates at least one result of the querying and provide at least one CDM result based on the CDM operation. Responsive to multiple results of the querying, the machine may provide an indication of a ranking corresponding to at least one of the results.

Abstract: To identify the best choice from a set of alternatives, team members select the criteria to be used in identifying the best choice. Each criterion describes at least one feature of the alternatives. Each team member then, for each alternative, defines a target value for the feature of the criteria and sets a level of knowledge about the feature of the criteria. Using the target values and the team member's knowledge of each feature, each team member estimates how well the alternatives meet the target values for the features. The system then calculates an expected utility for each alternative based on the team member's estimations and a probability that each alternative is the best choice. Finally, based on the expected utility and probability that each alternative is best, a best choice is identified.