Results of simulations of a system with the recommendation architecture

Functionally complex electronic systems are organized into functional components exchanging unambiguous information. The requirement to exchange unambiguous information results in difficulties in implementing parallel processing and extreme difficulty in implementing any capability to heuristically change functionality based on experience. The recommendation architecture allows the exchange of ambiguous information between functional components and therefore offers a way to reduce these difficulties. A system with the recommendation architecture uses a device imprinting mechanism to heuristically organize its inputs into a portfolio of ambiguous information repetition conditions on a range of levels of detail. The presence or absence of these conditions contains enough information to be used by a separate subsystem to determine appropriate behavior. Simulations of a simple system with the recommendation architecture demonstrate that sequences of inputs of wide range of different types can be heuristically organized into a functionally usable set of repetition conditions. Organization is successful even though there are no exact repetitions of input conditions. Learning effectiveness measures which make no use of information on the consequences of system actions can be used to adjust architectural parameters to organize even wider ranges of input types. These results demonstrate the feasibility of developing functionally complex systems with the recommendation architecture.