A connectionist model for diagnostic problem solving

A competition-based connectionist model for solving diagnostic problems is described. The problems under consideration are computationally difficult in that multiple disorders may occur simultaneously and a global optimum in the space exponential to the total number of possible disorders is sought as a solution. To solve this problem, global optimization criteria are decomposed into local optimization criteria that are used to govern node activation updating in the connectionist model. Nodes representing disorders compete with each other to account for each `individual´ present manifestation, yet complement each other to account for all present manifestation, yet complement each other to account for all present manifestations through parallel node interactions. When equilibrium is reached, the network settles into a locally optimal state in which some disorder nodes (winners) are fully activated and compose the diagnosis for the given case, while all other disorder nodes are fully deactivated. A resettling process is proposed to improve accuracy. Three randomly generated examples of diagnostic problems, each of which has 1024 cases, were tested