Network Regions: Alternatives to the Winner-Take-All Structure

Abstract

Winner-take-all (WTA) structures are currently used in massively parallel (connectionist) networks to represent competitive behavior among sets of alternative hypotheses. However, this form of competition might be too rigid and not be appropriate for certain applications. For example, applications that involve noisy and erroneous inputs might mislead WTA structures into selecting a wrong outcome. In addition, for networks that continuously process input data, the outcome must dynamically change with changing inputs; WTA structures might lock-in on a previous outcome. This paper offers an alternative competition model for these applications. The model is based upon a meta-network representation scheme called network regions that are analogous to net spaces in partitioned semantic networks. Network regions can be used in many ways to clarify the representational structure in massively parallel networks. This paper focuses on how they are used to provide a flexible and adaptive competition model. Regions can be considered as representational units that represent the conceptual abstraction of a collection of nodes (or hypotheses). Through this higher-level abstraction, regions can better influence the collective behavior of nodes within the region. Several AI applications were used to test and evaluate this model.