From Simple Associations to Systemic Reasoning: A Connectionist Representation of Rules, Variables and Dynamic Bindings

Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency as though these inferences are a reflex response of their cognitive apparatus. The work presented in this paper is a step toward a computational account of this remarkable reasoning ability. We describe how a connectionist system made up of simple and slow neuron-like elements can encode millions of facts and rules involving n-ary predicates and variables, and yet perform a variety of inferences within hundreds of milliseconds. We observe that an efficient reasoning system must represent and propagate, dynamically, a large number of variable bindings. The proposed system does so by propagating rhythmic patterns of activity wherein dynamic bindings are represented as the in-phase, i.e., synchronous, firing of appropriate nodes. The mechanisms for representing and propagating dynamic bindings are biologically plausible. Neurophysiological evidence suggests that similar mechanisms may in fact be used by the brain to represent and process sensorimotor information. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-90-05. This technical report is available at ScholarlyCommons: https://repository.upenn.edu/cis_reports/819 FROM SI'MPLE ASSOCIATIONS TO SYSTEMATIC REASONING: A CONNECTIONIST REPRESENTATION OF RULES, VARIABLES AND DYNAMIC BINDINGS Lokendra Shastri Venkat Ajjanagadde MS-CIS-90-05 LINC LAB 162 Department of Computer and Information Science School of Engineering and Applied Science University of Pennsylvania Philadelphia, PA 191 04