Masaru.Tomita@A.CS.CMU.EDU (11/12/86)
Time: 3:30pm Place: WeH 5409 Date: 11/18, Tuesday BoltzCONS: Representing and Transforming Recursive Objects in a Neural Network David S. Touretzky, CMU CSD BoltzCONS is a neural network in which stacks and trees are implemented as distributed activity patterns. The name reflects the system's mixed representational levels: it is a Boltzmann Machine in which Lisp cons cell-like structures appear as an emergent property of a massively parallel distributed representation. The architecture employs three ideas from connectionist symbol processing -- coarse coded distributed memories, pullout networks, and variable binding spaces, that first appeared together in Touretzky and Hinton's neural network production system interpreter. The distributed memory is used to store triples of symbols that encode cons cells, the building blocks of linked lists. Stacks and trees can then be represented as list structures, and they can be manipulated via associative retrieval. BoltzCONS' ability to recognize shallow energy minima as failed retrievals makes it possible to traverse binary trees of unbounded depth nondestructively without using a control stack. Its two most significant features as a connectionist model are its ability to represent structured objects, and its generative capacity, which allows it to create new symbol structures on the fly. A toy application for BoltzCONS is the transformation of parse trees from active to passive voice. An attached neural network production system contains a set of rules for performing the transformation by issuing control signals to BoltzCONS and exchanging symbols with it. Working together, the two networks are able to cooperatively transform ``John kissed Mary'' into ``Mary was kissed by John.'' -------