Memory model with unsupervised sequential learning: the effect of threshold self-adjustment

The goal of this research is the creation of a neural net-based autonomous AI system. Such a system, to be successful, should satisfy the following requirement: the learning must be unsupervised, consecutive and depend only on locally available knowledge. Adaptive Bidirectional Associative Memories (ABAM) provide unsupervised learning and flexibility of architecture, but they suffer from catastrophic interference when attempting to add some new knowledge after the completion of the initial training. The solution to this problem for backpropagation (BP) and Hopfield type nets is a pseudorehearsal of the base knowledge (Robins, 1998). This research applies this method to ABAM with attempt to pseudorehearse not the whole base population, but only the neighborhood of the new incoming knowledge. This allows the creation of pseudopatterns within the system without intervention of the experimenter, but requires the extension of conventional neural nets, which are currently viewed as sets of uniform simple units. The basis for this extension is provided by using the Cohen-Grossberg activation dynamics (Cohen & Grossberg, 1983) for the units and a SIMD computer that allows the user to change the parameters in this formula for a specific group of units in the net. Custom software has been written to achieve the required flexibility. The conceptual description of this software and parameters of the model are presented in this paper. Each of those parameters requires individual study; this paper focuses on threshold self-adjustment, which makes each unit more responsive to the change of input and provides additional sensitivity to the net. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. ’99 ACM Southeast Regional Conference 1999 ACM 1-58113-128-3/99/0004 5.00 Introduction An autonomous AI agent requires the memory system that allows it to act and learn independently. If we choose neural nets as an implementation of an agent’s memory, then from all known types of unsupervised nets, multilayer ABAM provides the most flexible and problem-adjustable architecture. Before we start modeling, we should consider the following problem: the ideal memory system should be stable enough to preserve the important information over time on one side, but sensitive enough to incorporate a new knowledge on the other. ABAM, as well as other neural networks, usually are sensitive to new knowledge, but is not stable in the sense that this new information in some cases severely disrupts the previous knowledge. This problem (known as catastrophic forgetting, catastrophic interference or the sequential learning problem) led to the limitation of existing neural network based AI systems to concurrent learning, when all information has to be presented at once before the system starts its functioning. Industry would, however, prefer an AI system that can learn while acting without complete retraining each time new information becomes available. Catastrophic forgetting also attracted a lot of criticism to connectionist cognitive modeling since such behavior of a neural net appeared to be not