262 13 Local, Global and Glocal Knowledge Representation

262 13 Local, Global and Glocal Knowledge Representation 13.6.1 A Semi-Formal Model of Glocal Memory To explain the notion of glocal memory more precisely, we will introduce a simple semi-formal model of a system S that uses a memory to record information relevant to the actions it carries out. The overall concept of glocal memory should not be considered as restricted to this particular model. This model is not intended for maximal generality, but is intended to encompass a variety of current AI system designs and formal neurological models. In this model, we will consider S’s memory subsystem as a set of objects we’ll call “tokens,” embedded in some metric space. The metric in the space, which we will call the “basic distance” of the memory, generally will not be defined in terms of the semantics of the items stored in the memory; though it may come to shape these dynamics through the specific architecture and evolution of the memory. Note that these tokens are not intended as generally being mapped one-to-one onto meaningful items stored in the memory. The “tokens” are the raw materials that the memory arranges in various patterns in order to store items. We assume that each token, at each point in time, may meaningfully be assigned a certain quantitative “activation level.” Also, tokens may have other numerical or discrete quantities associated with them, depending on the particular memory architecture. Finally, tokens may relate other tokens, so that optionally a token may come equipped with an (ordered or un- ordered) list of other tokens. To understand the meaning of the activation levels, one should think about S’s memory subsystem as being coupled with an action-selection subsystem, that dynamically chooses the actions to be taken by the overall system in which the two subsystems are embedded. Each combination of actions, in each particular type of context, will generally be associated with the activation of certain tokens in memory. Then, as analysts of the system S, we may associate each token T with an “activation vector” v(T,t), whose value for each discrete time ¢ consists of the activation of the token T at time t. So, the 50’th entry of the vector corresponds to the activation of the token at the 50/th time step. “Items stored in memory” over a certain period of time, may then be defined as clusters in the set of activation vectors associated with memory during that period of time. Note that the system S itself may explicitly recognize and remember patterns regarding what items are stored in its memory — but, from an external analyst’s perspective, the set of items in S’s memory is not restricted to the ones that S has explicitly recognized as memory items. The “localization” of a memory item may be defined as the degree to which the various tokens involved in the item are close to each other according to the metric in the memory metric-space. This degree may be formalized in various ways, but choosing a particular quantitative measure is not important here. A highly localized item may be called “local” and a not-very-localized item may be called “global.” We may define the “activation distance” of two tokens as the distance between their activation vectors. We may then say that a memory is “well aligned” to the extent that there is a correlation between the activation distance of tokens, and the basic distance of the memory metric-space. Given the above set-up, the basic notion of glocal memory can be enounced fairly simply. A glocal memory is one: e that is reasonably well-aligned (i.e. the correlation between activation and basic distance is significantly greater than random) HOUSE_OVERSIGHT_013178