Technical description of CogPrime cognitive architecture and SRAM model

7.2 A Simple Formal Agents Model (SRAM) 133 One additional aspect of CogPrime’s knowledge representation that is important to PLN is the attachment of nonnegative weights n; corresponding to elementary observations o;. These weights denote the amount of evidence contained in the observation. For instance, in the context of a robotic agent, one could use these values to encode the assumption that an elementary visual observation has more evidential value than an elementary olfactory observation. We now have a model of an agent with long-term memory comprising procedural, declarative and episodic aspects, an internal cognitive workspace, and the capability to use procedures to drive actions based on items in memory and the workspace, and to move items between long- term memory and the workspace. 7.2.2.1 Modeling CogPrime Of course, this formal model may be realized differently in various real-world AGI systems. In CogPrime we have e a weighted, labeled hypergraph structure called the AtomSpace used to store declarative knowledge (this is the representation used by PLN) e acollection of programs in a LISP-like language called Combo, stored in a ProcedureRepos- itory data structure, used to store procedural knowledge e a collection of partial “movies” of the system’s experience, played back using an internal simulation engine, used to store episodic knowledge e AttentionValue objects, minimally containing ShortTermImportance (STI) and LongTer- mImportance (LTT) values used to store attentional knowledge e Goal Atoms for intentional knowledge, stored in the same format as declarative knowledge but whose dynamics involve a special form of artificial currency that is used to govern action selection The AtomSpace is the central repository and procedures and episodes are linked to Atoms in the AtomSpace which serve as their symbolic representatives. The “workspace” in CogPrime exists only virtually: each item in the AtomSpace has a “short term importance” (STT) level, and the workspace consists of those items in the AtomSpace with highest STI, and those procedures and episodes whose symbolic representatives in the AtomSpace have highest STI. On the other hand, as we saw above, the LIDA architecture uses separate representations for procedural, declarative and episodic memory, but also has an explicit workspace component, where the most currently contextually relevant items from all different types of memory are gathered and used together in the course of actions. However, compared to CogPrime, it lacks comparably fine-grained methods for integrating the different types of memory. Systematically mapping various existing cognitive architectures, or human brain structure, into this formal agents model would be a substantial though quite plausible exercise; but we will not undertake this here. 7.2.3 The Cognitive Schematic Next we introduce an additional specialization into SRAM: the cognitive schematic, written informally as HOUSE_OVERSIGHT_013049