Technical Overview of OpenCog CogPrime Architecture

6.3 Current and Prior Applications of OpenCog 109 SPACETIME DIMENSIONAL BACKUP SERVER EMBEDDING STORE SPACE ASSOCIATIVE EPISODIC MEMORY REPOSITORY / \ FORMATION MaSES/ WORLD CLIMBING ‘errr PROCEDURE AGING Te EMBEDDING ATTENTION AGRA ALLOCATION EPISODIC ENCODING / RECALL FORGETTING/ FREEZING / DEFROSTING LEARNING BLENDING [ey ~DECLARATIVE/ y | SEMANTIC ATOMS cuusTerine |S | : \, a HEBBIAN \, ©) ATOMS ARE \ oy atoms \ . SELECTIVELY PLN A/T \ 9 \ “FORGOTTEN” PROBABILSTIC / he pee h \ 4 INFERENCE / PROCEDURE Se | 23 f ATOMS Es 7) NEW atoms = \ | ARE FORMED i A 2 |e ‘ | ot ALL ATOMS | / oe. @\| piatocve =| Pal HAVE SHORT | f s PROCEDURE / a ager AND LONG-TERM i 4 TOMS ( i2? IMPORTANCE VALUES ( jf MOTOR ¥ ¥ i f PROCEDURE f | / ATOMS _—— NY SOME ATOMS HAVE “N (UNCERTAIN) GOAL | (@ FEELING TRUTH VALUES ATOMS e ATOMS ATOM SPACE PATTERN MINER PATTERN IMPRINTER PERCEPTION HIERARCHY LANGUAGE LANGUAGE COMPREHENSION GENERATION HERARCHY HIERARCHY, SENSORS ~ ACTUATORS Fig. 6.1: High-Level Architecture of CogPrime. This is a conceptual depiction, not a detailed flowchart (which would be too complex for a single image). Figures 6.2 , 6.4 and 6.5 highlight specific aspects of this diagram. e Learning to build structures resembling structures that it’s shown (even if the available materials are a bit different) e Learning how to build bridges to cross chasms Of course, the AI significance of learning tasks like this all depends on what kind of feedback the system is given, and how complex its environment is. It would be relatively simple to make an AI system do things like this in a trivial and highly specialized way, but that is not the intent of the project the goal is to have the system learn to carry out tasks like this using general learning mechanisms and a general cognitive architecture, based on embodied experience and HOUSE_OVERSIGHT_013025