4.2 Symbolic Cognitive Architectures 63
4.2.4 NARS
Pei Wang’s NARS logic [Wan06] played a large role in the development of PLN, CogPrime’s uncertain logic component, a relationship that is discussed in depth in [GMIH08] and won’t be re-emphasized here. However, NARS is more than just an uncertain logic, it is also an overall cognitive architecture (which is centered on NARS logic, but also includes other aspects). CogPrime bears little relation to NARS except in the specific similarities between PLN logic and NARS logic, but, the other aspects of NARS are worth briefly recounting here.
NARS is formulated as a system for processing tasks, where a task consists of a question or a piece of new knowledge. The architecture is focused on declarative knowledge, but some pieces of knowledge may be associated with executable procedures, which allows NARS to carry out control activities (in roughly the same way that a Prolog program can).
At any given time a NARS system contains
e working memory: a small set of tasks which are active, kept for a short time, and closely related to new questions and new knowledge
e long-term memory: a huge set of knowledge which is passive, kept for a long time, and not necessarily related to current questions and knowledge
The working and long term memory spaces of NARS may each be thought of as a set of chunks, where each chunk consists of a set of tasks and a set of knowledge. NARS’s basic cognitive process is:
choose a chunk
choose a task from that chunk
choose a piece of knowledge from that chunk use the task and knowledge to do inference send the new tasks to corresponding chunks
Depending on the nature of the task and knowledge, the inference involved may be one of the following:
e if the task is a question, and the knowledge happens to be an answer to the question, a copy of the knowledge is generated as a new task
backward inference
revision (merging two pieces of knowledge with the same form but different truth value) forward inference
execution of a procedure associated with a piece of knowledge
Unlike many other systems, NARS doesn’t decide what type of inference is used to process a task when the task is accepted, but works in a data-driven way — that is, it is the task and knowledge that dynamically determine what type of inference will be carried out
The “choice” processes mentioned above are done via assigning relative priorities to
e chunks (where they are called activity) e tasks (where they are called urgency) e knowledge (where they are called importance)
HOUSE_OVERSIGHT_012979