Abstract:
Learning, memorizing and recalling knowledge are
the basic functions of cognitive models. These models must
prioritize which stimulants to respond to as well as package
acquired knowledge in an easy to retrieve manner. The human
brain is a cognitive model that derives information from sensor
data such as vision, associates different patterns to create
knowledge, and uses chunking mechanisms to package the
acquired knowledge in manageable entities. The use of chunking
mechanisms by the brain aids it to overcome its short-term
memory (STM) capacity limitation. Through chunking, each
entity held in the STM is a chunk containing more associations
(knowledge) in it. By mimicking the human brain, this study
proposes an associative memory and recall (AMR) model that
stores associative knowledge from sensor data. Using chunking
mechanisms, AMR can organize human activity knowledge in the
manner that is efficient and effective to store and recall. The
knowledge-information-data (KID) model is used for learning
associative knowledge while the AMR continuously looks for
associations among knowledge units and merges related units
using merging mechanisms. The chunking mechanisms used in
this study are inspired by the chunking mechanisms of the brain
i.e. goal oriented chunking and automatic chunking.
