Semantic model for artificial intelligence based on molecular computing

Abstract

In this work, a new DNA-based semantic model is proposed and described theoretically. This model, referred to as semantic model based on molecular computing (SMC) has the structure of a graph formed by the set of all attribute-value pairs contained in the set of represented objects, plus a tag node for each object. Attribute layers composed of attribute values then line up. Each path in the network, from an initial object-representing tag node to a terminal node represents the object named on the tag. Application of the model to a reasoning system was proposed, via virtual DNA operation. On input, object-representing dsDNAs will be formed via parallel self-assembly, from encoded ssDNAs representing (value, attribute)-pairs (nodes), as directed by ssDNA splinting strands representing relations (edges) in the network. The computational complexity of the implementation is estimated via simple simulation, which indicates the advantage of the approach over a simple sequential model.