M.Gh. Negoita et al. (Eds.): KES 2004, LNAI 3213, pp. 32–39, 2004. © Springer-Verlag Berlin Heidelberg 2004 Semantic Model for Artificial Intelligence Based on Molecular Computing Yusei Tsuboi, Zuwairie Ibrahim, and Osamu Ono Control System Laboratory, Institute of Applied DNA Computing, Graduate School of Science & Technology, Meiji University, 1-1-1, Higashimita, Tama-ku, Kawasaki-shi, Kanagawa, 214-8671 Japan {tsuboi, zuwairie, ono}@isc.meiji.ac.jp Abstract. In this work, a new DNA-based semantic model is proposed and de- scribed theoretically. This model, referred to as ‘semantic model based on mo- lecular 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 rea- soning 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 computa- tional complexity of the implementation is estimated via simple simulation, which indicates the advantage of the approach over a simple sequential model. 1 Introduction Our research group focuses on developing a semantic net (semantic network) [1] via a new computational paradigm. Human information processing often involves compar- ing concepts. There are various ways of assessing concept similarity, which vary depending on the adopted model of knowledge representation. In featural representa- tions, concepts are represented by sets of features. In Quillian’s model of semantic memory, concepts are represented by relationship name via links. Links are labeled by the name of the relationship and are assigned “criteriality tags” that attest to the importance of link. In artificial computer implementations, criteriality tags are nu- merical values that represent the degree of association between concept pairs (i.e., how often the link is traversed), and the nature of the association. The association is positive if the existence of that link indicates some sort of similarity between the end nodes, and negative otherwise. For example, superordinate links (the term used for ‘is-a…’ relationships) have a positive association, while ‘is-not-a…’ links have a negative association. Just as there are at least two research communities that deal necessarily with questions of generalization in science, there are at least two bodies of