A proposed mechanism to memory simulation within a Semantic-Based Conversational Agent framework

This paper presents a novel Semantic-Based Conversational Agent (CA) using memory simulation to create self-awareness and character. Semantics has been identified as an important memory system within human cognition. The proposed semantic-based CA interprets scripts consisting of natural language sentences by means of a sentence similarity measure. User input is measured semantically against the content of the scripts in order to respond with an appropriate output string. Scripts are organized into contexts consisting of rules possessing a select number of natural language sentences. Through the application of semantics, a mechanism to human-like memory simulation is proposed. Currently, traditional CAs employ very primitive memory mechanisms. Such CAs use only the structural form of a sentence and take no consideration of semantic content. The semantic-based CA has produced results with mimicry akin to that of an autobiographical memory-base, thus highlighting its potential application to memory simulation.