Cognitive Memory for Semantic Agents Architecture in Robotic Interaction

Abstract

Since 1960, AI researchers have worked on intelligent and reactive architectures capable of managing multiple events and acts in the environment. This issue is part of the Robotics domain. An extraction of meaning at different levels of abstraction and the decision process must be implemented in the robot brain to accomplish the multimodal interaction with humans in a human environment. This paper presents a semantic agents architecture giving the robot the ability to understand what is happening and thus provide more robust responses. Intelligence and knowledge about objects like behaviours in the environment are stored in two ontologies linked to an inference engine. To store and exchange information, an event knowledge representation language is used by semantic agents. This architecture brings other advantages: pervasive, cooperating, redundant, automatically adaptable, and interoperable. It is independent of platforms.

A Review of Norms and Normative Multiagent Systems

“…Norm detection is inspired by the process of norm learning [ 7 , 40 , 115 ] and norm cognition [ 40 ]. Several studies have been made by researchers on norm learning based on mechanisms of imitation [ 70 , 87 , 116 ]; social learning [ 3 , 117 – 120 ] case-based reasoning [ 121 ]; and data mining [ 122 – 124 ]. Others have worked on norm cognition [ 82 , 123 , 124 ].…”