A DISTRIBUTED ARTIFICIAL INTELLIGENCE REPRESENTATION LANGUAGE LANGUAGE

THE NEXT GENERATION OF ARTIFICIAL INTELLIGENCE DEVELOPMENT ENVIRONMENTS MUST BE ABLE TO INCORPORATE A WIDE RANGE OF REPRESENTATION AND REASONING TECHNOLOGIES IN A GENERAL FRAMEWORK. WE ARE PROPOSING THE DEVELOPMENT OF A TAXONOMIC AND DESCRIPTIVE REPRESENTATION LANGUAGE LANGUAGE (RLL). A RLL THAT CAN CAPTURE THE SEMANTIC DESCRIPTION OF AN ARTIFICIAL INTELLIGENCE SYSTEM'S SUPPORTING SUBSTRATES WILL EXTEND THE CURRENT GENERATION OF AI-SYSTEMS' ABILITY TO UNDERSTAND AND MODIFY THEMSELVES. THIS RLL WILL ENABLE AN INTELLIGENT SYSTEM TO REFLECTIVELY REASON ABOUT THE PROPERTIES OF ITS KNOWLEDGE REPRESENTATION, METHODS OF REASONING, AND METHODS OF COMMUNICATION. SECONDLY, AND OF MORE IMMEDIATE NEED, THE SAME SEMANTIC POWER OF EXPRESSION THAT ALLOWS AN AI-SYSTEM TO REASON WITH ITS OWN REPRESENTATION PRESENTS A HOMOGENEOUS DEVELOPMENT ENVIRONMENT FOR A HUMAN AI-SYSTEM ARCHITECT OR PROGRAMMER. THE EMPHASIS OF THE EFFORT WILL BE TO INVESTIGATE AND DEVELOP THE BASIC FEATURES THAT A RLL REQUIRES FOR SUPPORTING AN EFFICIENT DISTRIBUTED ARTIFICIAL INTELLIGENCE ENVIRONMENT FOR THE RAPID PROTOTYPING OF EXPERIMENTAL SYSTEMS. THE HOMOGENEITY AND SEMANTIC DESCRIPTIVE POWER OF DEVELOPMENT ENVIRONMENT BASED ON A RLL ALLOWS NEWLY DEVELOPED OR EXISTING ARTIFICIAL TECHNOLOGIES TO BE EASILY INCORPORATED INSTEAD OF BEING REIMPLEMENTED.