AN EFFICIENT LINEAR LANGUAGE FOR PARALLEL SYMBOLIC COMPUTATION

NEXT-GENERATION INTELLIGENT SYSTEMS AND ROBOTS REQUIRE THE POWER OF PARALLEL COMPUTERS, YET MOST NOVEL PARALLEL LANGUAGES ARE GEARED PRIMARILY FOR NUMERICAL CALCULATIONS; E.G., SIGNAL PROCESSING, IN WHICH THE DATA VALUES ARE ELEMENTARY NUMERICAL SAMPLES. THESE MODELS OF COMPUTATION FAIL FOR CALCULATIONS INVOLVING THE HIGHLY-STRUCTURED SYMBOLIC DATA REQUIRED FOR ARTIFICIAL INTELLIGENCE APPLICATIONS. A LINEAR LANGUAGE PROVIDES AN ELEGANT AND INTUTITIVE MECHANICAL MODEL OF PARALLEL COMPUTATION IN WHICH STRUCTURED VALUES ARE TRANSMITTED AS SIMPLY AS ELEMENTARY VALUES. THESE LINEAR DATA STRUCTURES RETAIN THE ABILITY TO TRANSMIT HIGHLY STRUCTURED INFORMATION, YET AVOID THE PROBLEMS OF ALIASING AND SHARING. SOME PROMISING NEW IMPLEMENTATION TECHNIQUES ARE BEING DEVELOPED. RESEARCHERS ARE INVESTIGATING THE ABILITY OF A NOVEL IMPLEMENTATION APPROACH TO PROVIDE MOST OF THE EFFICIENCY OF NONLINEAR SHARED DATA STRUCTURES WHILE PRESERVING THE ELEGANT LINEAR DATA MODEL.