Approximate Processing in Real-Time Problem Solving

Abstract

WE PROPOSE AN APPROACH FOR MEETING REAL-TIME CONSTRAINTS IN AI SYSTEMS THAT VIEWS (1) TIME AS A RESOURCE THAT SHOULD BE CONSIDERED WHEN MAKING CONTROL DECISIONS, (2) PLANS AS WAYS OF EXPRESSING CONTROL DECISIONS, AND (3) APPROXIMATE PROCESSING AS A WAY OF SATISFYING TIME CONSTRAINTS THAT CANNOT BE ACHIEVED THROUGH NORMAL PROCESSING. IN THIS APPROACH, A REAL-TIME PROBLEM SOLVER ESTIMATES THE TIME REQUIRED TO GENERATE SOLUTIONS AND THEIR QUALITY. THIS ESTIMATE PERMITS THE SYSTEM TO ANTICIPATE WHETHER THE CURRENT OBJECTIVES WILL BE MET IN TIME. THE SYSTEM CAN THEN TAKE CORRECTIVE ACTION BY FORMING LOWER QUALITY SOLUTIONS WITHIN TIME CONSTRAINTS. THIS MAY INVOLVE MODIFYING EXISTING PLANS OR FORMING RADICALLY DIFFERENT PLANS THAT UTILIZE ONLY ROUGH DATA CHARACTERISTICS AND APPROXIMATE KNOWLEDGE TO ACHIEVE A DESIRED SPEEDUP. A DECISION ABOUT HOW TO CHANGE PROCESSING SHOULD BE SITUATION-DEPENDENT, BASED ON THE CURRENT STATE OF PROCESSING AND ON DOMAIN-DEPENDENT SOLUTION CRITERIA. WE PRESENT PRELIMINARY EXPERIMENTS THAT SHOW HOW APPROXIMATE PROCESSING HELPS A VEHICLE-MONITORING PROBLEM SOLVER MEET DEADLINES, AND OUTLINE A FRAMEWORK FOR FLEXIBLY MEETING REAL-TIME CONSTRAINTS.