Applying an Architecture for General Intelligence to Reduce Scheduling Effort

Abstract

A system called Merle-Soar is described which demonstrates how a specific architecture for general intelligence and learning (Soar) can be used to reduce scheduling effort when solving simple scheduling problems. In particular, we describe how Merle-Soar schedules sequences of jobs on a single bottleneck machine in a job shop. The knowledge of dispatching, acquired from examining how a human expert performs the task, is cast as search rules. A study was conducted which examined the extent to which learning could contribute to decreases in scheduling effort; specifically, the contribution of learning within-tasks was explored—the change in reasoning effort while solving a particular scheduling problem as knowledge is accumulated from successive trials. The results indicated that dramatic reductions in scheduling effort (in terms of the Soar architecture) were obtained. Knowledge gained early in the scheduling task was subsequently applied later in the task to reduce deliberation, and knowledge gained from one trial successfully reduced deliberation effort in subsequent trials. Additionally, the reduction exhibited the general power law of learning documented in psychological studies of skill acquisition. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.