A QUANTITATIVE MODEL FOR ADAPTIVE TASK ALLOCATION IN HUMAN-COMPUTER INTERFACES

The paper describes the design and implementation of a task allocation for a human-computer interface that is capable of adapting the human's workload, on-line, according to his ability to perform tasks for systems where the computer acts as a backup decision maker. During the operation of the system, the human and the computer's service and error rates are continually measured. An embedded human model estimates the change in the human's ability to service his tasks based on the measured service and error rates. Future human and computer service rates and task arrival rates in the system are then predicted. Every time a new task arrives in the system the interface decides whether the human is capable of dealing with all current tasks before the next task arrives. If the human is unable to deal with the workload, the computer is switched on to aid him. An explicit task allocation for both servers is calculated. Tasks are then allocated to both servers and their performances measured while they service their tasks. The interface is broken down into modules, with each module's quantitative behavior defined separately. A qualitative assessment of the interface's overall behavior proves that the interface will adapt the human's workload to his ability in a desired way, when all the modules are combined. A probabilistic sensitivity analysis of the task allocation equations develops metrics that can be used to assess the task allocation's robustness with regard to uncertainties in the predicted human and system performances. The interface is implemented in a real time, human operated surveillance system. Results show that both the human's performance and especially the overall system performance are enhanced when the interface aids the human during operation.