Computational infrastructures for cooperative work should contain embedded agents for handling many routine tasks (Galegher, Kraut, Egldo 1990), but as the number of agents increases and the agents become geographically and/or conceptually dispersed, supervision of the agents will become increasingly problematic. We argue that agents should be provided with deep domain knowledge that allows them to make justifiable decisions, rather than shallow models of users to mimic. In this paper, we use the application domain of distributed meeting scheduling to investigate how agents embodying deeper domain knowledge can choose among alternative strategies for searching their calendars in order to create flexible schedules within reasonable cost.
['Sandip Sen', 'Edmund H. Durfee']
Unsupervised Surrogate Agents and Search Bias Change in Flexible Distributed Scheduling