Mixed-Initiative Management of Online Calendars

Abstract

Abstract. Calendar management has been recognized as a complex, highly personal type of activity, which must take individual preferences and constraints into account in the formulation of satisfactory schedules. Current calendar management services are affected by two limitations: most of them lack any reasoning capabilities and thus cannot help the user in the management of tight schedules, which make the allocation of new tasks particularly challenging. Others are too impositive because they proactively schedule events without involving the user in the decision process. In order to address such issues, we propose a mixed-initiative approach which enables the user to select the events to be considered, receive safe schedule suggestions from the system and select the preferred ones for revising a calendar. A peculiarity of our system is the fact that, in the suggestion of alternative schedules for an event, it searches for solutions which are very similar to the user's current schedule, with the aim of limiting changes to her/his daily plans as much as possible. Our calendar management service is based on the exploitation of well-known Temporal Constraint Satisfaction Problems techniques, which guarantee the generation of safe scheduling solutions.

Computational design of mixed-initiative human–robot teaming that considers human factors: situational awareness, workload, and workflow preferences

“…Human–robot teaming has the potential to increase the productivity of human labor and improve the ergonomics of manual tasks. Based on recent industry interest in fielding human–robot teams, researchers have been investigating how best to include a human participant in the decision-making loop as a member of a human–robot team (Adams, 2009; Ardissono et al, 2012; Barnes et al, 2011; Clare et al, 2012; Dragan and Srinivasa, 2012; Goodrich et al, 2009; Herlant et al, 2016; Hooten et al, 2011; Pierce and Kuchenbecker, 2012; Sanderson, 1989; Zhang et al, 2012). However, the intricate choreography required to coordinate human-robot teams safely and efficiently represents a challenging computational problem.…”

“…In such work, researchers often view the human operator as a vital component of the decision-making loop, particularly when this operator has knowledge of factors that are difficult to capture within a manually encoded, autonomous framework (Clare et al, 2012; Cummings et al, 2007; Durfee et al, 2014). Complementary to approaches that include the human operator in the loop, other work has focused on the development of computational methods able to generate scheduling solutions using information collected a priori from human experts (Ardissono et al, 2012; Hamasaki et al, 2004; Haynes et al, 1997; Macho et al, 2000; Zhang et al, 2012).…”