This paper explores the interaction between human and artificial problem solvers when interacting with an Intelligent Scheduling
System. An experimental study is presented aimed at investigating the users’ attitude towards two alternative strategies for
solving scheduling problems: automated and interactive. According to an automated strategy the responsibility of solving the
problem is delegated to the artificial solver, while according to an interactive strategy human and automated solvers cooperate
to achieve a problem solution.
Previous observations of end-users’ reactions to problem solving systems have shown that users are often skeptical toward
artificial solver performance and prefer to keep the control of the problem solving process. The current study aims at understanding
the role played by both the users’ expertise and the difficulty of the problem in choosing one of the two strategies. Results
show that user expertise and task difficulty interact in influencing this choice.
A second aspect explored in the paper concerns the context in which the end-users rely on explanations to understand the solving
process. Explanations are in fact expected to play an important role when artificial systems are used for cooperative and
interactive problem solving. Results support the hypothesis that explanation services are more often called into play in case
of problem solving failures.
This research is partially supported by MIUR (Italian Ministry of Education, University and Research) under project RoboCare (A Multi-Agent System with Intelligent Fixed and Mobile Robotic Components).