Prognostic normative reasoning in coalition planning (Extended Abstract)
Jean Oh, Felipe Rech Meneguzzi, Katia P. Sycara, Timothy J. Norman · 2011
INTRODUCTION Human users planning for multiple objectives in coalition environments are subjected to high levels of cognitive workload, which can severely impair the quality of the plans created. The cognitive workload is significantly increased when a user must not only cope with a complex environment, but also with a set of unaccustomed rules that prescribe how the coalition planning process must be carried out. In this context, we develop a prognostic assistant agent that takes a proactive stance in assisting cognitively overloaded human users by providing timely support for normative reasoning– reasoning about prohibitions and obligations. Existing work on automated norm management relies on a deterministic view of the planning model [1], where norms are specified in terms of classical logic; in this approach, violations are detected only after they have occurred, consequently assistance can only be provided after the user has already committed actions that caused the violation [3]. By contrast, our agent predicts potential future violations and proactively takes action to help prevent the user from violating the norms. Here, we introduce the notion of prognostic normative reasoning so that the agent can reason about norm-compliant planning in advance. In order for that, we use probabilistic plan recognition to predict the user’s future plan steps based on the user’s current ∗This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence and was accomplished under Agreement Number W911NF-09-2-0053. The views and conclusions contained in this document are those of the author(s) and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.