Coming up With Good Excuses: What to do When no Plan Can be Found

By Moritz Göbelbecker, Thomas Keller, Patrick Eyerich, Michael Brenner, Bernhard Nebel. 2010

140 citations recorded by the source corpus.

When using a planner-based agent architecture, many things can go wrong. First and foremost, an agent might fail to execute one of the planned actions for some reasons. Even more annoying, however, is a situation where the agent is incompetent, i.e., unable to come up with a plan. This might be due to the fact that there are principal reasons that prohibit a successful plan or simply because the task's description is incomplete or incorrect. In either case, an explanation for such a failure would be very helpful. We will address this problem and provide a formalization of coming up with excuses for not being able to find a plan. Based on that, we will present an algorithm that is able to find excuses and demonstrate that such excuses can be found in practical settings in reasonable time.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Plan explicability and predictability for robot task planning (2015, methodology): “ Furthermore, as an extension to our work, when robots cannot ﬁnd an explicable plan that is also cost efﬁcient, they need to explain the situation. In this regard, our work is also related to excuse [9] and explanation generation [10]. Finally, while our learning approach appears to be similar to i”
Preferred Explanations: Theory and Generation via Planning (2011, background): “These include narrative understanding , plan recognition (Ramírez and Geffner 2009), finding excuses (Göbelbecker et al. 2010), and diagnosis (e.g., Sohrabi, Baier, and McIlraith 2010; Grastien et al. 2007).”