Data Set Used
This paper proposes a framework for planning under uncertainty given a partially known initial state and a set of actions having nondeterministic (disjunctive) eeects, some being more possible (normal) than the others. The problem, henceforth called possibilistic planning problem, is represented in an extension of the STRIPS formalism in which the initial… (More)
In brief, this license authorizes each and everybody to share (to copy, distribute and transmit) the work under the following conditions, without impairing or restricting the authors' moral rights: Attribution: The work must be attributed to its authors. The series Dagstuhl Follow-Ups is a publication format which offers a frame for the publication of… (More)
A rational agent revises its goals if something changes in its mental state. In this paper, we propose (i) a general framework based on classical propositional logic, to represent changes in the mental state of the agent after the acquisition of new information and/or after the arising of new desires; (ii) fundamental postulates that the function which… (More)
We address the issue, in cognitive agents, of possible loss of previous information, which later might turn out to be correct when new information becomes available. To this aim, we propose a framework for changing the agent's mind without erasing forever previous information, thus allowing its recovery in case the change turns out to be wrong. In this new… (More)
We propose a general framework to represent changes in the mental state of a rational agent due to the acquisition of new information and/or to the arising of new desires; fundamental postulates and properties of the function which generates the goal set are also provided.
We extend hybrid possibilistic conditioning to deal with inputs consisting of a set of triplets composed of propositional formulas, the level at which the formulas should be accepted, and the way in which their models should be revised. We characterize such conditioning using elementary operations on possibility distributions. We then solve a difficult… (More)