1 code implementation • 16 Mar 2023 • Giulio Mazzi, Daniele Meli, Alberto Castellini, Alessandro Farinelli
In this paper, we use inductive logic programming to learn logic specifications from traces of POMCP executions, i. e., sets of belief-action pairs generated by the planner.
1 code implementation • 28 Apr 2021 • Giulio Mazzi, Alberto Castellini, Alessandro Farinelli
Results show that the shielded POMCP outperforms the standard POMCP in a case study in which a wrong parameter of POMCP makes it select wrong actions from time to time.
1 code implementation • 23 Dec 2020 • Giulio Mazzi, Alberto Castellini, Alessandro Farinelli
We propose an iterative process of trace analysis consisting of three main steps, i) the definition of a question by means of a parametric logical formula describing (probabilistic) relationships between beliefs and actions, ii) the generation of an answer by computing the parameters of the logical formula that maximize the number of satisfied clauses (solving a MAX-SMT problem), iii) the analysis of the generated logical formula and the related decision boundaries for identifying unexpected decisions made by POMCP with respect to the original question.