318 citations · 417 across the 6 of their papers we have counts for
7 papers
Goal Recognition as a Deep Learning Task: the GRNet Approach
Mattia Chiari, Alfonso E. Gerevini, Luca Putelli +2
In automated planning, recognising the goal of an agent from a trace of observations is an important task with many applications. The state-of-the-art approaches to goal recognitio…
Prognosis Prediction in Covid-19 Patients from Lab Tests and X-ray Data through Randomized Decision Trees
Alfonso Emilio Gerevini, Roberto Maroldi, Matteo Olivato +2
AI and Machine Learning can offer powerful tools to help in the fight against Covid-19. In this paper we present a study and a concrete tool based on machine learning to predict th…
Novelty Messages Filtering for Multi Agent Privacy-preserving Planning
Alfonso E. Gerevini, Nir Lipovetzky, Nico Peli +3
In multi-agent planning, agents jointly compute a plan that achieves mutual goals, keeping certain information private to the individual agents. Agents' coordination is achieved th…
Best-First Width Search for Multi Agent Privacy-preserving Planning
Alfonso E. Gerevini, Nir Lipovetzky, Francesco Percassi +2
In multi-agent planning, preserving the agents' privacy has become an increasingly popular research topic. For preserving the agents' privacy, agents jointly compute a plan that ac…
Case-Based Merging Techniques in OAKPLAN
Anna Roubíčková, Ivan Serina
Case-based planning can take advantage of former problem-solving experiences by storing in a plan library previously generated plans that can be reused to solve similar planning pr…
An Approach to Temporal Planning and Scheduling in Domains with Predictable Exogenous Events
A. Gerevini, A. Saetti, I. Serina
The treatment of exogenous events in planning is practically important in many real-world domains where the preconditions of certain plan actions are affected by such events. In th…