activity
20182023
most citedReinforcement Learning for Combining Search Methods in the Calibration of Economic ABMs

9 citations · 24 across the 5 of their papers we have counts for

collaborators

8 papers

cs.LG2023★ 1 cited

Exploiting Multiple Abstractions in Episodic RL via Reward Shaping

Roberto Cipollone, Giuseppe De Giacomo, Marco Favorito +2

One major limitation to the applicability of Reinforcement Learning (RL) to many practical domains is the large number of samples required to learn an optimal policy. To address th…

cs.LO2023★ 4 cited

Forward LTLf Synthesis: DPLL At Work

Marco Favorito

This paper proposes a new AND-OR graph search framework for synthesis of Linear Temporal Logic on finite traces (\LTLf), that overcomes some limitations of previous approaches. Wit…

cs.LG2023★ 9 cited

Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMs

Aldo Glielmo, Marco Favorito, Debmallya Chanda +1

Calibrating agent-based models (ABMs) in economics and finance typically involves a derivative-free search in a very large parameter space. In this work, we benchmark a number of s…

cs.DC2022★ 4 cited

A PoW-less Bitcoin with Certified Byzantine Consensus

Marco Benedetti, Francesco De Sclavis, Marco Favorito +4

Distributed Ledger Technologies (DLTs), when managed by a few trusted validators, require most but not all of the machinery available in public DLTs. In this work, we explore one p…

cs.AI2022★ 6 cited

Planning for Temporally Extended Goals in Pure-Past Linear Temporal Logic: A Polynomial Reduction to Standard Planning

Giuseppe De Giacomo, Marco Favorito, Francesco Fuggitti

We study temporally extended goals expressed in Pure-Past LTL (PPLTL). PPLTL is particularly interesting for expressing goals since it allows to express sophisticated tasks as in t…

cs.LO2022

On the Relationship between Shy and Warded Datalog+/-

Teodoro Baldazzi, Luigi Bellomarini, Marco Favorito +1

Datalog^E is the extension of Datalog with existential quantification. While its high expressive power, underpinned by a simple syntax and the support for full recursion, renders i…