activity
20172022
most citedMarkov Abstractions for PAC Reinforcement Learning in Non-Markov Decision Processes

3 citations · 7 across the 7 of their papers we have counts for

collaborators

11 papers

cs.LG20223 cited

Markov Abstractions for PAC Reinforcement Learning in Non-Markov Decision Processes

Alessandro Ronca, Gabriel Paludo Licks, Giuseppe De Giacomo

Our work aims at developing reinforcement learning algorithms that do not rely on the Markov assumption. We consider the class of Non-Markov Decision Processes where histories can…

cs.AI20211 cited

Recognizing LTLf/PLTLf Goals in Fully Observable Non-Deterministic Domain Models

Ramon Fraga Pereira, Francesco Fuggitti, Giuseppe De Giacomo

Goal Recognition is the task of discerning the correct intended goal that an agent aims to achieve, given a set of possible goals, a domain model, and a sequence of observations as…

cs.LO2021

Behavioral QLTL

Giuseppe De Giacomo, Giuseppe Perelli

In this paper we introduce Behavioral QLTL, which is a ``behavioral'' variant of linear-time temporal logic on infinite traces with second-order quantifiers. Behavioral QLTL is cha…

cs.AI2019

Stochastic Fairness and Language-Theoretic Fairness in Planning on Nondeterministic Domains

Benjamin Aminof, Giuseppe De Giacomo, Sasha Rubin

We address two central notions of fairness in the literature of planning on nondeterministic fully observable domains. The first, which we call stochastic fairness, is classical, a…

cs.AI2019

LTLf Synthesis with Fairness and Stability Assumptions

Shufang Zhu, Giuseppe De Giacomo, Geguang Pu +1

In synthesis, assumptions are constraints on the environment that rule out certain environment behaviors. A key observation here is that even if we consider systems with LTLf goals…

cs.AI20191 cited

Generalized Planning: Non-Deterministic Abstractions and Trajectory Constraints

Blai Bonet, Giuseppe De Giacomo, Hector Geffner +1

We study the characterization and computation of general policies for families of problems that share a structure characterized by a common reduction into a single abstract problem…