activity
20132022
most citedRestricted Manipulation in Iterative Voting: Convergence and Condorcet Efficiency

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

collaborators

5 papers

cs.LG2022

Learning Behavioral Soft Constraints from Demonstrations

Arie Glazier, Andrea Loreggia, Nicholas Mattei +3

Many real-life scenarios require humans to make difficult trade-offs: do we always follow all the traffic rules or do we violate the speed limit in an emergency? These scenarios fo…

cs.AI2020

Modeling Contrary-to-Duty with CP-nets

Roberta Calegari, Andrea Loreggia, Emiliano Lorini +2

In a ceteris-paribus semantics for deontic logic, a state of affairs where a larger set of prescriptions is respected is preferable to a state of affairs where some of them are vio…

cs.LG2018

CPMetric: Deep Siamese Networks for Learning Distances Between Structured Preferences

Andrea Loreggia, Nicholas Mattei, Francesca Rossi +1

Preference are central to decision making by both machines and humans. Representing, learning, and reasoning with preferences is an important area of study both within computer sci…

cs.AI20151 cited

Logical Conditional Preference Theories

Cristina Cornelio, Andrea Loreggia, Vijay Saraswat

CP-nets represent the dominant existing framework for expressing qualitative conditional preferences between alternatives, and are used in a variety of areas including constraint s…

cs.AI20132 cited

Restricted Manipulation in Iterative Voting: Convergence and Condorcet Efficiency

Umberto Grandi, Andrea Loreggia, Francesca Rossi +2

In collective decision making, where a voting rule is used to take a collective decision among a group of agents, manipulation by one or more agents is usually considered negative…