11 citations · 37 across the 13 of their papers we have counts for
15 papers
Argumentative Reward Learning: Reasoning About Human Preferences
Francis Rhys Ward, Francesco Belardinelli, Francesca Toni
We define a novel neuro-symbolic framework, argumentative reward learning, which combines preference-based argumentation with existing approaches to reinforcement learning from hum…
Model Checking Strategic Abilities in Information-sharing Systems
Francesco Belardinelli, Ioana Boureanu, Catalin Dima +1
We introduce a subclass of concurrent game structures (CGS) with imperfect information in which agents are endowed with private data-sharing capabilities. Importantly, our CGSs are…
Reasoning about Human-Friendly Strategies in Repeated Keyword Auctions
Francesco Belardinelli, Wojtek Jamroga, Vadim Malvone +3
In online advertising, search engines sell ad placements for keywords continuously through auctions. This problem can be seen as an infinitely repeated game since the auction is ex…
An Abstraction-based Method to Check Multi-Agent Deep Reinforcement-Learning Behaviors
Pierre El Mqirmi, Francesco Belardinelli, Borja G. León
Multi-agent reinforcement learning (RL) often struggles to ensure the safe behaviours of the learning agents, and therefore it is generally not adapted to safety-critical applicati…
Aggregating Bipolar Opinions (With Appendix)
Stefan Lauren, Francesco Belardinelli, Francesca Toni
We introduce a novel method to aggregate Bipolar Argumentation (BA) Frameworks expressing opinions by different parties in debates. We use Bipolar Assumption-based Argumentation (A…
A Hennessy-Milner Theorem for ATL with Imperfect Information
Francesco Belardinelli, Catalin Dima, Vadim Malvone +1
We show that a history-based variant of alternating bisimulation with imperfect information allows it to be related to a variant of Alternating-time Temporal Logic (ATL) with imper…