no code implementations • 11 Jul 2023 • James Fox, Matt MacDermott, Lewis Hammond, Paul Harrenstein, Alessandro Abate, Michael Wooldridge
Multi-agent influence diagrams (MAIDs) are a popular game-theoretic model based on Bayesian networks.
no code implementations • 5 Jan 2023 • Lewis Hammond, James Fox, Tom Everitt, Ryan Carey, Alessandro Abate, Michael Wooldridge
Regarding question iii), we describe correspondences between causal games and other formalisms, and explain how causal games can be used to answer queries that other causal or game-theoretic models do not support.
1 code implementation • 28 Dec 2022 • Joar Skalse, Lewis Hammond, Charlie Griffin, Alessandro Abate
In this work we introduce reinforcement learning techniques for solving lexicographic multi-objective problems.
Multi-Objective Reinforcement Learning
reinforcement-learning
no code implementations • 30 Sep 2022 • Daniel Jarne Ornia, Licio Romao, Lewis Hammond, Manuel Mazo Jr., Alessandro Abate
Policy gradient algorithms that have strong convergence guarantees are usually modified to obtain robust policies in ways that do not preserve algorithm guarantees, which defeats the purpose of formal robustness requirements.
no code implementations • 19 Jul 2021 • Julian Gutierrez, Lewis Hammond, Anthony W. Lin, Muhammad Najib, Michael Wooldridge
Rational verification is the problem of determining which temporal logic properties will hold in a multi-agent system, under the assumption that agents in the system act rationally, by choosing strategies that collectively form a game-theoretic equilibrium.
1 code implementation • 9 Feb 2021 • Lewis Hammond, James Fox, Tom Everitt, Alessandro Abate, Michael Wooldridge
Multi-agent influence diagrams (MAIDs) are a popular form of graphical model that, for certain classes of games, have been shown to offer key complexity and explainability advantages over traditional extensive form game (EFG) representations.
1 code implementation • 1 Feb 2021 • Lewis Hammond, Alessandro Abate, Julian Gutierrez, Michael Wooldridge
In this paper, we study the problem of learning to satisfy temporal logic specifications with a group of agents in an unknown environment, which may exhibit probabilistic behaviour.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
no code implementations • 8 Oct 2018 • Lewis Hammond, Vaishak Belle
From the viewpoint of such systems, the urgent questions are: (a) How can models of moral scenarios and blameworthiness be extracted and learnt automatically from data?