9 citations · 9 across the 3 of their papers we have counts for
9 papers · 1 filter
Learning Probabilistic Temporal Safety Properties from Examples in Relational Domains
Gavin Rens, Wen-Chi Yang, Jean-François Raskin +1
We propose a framework for learning a fragment of probabilistic computation tree logic (pCTL) formulae from a set of states that are labeled as safe or unsafe. We work in a relatio…
Online Learning of Non-Markovian Reward Models
Gavin Rens, Jean-François Raskin, Raphaël Reynouad +1
There are situations in which an agent should receive rewards only after having accomplished a series of previous tasks, that is, rewards are non-Markovian. One natural and quite g…
Reputation-driven Decision-making in Networks of Stochastic Agents
David Maoujoud, Gavin Rens
This paper studies multi-agent systems that involve networks of self-interested agents. We propose a Markov Decision Process-derived framework, called RepNet-MDP, tailored to domai…
Learning Non-Markovian Reward Models in MDPs
Gavin Rens, Jean-François Raskin
There are situations in which an agent should receive rewards only after having accomplished a series of previous tasks. In other words, the reward that the agent receives is non-M…
Maximizing Expected Impact in an Agent Reputation Network -- Technical Report
Gavin Rens, Abhaya Nayak, Thomas Meyer
Many multi-agent systems (MASs) are situated in stochastic environments. Some such systems that are based on the partially observable Markov decision process (POMDP) do not take th…
Imagining Probabilistic Belief Change as Imaging (Technical Report)
Gavin Rens, Thomas Meyer
Imaging is a form of probabilistic belief change which could be employed for both revision and update. In this paper, we propose a new framework for probabilistic belief change bas…