881 citations · 1.7k across the 23 of their papers we have counts for
22 papers · 1 filter
Learning and Solving Regular Decision Processes
Eden Abadi, Ronen I. Brafman
Regular Decision Processes (RDPs) are a recently introduced model that extends MDPs with non-Markovian dynamics and rewards. The non-Markovian behavior is restricted to depend on r…
Reinforcement Learning with Non-Markovian Rewards
Maor Gaon, Ronen I. Brafman
The standard RL world model is that of a Markov Decision Process (MDP). A basic premise of MDPs is that the rewards depend on the last state and action only. Yet, many real-world r…
Privacy Preserving Multi-Agent Planning with Provable Guarantees
Amos Beimel, Ronen I. Brafman
In privacy-preserving multi-agent planning, a group of agents attempt to cooperatively solve a multi-agent planning problem while maintaining private their data and actions. Althou…
A Planning Approach to Monitoring Behavior of Computer Programs
Alexandre Cukier, Ronen I. Brafman, Yotam Perkal +1
We describe a novel approach to monitoring high level behaviors using concepts from AI planning. Our goal is to understand what a program is doing based on its system call trace. T…
Specifying Non-Markovian Rewards in MDPs Using LDL on Finite Traces (Preliminary Version)
Ronen Brafman, Giuseppe De Giacomo, Fabio Patrizi
In Markov Decision Processes (MDPs), the reward obtained in a state depends on the properties of the last state and action. This state dependency makes it difficult to reward more…
Replanning in Domains with Partial Information and Sensing Actions
Ronen I. Brafman, Guy Shani
Replanning via determinization is a recent, popular approach for online planning in MDPs. In this paper we adapt this idea to classical, non-stochastic domains with partial informa…