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Found 10 papers, 1 papers with code
Safe Reinforcement Learning via Probabilistic Logic Shields
To this end, we introduce Probabilistic Logic Policy Gradient (PLPG).
Learning Probabilistic Temporal Safety Properties from Examples in Relational Domains
The task then consists of learning this unknown formula from states that are labeled as safe or unsafe by a domain expert.
Online Learning of Non-Markovian Reward Models
In our formal setting, we consider a Markov decision process (MDP) that models the dynamics of the environment in which the agent evolves and a Mealy machine synchronized with this MDP to formalize the non-Markovian reward function.
Reputation-driven Decision-making in Networks of Stochastic Agents
This paper studies multi-agent systems that involve networks of self-interested agents.
Learning Non-Markovian Reward Models in MDPs
There are situations in which an agent should receive rewards only after having accomplished a series of previous tasks.
Maximizing Expected Impact in an Agent Reputation Network -- Technical Report
We propose a new POMDP-based framework which is general enough for the specification of a variety of stochastic MAS domains involving the impact of agents on each other's reputations.
Imagining Probabilistic Belief Change as Imaging (Technical Report)
Imaging is a form of probabilistic belief change which could be employed for both revision and update.
A Hybrid POMDP-BDI Agent Architecture with Online Stochastic Planning and Plan Caching
This article presents an agent architecture for controlling an autonomous agent in stochastic environments.
Revising Incompletely Specified Convex Probabilistic Belief Bases
In this work, an agent's beliefs are represented by a set of probabilistic formulae -- a belief base.
On Stochastic Belief Revision and Update and their Combination
I propose a framework for an agent to change its probabilistic beliefs when a new piece of propositional information $\alpha$ is observed.
