An Overview of Trustworthy AI: Advances in IP Protection, Privacy-preserving Federated Learning, Security Verification, and GAI Safety Alignment
AI has undergone a remarkable evolution journey marked by groundbreaking milestones.
Like any powerful tool, it can be turned into a weapon for devastation in the wrong hands …
Like any powerful tool, it can be turned into a weapon for devastation in the wrong hands …
[HTML][HTML] Risk-aware shielding of partially observable monte carlo planning policies
Abstract Partially Observable Monte Carlo Planning (POMCP) is a powerful online algorithm
that can generate approximate policies for large Partially Observable Markov Decision …
that can generate approximate policies for large Partially Observable Markov Decision …
Learning logic specifications for soft policy guidance in POMCP
Partially Observable Monte Carlo Planning (POMCP) is an efficient solver for Partially
Observable Markov Decision Processes (POMDPs). It allows scaling to large state spaces …
Observable Markov Decision Processes (POMDPs). It allows scaling to large state spaces …
Scalable safe policy improvement via Monte Carlo tree search
Algorithms for safely improving policies are important to deploy reinforcement learning
approaches in real-world scenarios. In this work, we propose an algorithm, called MCTS …
approaches in real-world scenarios. In this work, we propose an algorithm, called MCTS …
Partially Observable Monte Carlo Planning with state variable constraints for mobile robot navigation
Autonomous mobile robots employed in industrial applications often operate in complex and
uncertain environments. In this paper we propose an approach based on an extension of …
uncertain environments. In this paper we propose an approach based on an extension of …
Learning logic specifications for policy guidance in pomdps: an inductive logic programming approach
Abstract Partially Observable Markov Decision Processes (POMDPs) are a powerful
framework for planning under uncertainty. They allow to model state uncertainty as a belief …
framework for planning under uncertainty. They allow to model state uncertainty as a belief …
Rule-based shielding for partially observable Monte-Carlo planning
Abstract Partially Observable Monte-Carlo Planning (POMCP) is a powerful online algorithm
able to generate approximate policies for large Partially Observable Markov Decision …
able to generate approximate policies for large Partially Observable Markov Decision …
Unsupervised active visual search with monte carlo planning under uncertain detections
We propose a solution for Active Visual Search of objects in an environment, whose 2D floor
map is the only known information. Our solution has three key features that make it more …
map is the only known information. Our solution has three key features that make it more …
Safe and efficient reinforcement learning for environmental monitoring
This paper discusses the challenges of applying reinforcement techniques to real-world
environmental monitoring problems and proposes innovative solutions to overcome them. In …
environmental monitoring problems and proposes innovative solutions to overcome them. In …
Active generation of logical rules for POMCP shielding
We consider the popular Partially Observable Monte-Carlo Plan-ning (POMCP) algorithm
and propose a methodology, called Active XPOMCP, for generating compact logical rules …
and propose a methodology, called Active XPOMCP, for generating compact logical rules …