Sequential decision-making problems with multiple objectives arise naturally in practice and
pose unique challenges for research in decision-theoretic planning and learning, which has …

Safe exploration is a common problem in reinforcement learning (RL) that aims to prevent
agents from making disastrous decisions while exploring their environment. A family of …

Despite the success of neural-based combinatorial optimization methods for end-to-end
heuristic learning, out-of-distribution generalization remains a challenge. In this paper, we …

Penetration testing (pentesting) is a well established method for identifying security
weaknesses, by conducting friendly attacks. Simulated pentesting automates this process …

Many real-world decision problems have multiple objectives. For example, when choosing a
medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize …

We present a framework for mobile service robot task planning and execution, based on the
use of probabilistic verification techniques for the generation of optimal policies with …

Enabling autonomous systems to comply with an ethical theory is critical given their
accelerating deployment in domains that impact society. While many ethical theories have …

Abstract A Relational Markov Decision Process (RMDP) is a first-order representation to
express all instances of a single probabilistic planning domain with possibly unbounded …

This paper introduces a concept of safe path planning for UAV's autonomous operation in an
urban environment where GNSS-positioning may become unreliable or even unavailable. If …

Reinforcement learning in problems with symbolic state spaces is challenging due to the
need for reasoning over long horizons. This paper presents a new approach that utilizes …