The construction of multicast trees is complicated by the need to balance a number of important objectives, including: minimizing latencies, minimizing depth/hops, and bounding th...
Many signals of interest are corrupted by faults of an unknown type. We propose an approach that uses Gaussian processes and a general “fault bucket” to capture a priori uncha...
Michael A. Osborne, Roman Garnett, Kevin Swersky, ...
This work considers the problem of designing optimal multi-agent trajectories to patrol an environment. As performance criterion for optimal patrolling we consider the worst-case t...
Fabio Pasqualetti, Antonio Franchi, Francesco Bull...
While in general trading off exploration and exploitation in reinforcement learning is hard, under some formulations relatively simple solutions exist. Optimal decision thresholds ...
Aiming to clarify the convergence or divergence conditions for Learning Classifier System (LCS), this paper explores: (1) an extreme condition where the reinforcement process of ...