RANDOMIZED DECISION STRATEGY FOR THE HIERARCHICAL OPTIMIZATION PROBLEMS

A New Recursive Formulation for the Mixed Redundancy Strategy in Reliability Optimization Problems

One of the common approaches for improving the reliability of a specific system is to use parallel redundant components in subsystems. This approach, which is known as the redundancy allocation problem (RAP), includes the simultaneous selection of the component type and its level for each subsystem in order to maximize the system reliability.Traditionally, there are two redundancy strategies, n...

Hierarchical Strategy Synthesis for Pursuit-Evasion Problems

We present a novel approach for solving pursuit-evasion problems where multiple pursuers with limited sensing capabilities are used to detect all possible mobile evaders in a given environment. We make no assumptions about the number, the speed, or the maneuverability of evaders. Our algorithm takes as input a map of the environment and sensor models for the pursuers. We then obtain a graph rep...

Iterative Hierarchical Optimization for Misspecified Problems (IHOMP)

Reinforcement Learning (RL) aims to learn an optimal policy for a Markov Decision Process (MDP). For complex, high-dimensional MDPs, it may only be feasible to represent the policy with function approximation. If the policy representation used cannot represent good policies, the problem is misspecified and the learned policy may be far from optimal. We introduce IHOMP as an approach for solving...

Randomized Algorithms for Geometric Optimization Problems

This chapter reviews randomization algorithms developed in the last few years to solve a wide range of geometric optimization problems. We review a number of general techniques, including randomized binary search, randomized linear-programming algorithms, and random sampling. Next, we describe several applications of these techniques, including facility location, proximity problems, nearest nei...

Randomized Algorithms for Geometri Optimization Problems