Robotic Adversarial Coverage: (Doctoral Consortium)

Robotic Adversarial Coverage: From Theory to Practice

This work implements and simulates the problem known as adversarial coverage. In this problem, a robot is required to visit every point in a target area, which contains hazards or threats that might stop the robot. The objective of the robot is to cover the target area as quickly as possible, while maximizing the probability of completing the coverage before it is stopped. Previous studies of t...

Robotic Strategic Behavior in Adversarial Environments

The presence of robots in areas containing threats is becoming more prevalent, due to their ability to perform missions accurately, efficiently, and with little risk to humans. Having the robots handle adversarial forces in missions such as search and rescue, intelligence gathering, border protection and humanitarian assistance, raises many new, exciting research challenges. This paper describe...

Adversarial Modeling in the Robotic Coverage Problem: Proofs and Algorithm Details

Robotic adversarial coverage of known environments

Coverage is a fundamental problem in robotics, where one or more robots are required to visit each point in a target area at least once. Most previous work concentrated on finding a coverage path that minimizes the coverage time. In this paper we consider a new and more general version of the problem: adversarial coverage. Here, the robot operates in an environment that contains threats that mi...

Strategic Reasoning in Digital Zero-Sum Games (Doctoral Consortium)

Digital zero-sum games are a challenging domain for artificial intelligence techniques. In such games, human players often resort to strategies, i.e., memorized sequences of lowlevel actions that guide their behavior. In this research we model this way of playing by introducing the algorithm selection metagame, in which agents select algorithms to perform low-level game actions on their behalf....