Adversarial sampling-based motion planning
There are many scenarios in which a mobile agent may not want its path to be predictable.
Examples include preserving privacy or confusing an adversary. However, this desire for …
Examples include preserving privacy or confusing an adversary. However, this desire for …
A dynamic game framework for rational and persistent robot deception with an application to deceptive pursuit-evasion
This article studies rational and persistent deception among intelligent robots to enhance
security and operational efficiency. We present an-player-stage game with an asymmetric …
security and operational efficiency. We present an-player-stage game with an asymmetric …
Stochastic Goal Recognition Design Problems with Suboptimal Agents
Abstract Goal Recognition Design (GRD) problems identify the minimum number of
environmental modifications aiming to force an interacting agent to reveal its goal as early …
environmental modifications aiming to force an interacting agent to reveal its goal as early …
Single real goal, magnitude-based deceptive path-planning
K Xu, Y Zeng, L Qin, Q Yin - Entropy, 2020 - mdpi.com
Deceptive path-planning is the task of finding a path so as to minimize the probability of an
observer (or a defender) identifying the observed agent's final goal before the goal has been …
observer (or a defender) identifying the observed agent's final goal before the goal has been …
Optimizing a Model-Agnostic Measure of Graph Counterdeceptiveness via Reattachment
A Dey, S Ruggerio, M Ornik - ar** optimal inference strategies …
Measuring target predictability for optimal environment design
M Ornik - 2020 59th IEEE Conference on Decision and Control …, 2020 - ieeexplore.ieee.org
Motivated by the study of deceptive strategies, this paper considers the problems of
detecting an agent's objective from its partial path and determining an optimal environment …
detecting an agent's objective from its partial path and determining an optimal environment …
Improving the scalability of the magnitude-based deceptive path-planning using subgoal graphs
K Xu, Y Hu, Y Zeng, Q Yin, M Yang - Entropy, 2020 - mdpi.com
Deceptive path-planning is the task of finding a path so as to minimize the probability of an
observer (or a defender) identifying the observed agent's final goal before the goal has been …
observer (or a defender) identifying the observed agent's final goal before the goal has been …
Stochastic goal recognition design
C Wayllace - Proceedings of the AAAI Conference on Artificial …, 2019 - aaai.org
Given an environment and a set of allowed modifications, the task of goal recognition design
(GRD) is to select a valid set of modifications that minimizes the maximal number of steps an …
(GRD) is to select a valid set of modifications that minimizes the maximal number of steps an …
[PDF][PDF] School of Engineering & Applied Science Department of Computer Science and Engineering
C Wayllace - 2021 - yeoh-lab.wustl.edu
Figure 6.1: Comparison between approaches avoiding and using policy enumeration to
solve OS-GRD problems. Vertical axes show the running time in seconds on a logarithmic …
solve OS-GRD problems. Vertical axes show the running time in seconds on a logarithmic …