120 citations · 145 across the 36 of their papers we have counts for
8 papers · 1 filter
Adapting to Changes in Agent Behavior via Finite-Depth Policy Sensitivity
Lan Shi, Daigo Shishika, Xuan Wang
Adapting a reinforcement learning policy to changes in another agent's behavior typically requires a large amount of new interaction data. Policy sensitivity provides a first-order…
Feedback Dominance Analysis for Pursuit-Evasion Games on Graphs
Yue Guan, Daigo Shishika, Dipankar Maity +2
This work identifies the dominance regions for discrete, simultaneous-move pursuit-evasion games on graphs. Existing geometric approaches provide efficient characterizations of win…
Deception and Counter Deception in Adversarial Graph Traversal Game
Violetta Rostobaya, James Berneburg, Daigo Shishika
We study deception in adversarial graph traversal, where a mobile agent seeks to reach a goal with minimum cost while an adversary alters edge costs to increase the total traversal…
Linear Programming Approach to Deceptive Path Planning Game with Goal Selection
Violetta Rostobaya, Yue Guan, James Berneburg +1
In adversarial settings, a mobile agent may strategically plan its motion to influence an opponent's inference about its intended goal. We study deceptive path planning in a scenar…
Asymmetric-Information Resource Allocation Games: An LP Approach to Purposeful Deception
Longxu Pan, Yue Guan, Daigo Shishika +1
In this work, we introduce the Deceptive Resource Allocation Game (DRAG), which studies purposeful deception within a Bayesian game framework. In DRAG, a Defender allocates resourc…
Optimal Hiding with Partial Information of the Seeker's Route
Prajakta Surve, Shaunak D. Bopardikar, Daigo Shishika +2
We consider a hide-and-seek game between a Hider and a Seeker over a finite set of locations. The Hider chooses one location to conceal a stationary treasure, while the Seeker visi…
Multi-robot Graph Traversal with Support Coordination under Stochastically Moving Adversaries
Manshi Limbu, Xuan Wang, Gregory J. Stein +2
Cooperative multi-robot missions require team of robots to traverse environments where adversaries or hazards with stochastic dynamics induce time-varying traversal risk. While sup…
Game-Theory-Assisted Reinforcement Learning for Border Defense: Early Termination based on Analytical Solutions
Goutam Das, Michael Dorothy, Kyle Volle +1
Game theory provides the gold standard for analyzing adversarial engagements, offering strong optimality guarantees. However, these guarantees often become brittle when assumptions…