28 citations · 67 across the 22 of their papers we have counts for
8 papers · 1 filter
Deceptive Exploration in Multi-armed Bandits
I. Arda Vurankaya, Mustafa O. Karabag, Wesley A. Suttle +3
We consider a multi-armed bandit setting in which each arm has a public and a private reward distribution. An observer expects an agent to follow Thompson Sampling according to the…
Deceptive Planning Exploiting Inattention Blindness
Mustafa O. Karabag, Jesse Milzman, Ufuk Topcu
We study decision-making with rational inattention in settings where agents have perception constraints. In such settings, inaccurate prior beliefs or models of others may lead to…
Designing Inferable Signaling Schemes for Bayesian Persuasion
Caleb Probine, Mustafa O. Karabag, Ufuk Topcu
In Bayesian persuasion, an informed sender, who observes a state, commits to a randomized signaling scheme that guides a self-interested receiver's actions. Classical models assume…
Monotonic Transformation Invariant Multi-task Learning
Surya Murthy, Kushagra Gupta, Mustafa O. Karabag +2
Multi-task learning (MTL) algorithms typically rely on schemes that combine different task losses or their gradients through weighted averaging. These methods aim to find Pareto st…
Cooperative Bargaining Games Without Utilities: Mediated Solutions from Direction Oracles
Kushagra Gupta, Surya Murthy, Mustafa O. Karabag +2
Cooperative bargaining games are widely used to model resource allocation and conflict resolution. Traditional solutions assume the mediator can access agents utility function valu…
Value of Information-based Deceptive Path Planning Under Adversarial Interventions
Wesley A. Suttle, Jesse Milzman, Mustafa O. Karabag +2
Existing methods for deceptive path planning (DPP) address the problem of designing paths that conceal their true goal from a passive, external observer. Such methods do not apply…
Do LLMs Strategically Reveal, Conceal, and Infer Information? A Theoretical and Empirical Analysis in The Chameleon Game
Mustafa O. Karabag, Jan Sobotka, Ufuk Topcu
Large language model-based (LLM-based) agents have become common in settings that include non-cooperative parties. In such settings, agents' decision-making needs to conceal inform…
Deceptive Sequential Decision-Making via Regularized Policy Optimization
Yerin Kim, Alexander Benvenuti, Bo Chen +5
Autonomous systems are increasingly expected to operate in the presence of adversaries, though adversaries may infer sensitive information simply by observing a system. Therefore,…