1 citations · 2 across the 12 of their papers we have counts for
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Safe Control of Multi-Agent Systems with Minimal Communication
Mo Yang, Jing Yu, Necmiye Ozay
In many multi-agent systems, communication is limited by bandwidth, latency, and energy constraints. Designing controllers that achieve coordination and safety with minimal communi…
Safe and Optimal Learning from Preferences via Weighted Temporal Logic with Applications in Robotics and Formula 1
Ruya Karagulle, Cristian-Ioan Vasile, Necmiye Ozay
Autonomous systems increasingly rely on human feedback to align their behavior, expressed as pairwise comparisons, rankings, or demonstrations. While existing methods can adapt beh…
Initial Distribution Sensitivity of Constrained Markov Decision Processes
Alperen Tercan, Necmiye Ozay
Constrained Markov Decision Processes (CMDPs) are notably more complex to solve than standard MDPs due to the absence of universally optimal policies across all initial state distr…
Efficient Reward Identification In Max Entropy Reinforcement Learning with Sparsity and Rank Priors
Mohamad Louai Shehab, Alperen Tercan, Necmiye Ozay
In this paper, we consider the problem of recovering time-varying reward functions from either optimal policies or demonstrations coming from a max entropy reinforcement learning p…
Mode-Prefix-Based Control of Switched Linear Systems with Applications to Fault Tolerance
Ram Padmanabhan, Antoine Aspeel, Necmiye Ozay +1
In this paper, we consider the problem of designing prefix-based optimal controllers for switched linear systems over finite horizons. This problem arises in fault-tolerant control…
LLM-Enhanced Symbolic Control for Safety-Critical Applications
Amir Bayat, Alessandro Abate, Necmiye Ozay +1
Motivated by Smart Manufacturing and Industry 4.0, we introduce a framework for synthesizing Abstraction-Based Controller Design (ABCD) for reach-avoid problems from Natural Langua…
Learning Reward Machines from Partially Observed Policies
Mohamad Louai Shehab, Antoine Aspeel, Necmiye Ozay
Inverse reinforcement learning is the problem of inferring a reward function from an optimal policy or demonstrations by an expert. In this work, it is assumed that the reward is e…