most citedDC-Ada: Reward-Only Decentralized Sensor Adaptation for Heterogeneous Multi-Robot Teams

1 citations · 1 across the 1 of their papers we have counts for

collaborators

6 papers

cs.RO20261 cited

DC-Ada: Reward-Only Decentralized Sensor Adaptation for Heterogeneous Multi-Robot Teams

Saad Alqithami

Heterogeneity is a defining feature of deployed multi-robot teams: platforms often differ in sensing modalities, ranges, fields of view, and failure patterns. Controllers trained u…

cs.AI2026

Preregistered Belief Revision Contracts

Saad Alqithami

Deliberative multi-agent systems allow agents to exchange messages and revise beliefs over time. While this interaction is meant to improve performance, it can also create dangerou…

cs.GT2026

Latency-Aware Resource Allocation over Heterogeneous Networks: A Lorentz-Invariant Market Mechanism

Saad Alqithami

We present a telecom-native auction mechanism for allocating bandwidth and time slots across heterogeneous-delay networks, ranging from low-Earth-orbit (LEO) satellite constellatio…

cs.AI2026

Autonomous Agents on Blockchains: Standards, Execution Models, and Trust Boundaries

Saad Alqithami

Advances in large language models have enabled agentic AI systems that can reason, plan, and interact with external tools to execute multi-step workflows, while public blockchains…

cs.LG2025

Hierarchical Adversarially-Resilient Multi-Agent Reinforcement Learning for Cyber-Physical Systems Security

Saad Alqithami

Cyber-Physical Systems play a critical role in the infrastructure of various sectors, including manufacturing, energy distribution, and autonomous transportation systems. However,…

cs.AI2025

CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logistics

Saad Alqithami

Addressing global challenges such as greenhouse gas emissions and resource inequity demands advanced AI-driven coordination among autonomous agents. We propose CH-MARL (Constrained…