the Framework for Decision
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A Game-Theoretic Decision Framework for Optimal Selection of Coordination Detection Methods in Multi-UAV Fleet Operations
Announce Type: replace Abstract: Detecting coordination among unmanned aerial vehicle (UAV) fleets operating in shared airspace and identifying the route-lead aircraft whose navigation decisions govern fleet behavior presents a fundamental speed--accuracy trade-off: fast methods enable real-time traffic management but sacrifice detection fidelity, while accurate methods may exceed the time budget for actionable airspace deconfliction. This paper presents a game-theoretic decision framework...
Manifold partitioning induced sequential optical reasoning and decision framework for photonic computing
arXiv:2606.01616v1 Announce Type: new Abstract: Real-world data are intrinsically embedded in highly entangled manifolds, making the extraction of separable representations a central challenge for artificial intelligent (AI) systems. While optical neural networks (ONNs) offer ultrafast and energy-efficient data processing, their capacity is constrained by limited physical depth. Here, we introduce a sequential optical reasoning and decision (SORD) framework, an architecture that performs...
A Game-Theoretic Decision Framework for Optimal Selection of Coordination Detection Methods in Multi-UAV Fleet Operations
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FDM: A Framework for Decision-making to build ML-based Malware detection systems
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2-Step Agent: A Framework for the Interaction of a Decision Maker with AI Decision Support
arXiv:2602.21889v3 Announce Type: replace Abstract: Predictions from ML models support human decision making in several fields, including high-stakes ones such as healthcare and the judiciary. Yet, we still lack a clear understanding of how decision makers learn from ML-based decision support (ML-DS). In this paper, we introduce a general computational framework, the 2-Step Agent, to capture this process.
2-Step Agent: A Framework for the Interaction of a Decision Maker with AI Decision Support
arXiv:2602.21889v2 Announce Type: replace Abstract: Predictions from ML models support human decision making in several fields, including high-stakes ones such as healthcare and the judiciary. Yet, we still lack a clear understanding of how decision makers learn from ML-based decision support (ML-DS). In this paper, we introduce a general computational framework, the 2-Step Agent, to capture this process.
Buzz, Choose, Forget: A Meta-Bandit Framework for Bee-Like Decision Making
arXiv:2510.16462v3 Announce Type: replace Abstract: This work introduces MAYA, a sequential imitation learning model based on multi-armed bandits, designed to reproduce and predict individual bees' decisions in contextualized foraging tasks. The model accounts for bees' limited memory through a temporal window $\tau$, whose optimal value is around 7 trials, with a slight dependence on weather conditions. Experimental results on real, simulated, and complementary (mice) datasets show that...
RISED: A Pre-Deployment Evaluation Framework for High-Stakes AI Decision-Support Systems, with Application to Healthcare
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Policy Gradient for Continuous-Time Robust Markov Decision Processes
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CarbonSim: A Lifecycle-Aware Framework for Evaluating Carbon Tradeoffs in Hardware Upgrade Decisions
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