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BACKGROUND Research has demonstrated the efficacy of closed-loop control of anesthesia using bispectral index (BIS) as the controlled variable. Model-based and proportional-integral-derivative (PID) controllers outperform manual control. We investigated the application of reinforcement learning (RL), an intelligent systems control method, to closed-loop(More)
The intensive care unit is a challenging environment to both patient and caregiver. Continued shortages in staffing, principally in nursing, increase risk to patient and healthcare workers. To evaluate the use of intelligent systems in the improvement of patient care, an agent was developed to regulate ICU patient sedation. A temporal differencing form of(More)
The obstacle avoidance problem has been a well researched topic by many robotics researchers. We focus on one popular solution to this problem, the Curvature-Velocity Method(CVM). In Sim-mons' original paper [5], differentially steered (DS) robots where not considered thoroughly. This paper presents a variation on CVM, specifically designed for DS robots.(More)
— We present a method for multi-agent coordination in mapping using dynamic allocation in a centralized storage system. Typical multi-agent mapping systems use redundant storage of data to allow every agent in the system to have equivalent knowledge of the world. There are several problems with this implementation including the inefficient use of bandwidth(More)
Partially Observable Markov Decision Processes (POMDPs) have been applied extensively to planning in environments where knowledge of an underlying process is confounded by unknown factors[3, 4, 7]. By applying the POMDP architecture to basic recognition tasks, we introduce a novel pattern recognizer that operates under partially observable conditions. This(More)
Partially Observable Markov Decision Processes (POMDPs) have been applied extensively to planning in environments where knowledge of an underlying process is confounded by unknown factors[3, 4, 7]. By applying the POMDP architecture to a basic recognition task, we introduce a novel pattern recognizer that operates under partially observable conditions. This(More)
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