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Cognitive Reasoning Agents and the Extended Information Filter
Teaching AI Agents to Guess Smarter, Not Harder
Probabilistic reasoning plays a pivotal role in advancing intelligent systems by enabling cognitive agents to operate effectively in dynamic and uncertain contexts. As a GPS signal jumps across the map just so slightly, the randomness of noise creeps into sensor measurements creating the need for probabilistic over deterministic methods to allow for more robust decision-making and specifically adaptability. When building reasoning agents that guide real-world applications like autonomous cars, unmanned aerial vehicles, or robots, being able to correctly and reliably update states given noisy sensor data is really important.
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