Source: International Business Times
Pixabay Intelligent systems may encounter environments characterized by varying levels of uncertainty, limited visibility, and continuous shifts. As these systems expand into areas such as autonomous mobility, large‑scale industrial automation, and adaptive decision‑making, software engineer Sai Bhargav Yalamanchi notes that mathematical tools helping practitioners interpret uncertainty have become increasingly relevant. "One example of these tools is Markov models, which describe how a system changes over time by assigning probabilities to different state transitions," he explains.
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