What practical roadblock prevented generative POMDP-based trackers from scaling effectively?

Answer

The number of possible states ($S$) grew exponentially, causing intractability.

Despite their mathematical superiority in handling uncertainty compared to rule-based systems, generative POMDP approaches encountered significant issues related to scalability and computational feasibility. The core problem stemmed from the nature of the full Bayesian network required to accurately model dependencies between all dialogue features, such as slot confirmations or goal alterations. As the complexity increased, the total number of possible states ($S$) expanded exponentially. This rapid growth made computing the exact state distribution computationally intractable for real-time execution, forcing researchers to employ approximations like maintaining only a narrow beam of likely states.

What practical roadblock prevented generative POMDP-based trackers from scaling effectively?

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