A linguistic guessing game has been turned into a pure graph problem that can be solved with perfect mathematical precision.
This new framework solves complex word deduction games like Jotto by iteratively pruning a lexical overlap network. It treats the search for a word as a traversal through a mathematical graph rather than a series of guesses. The system converges efficiently even when words have variable lengths or many repeated letters. This approach proves that high-level linguistic reasoning can be fully automated using network theory. It provides a blueprint for building AI that can solve any partial feedback problem with absolute efficiency.
A Graph-Based Inference Framework for Word Deduction under Partial Feedback
research_square · rs-9222809
Abstract Reasoning under incomplete feedback remains a recurring challenge in intelligentinference systems. We present a computational perspective to examine Jotto, theword deduction problem. The study proposes a graph-based framework for identifying secret words through iterative feedback. In the proposed model, candidatewords are represented as nodes in a lexical overlap network where the sharedletter relationships is captured by the weighted edges. The response received asthe feedback is used