Summary of Can We Forget How We Learned? Doxastic Redundancy in Iterated Belief Revision, by Paolo Liberatore
Can we forget how we learned? Doxastic redundancy in iterated belief revision
by Paolo Liberatore
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the concept of redundant information in iterated belief revision, a fundamental problem in artificial intelligence and machine learning. The authors investigate when a specific revision becomes irrelevant in the presence of others, providing a necessary and sufficient condition for this redundancy to occur. They also analyze the complexity of the problem, showing that it is coNP-complete even with only two propositional revisions. Furthermore, they demonstrate that lexicographic revisions are not only relevant individually but also crucial as part of sequences, which represent the most compact state of an iterated revision process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how information can become unnecessary in a process called iterated belief revision. Imagine you’re building on previous ideas and some steps become repeated or unnecessary. The authors find a rule to identify when this happens and show that it’s a challenging problem to solve, even with just two simple steps. They also explain why these kinds of revisions are important for representing complex states in artificial intelligence. |
Keywords
» Artificial intelligence » Machine learning
