Summary of Towards Learning Abductive Reasoning Using Vsa Distributed Representations, by Giacomo Camposampiero et al.
Towards Learning Abductive Reasoning using VSA Distributed Representations
by Giacomo Camposampiero, Michael Hersche, Aleksandar Terzić, Roger Wattenhofer, Abu Sebastian, Abbas Rahimi
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Symbolic Computation (cs.SC)
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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 The paper introduces the Abductive Rule Learner with Context-awareness (ARLC) model, which excels in abstract reasoning tasks based on Learn-VRF. ARLC features a novel training objective for abductive reasoning, leading to better interpretability and higher accuracy when solving Raven’s progressive matrices (RPM). Unlike other models, ARLC combines programming domain knowledge with learning rules underlying data distributions. The authors evaluate ARLC on the I-RAVEN dataset, achieving state-of-the-art accuracy in both in-distribution and out-of-distribution tests. ARLC surpasses neuro-symbolic and connectionist baselines, including large language models, despite having fewer parameters. Additionally, ARLC demonstrates robustness to post-programming training by incrementally learning from examples on top of programmed knowledge, which improves its performance without catastrophic forgetting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new AI model called ARLC that’s really good at solving complex puzzles and problems. The model is special because it can learn rules and patterns in data and then apply them to solve new problems. It even does better than other models, like big language models, despite being much simpler. The authors tested the model on a dataset of puzzles and found that it worked amazingly well, both when given examples from the same type of puzzle and when given entirely new ones. |
