Summary of Counterfactual and Semifactual Explanations in Abstract Argumentation: Formal Foundations, Complexity and Computation, by Gianvincenzo Alfano et al.
Counterfactual and Semifactual Explanations in Abstract Argumentation: Formal Foundations, Complexity and Computation
by Gianvincenzo Alfano, Sergio Greco, Francesco Parisi, Irina Trubitsyna
First submitted to arxiv on: 7 May 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 bridges the gap between Explainable Artificial Intelligence (XAI) and Formal Argumentation by exploring counterfactual and semifactual reasoning in abstract Argumentation Frameworks. Specifically, it investigates the computational complexity of counterfactual- and semifactual-based reasoning problems, showing that they are generally harder than classical argumentation problems like credulous and skeptical acceptance. The paper also demonstrates how counterfactual and semifactual queries can be encoded in weak-constrained Argumentation Frameworks, providing a computational strategy through ASP solvers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how artificial intelligence (AI) can explain its decisions better. AI often makes decisions based on arguments or reasoning. However, it’s hard to see why these AI systems make certain choices. The researchers in this paper work on making AI more transparent by allowing it to generate alternative scenarios that show what would have happened if different things had occurred. They study how difficult it is to do this and find ways to make it easier using special tools called ASP solvers. |
