Summary of Reasoning About Actual Causes in Nondeterministic Domains — Extended Version, by Shakil M. Khan et al.
Reasoning about Actual Causes in Nondeterministic Domains – Extended Version
by Shakil M. Khan, Yves Lespérance, Maryam Rostamigiv
First submitted to arxiv on: 21 Dec 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 Medium Difficulty Summary: This paper delves into causation in non-deterministic domains, where an agent lacks control and knowledge over environmental choices. Building on recent work on actual causation in situation calculus, the authors formalize more advanced reasoning methods for identifying actual causes of agent actions in these domains. The paper introduces notions like “Certainly Causes” and “Possibly Causes,” enabling the representation of actual cause for agent actions. Furthermore, it demonstrates how regression in situation calculus can be extended to reason about actual causes. This work contributes to the formalization of rationality by providing a framework for reasoning about causality in realistic, non-deterministic settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: Imagine trying to figure out why something happened. Most research on this topic has focused on simple situations where everything is predictable. But what if things aren’t so clear-cut? This paper explores how we can understand causes and effects when the world is more complicated. It introduces new ways of thinking about causality that take into account uncertainties and unknowns. The authors show how these ideas can be applied to make decisions and understand events in a more realistic way. |
Keywords
» Artificial intelligence » Regression
