Abstract
The study of actual causation concerns reasoning about events that have been instrumental in bringing about a particular outcome. Although the subject has long been studied in a number of fields including artificial intelligence, existing approaches have not yet reached the point where their results can be directly applied to explain causation in certain advanced scenarios, such as pin-pointing causes and responsibilities for the behavior of a complex cyber-physical system. We believe that this is due, at least in part, to a lack of distinction between the laws that govern individual states of the world and events whose occurrence cause state to evolve. In this paper, we present a novel approach to reasoning about actual causation that leverages techniques from Reasoning about Actions and Change to identify detailed causal explanations for how an outcome of interest came to be. We also present an implementation of the approach that leverages Answer Set Programming.
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Notes
- 1.
For convenience and compatibility with the terminology from RAC, in this paper we use action and event as synonyms.
- 2.
We focus on elementary actions for simplicity of presentation. It is straightforward to expand the statements to allow non-elementary actions.
- 3.
Note that an event may occur without having an effect on the state of the world, commonly referred to in the literature as a NOP action.
- 4.
In \(\mathcal {A}\mathcal {L}\), it is possible that a set of literals must hold simultaneously in order to cause a literal to hold. Consider \(AD=\{a\,\mathbf{causes }\,b;\,c\,\mathbf{causes }\,d;\,e\,\mathbf{if }\,b, d\}\) of a causing compound event \(\epsilon _i\) of l.
- 5.
We will use the predicate \(prec_h\) when computing both direct and indirect causes.
- 6.
The strict sequence indirect cause (SSIC) case takes the second to longest time to explain 10 literals at 0.67 s.
- 7.
The SSDC case takes the second longest time to explain 50 literals at approximately 0.4 s.
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LeBlanc, E., Balduccini, M., Vennekens, J. (2019). Explaining Actual Causation via Reasoning About Actions and Change. In: Calimeri, F., Leone, N., Manna, M. (eds) Logics in Artificial Intelligence. JELIA 2019. Lecture Notes in Computer Science(), vol 11468. Springer, Cham. https://doi.org/10.1007/978-3-030-19570-0_15
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