Using ASP with recent extensions for causal explanations
Yves Moinard (INRIA - IRISA)

TL;DR
This paper demonstrates how recent advancements in Answer Set Programming (ASP) make it practical and efficient for representing and computing causal explanations, addressing previous limitations in data handling and reusability.
Contribution
It provides an implementation showing the naturalness and efficiency of using ASP with recent extensions for causal explanations, highlighting practical improvements over earlier systems.
Findings
Recent ASP systems significantly improve translation ease.
The approach is natural and efficient for causal explanations.
Limitations of earlier ASP systems are addressed.
Abstract
We examine the practicality for a user of using Answer Set Programming (ASP) for representing logical formalisms. We choose as an example a formalism aiming at capturing causal explanations from causal information. We provide an implementation, showing the naturalness and relative efficiency of this translation job. We are interested in the ease for writing an ASP program, in accordance with the claimed ``declarative'' aspect of ASP. Limitations of the earlier systems (poor data structure and difficulty in reusing pieces of programs) made that in practice, the ``declarative aspect'' was more theoretical than practical. We show how recent improvements in working ASP systems facilitate a lot the translation, even if a few improvements could still be useful.
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Taxonomy
TopicsLogic, Reasoning, and Knowledge · Multi-Agent Systems and Negotiation · Bayesian Modeling and Causal Inference
