Defeasible Normative Reasoning: A Proof-Theoretic Integration of Logical Argumentation
Authors: Ofer Arieli, Kees van Berkel, Christian Straßer
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a novel computational approach to resolving conflicts among norms by nonmonotonic normative reasoning (in constrained I/O logics). Our approach extends standard sequent-based proof systems and makes them more adequate to nonmonotonic reasoning by adding to the sequents annotations that keep track of what is known about the defeasible status of the derived sequents. This makes transparent the reasons according to which norms should be applicable or inapplicable, and accordingly the sequents that make use of such norms are accepted or retracted. We also show that this proof theoretic method has tight links to the semantics of formal argumentation frameworks. The outcome of this paper is thus a threefold characterization result that relates, in the context of nonmonotonic normative reasoning, three traditional ingredients of AI-based reasoning methods: maximally consistent sets of premises (in constrained I/O logics), derived sequents (which are accepted in corresponding annotated sequent calculi), and logical arguments (that belong to the grounded extensions of the induced logical argumentation frameworks). |
| Researcher Affiliation | Academia | Ofer Arieli1, Kees van Berkel2, Christian Straßer2 1School of Computer Science, Tel-Aviv Academic College, Israel 2Institute for Philosophy II, Ruhr University Bochum, Germany |
| Pseudocode | No | The paper presents formal rules for logical systems and defines processes like acceptance and rejection (Acpt-1, Acpt-2, Rjct), but it does not include a figure, block, or section explicitly labeled "Pseudocode" or "Algorithm". |
| Open Source Code | No | The paper does not contain any statement or link indicating that open-source code for the described methodology is available. |
| Open Datasets | No | This paper is theoretical and focuses on formal logic and proof systems. It uses abstract normative knowledge bases and illustrative examples rather than empirical datasets. There is no mention of a publicly available or open dataset being used for training. |
| Dataset Splits | No | This paper is theoretical and focuses on formal logic and proof systems. It does not conduct empirical experiments with datasets that would require training/validation/test splits. |
| Hardware Specification | No | The paper is theoretical and focuses on formal logic and proof systems. It does not describe any experimental hardware used for computation. |
| Software Dependencies | No | The paper describes formal systems and refers to established logical frameworks but does not list any specific software components with version numbers that would be dependencies for reproducing practical implementation or experiments. |
| Experiment Setup | No | This paper is theoretical and focuses on formal logic and proof systems. It describes logical definitions and rules rather than an experimental setup with hyperparameters or system-level training settings. |