Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Normative Practical Reasoning via Argumentation and Dialogue
Authors: Zohreh Shams, Marina De Vos, Nir Oren, Julian Padget
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we propose an argumentation-based approach to normative practical reasoning using a dialogue game to provide an intuitive overview of agent s reasoning. In achieving this aim, the following contributions are made: (i) we formalise a set of argument schemes and critical questions [Walton, 1996] aimed at checking plan justifiability with respect to goal satisfaction and norm compliance/violation; (ii) we offer a novel decision criterion that identifies the best plan(s) both in the presence and absence of preferences over goals and norms; and (iii) we investigate the properties of the best plan(s). These properties, together with Caminada s Socratic dialogu game [Caminada et al., 2014a], are used to generate an explanation for the justifiability of the best plan(s). ... In future work we will investigate temporal solutions to addressing goal-goal and goal-norm conflict, similar to how conflicts between norms are handled. We also intend to empirically evaluate the effectiveness of our explanations, determining how likely a human is to accept the recommendation of a system regarding the best plan(s). |
| Researcher Affiliation | Academia | a Department of Computer Science, University of Bath, UK EMAIL b Department of Computing Science, University of Aberdeen, UK EMAIL |
| Pseudocode | No | The paper provides definitions and describes a theoretical framework with properties and examples, but it does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology. There are no links or statements about code availability. |
| Open Datasets | No | The paper is theoretical and uses worked examples (e.g., Example 6) rather than empirical datasets for training or evaluation. Therefore, there is no mention of publicly available datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments involving data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments, therefore no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, thus no experimental setup details like hyperparameters or system-level training settings are provided. |