References

Arg2P is a research artefact: most of what it implements is described in a paper. This page collects the references, grouped by the part of the framework they underpin.

The framework

The reference paper for Arg2P as a whole:

Roberta Calegari, Andrea Omicini, Giuseppe Pisano, Giovanni Sartor. Arg2P: an argumentation framework for explainable intelligent systems. Journal of Logic and Computation, 32(2):369–401, 2022. 10.1093/logcom/exab089

A broader treatment of the framework, its meta-models and the reasoning machinery behind it:

Giuseppe Pisano. Argumentation for legal reasoning: meta-models, technology and beyond. PhD thesis, Alma Mater Studiorum — Università di Bologna, Law, Science and Technology, 36th cycle, 2024. 10.48676/unibo/amsdottorato/11671

Predecessors

The tool Arg2P grew out of:

Giuseppe Pisano, Roberta Calegari, Andrea Omicini, Giovanni Sartor. Arg-tuProlog: A tuProlog-based argumentation framework. CILC 2021, CEUR Workshop Proceedings 2719:51–66, 2020.

Roberta Calegari, Giuseppe Contissa, Giuseppe Pisano, Galileo Sartor, Giovanni Sartor. Arg-tuProlog: A modular logic argumentation tool for PIL. JURIX 2020, Frontiers in Artificial Intelligence and Applications, 265–268, 2020.

Burden of persuasion

The model implemented by the bp_grounded, bp_grounded_partial and bp_grounded_complete semantics:

Roberta Calegari, Giovanni Sartor. Burden of persuasion in argumentation. ICLP 2020, Electronic Proceedings in Theoretical Computer Science 325:151–163, 2020. 10.4204/EPTCS.325.21

Roberta Calegari, Giovanni Sartor. A model for the burden of persuasion in argumentation. JURIX 2020, Frontiers in Artificial Intelligence and Applications 334:13–22, 2020.

Roberta Calegari, Régis Riveret, Giovanni Sartor. The burden of persuasion in structured argumentation. ICAIL 2021, 180–184, 2021.

The meta-argumentation approach behind the graphExtension(bp) extension, where the burden is expressed inside the rules themselves:

Giuseppe Pisano, Roberta Calegari, Andrea Omicini, Giovanni Sartor. Burden of persuasion in meta-argumentation. AIxIA 2021, Lecture Notes in Computer Science, 104–119, 2022. 10.1007/978-3-031-08421-8_8

Giuseppe Pisano, Roberta Calegari, Andrea Omicini, Giovanni Sartor. Burden of persuasion: a meta-argumentation approach. Journal of Applied Logics, 10(3):393–420, 2023.

Preferences

The mechanism behind graphExtension(defeasiblePref) and graphExtension(defeasibleAllPref):

Giuseppe Pisano, Roberta Calegari, Andrea Omicini, Giovanni Sartor. A mechanism for reasoning over defeasible preferences in Arg2P. CILC 2021, CEUR Workshop Proceedings 3002:16–30, 2021. ceur-ws.org/Vol-3002/paper10.pdf

Modularity

The modular argumentation model behind the module system — theory fragmentation, modules that coexist and interact, and module nesting:

Roberta Calegari, Giuseppe Contissa, Giuseppe Pisano, Galileo Sartor, Giovanni Sartor. Modular logic argumentation in Arg-tuProlog. AIxIA 2021, Lecture Notes in Computer Science, 91–103, 2022. 10.1007/978-3-031-08421-8_7

Conflicts

The treatment of conflicts that the parser and the metaConflicts flag build on:

Giuseppe Pisano, Roberta Calegari, Henry Prakken, Giovanni Sartor. Arguing about the existence of conflicts. COMMA 2022, Frontiers in Artificial Intelligence and Applications 353:284–295, 2022.

Causality

The model implemented by ness_original/3 and ness_original_intervention/2 in the causality module:

Giuseppe Pisano, Henry Prakken, Giovanni Sartor, Ruta Liepina. Modelling cause-in-fact in legal cases through defeasible argumentation. ICAIL 2025, 278–287, 2025. 10.1145/3769126.3769228

The revised model behind ness/3 and ness_intervention/2 is to appear at ICAIL 2026; this page will be updated once it is published.

Related work on causal reasoning in law from the same group:

Ruta Liepina, Giuseppe Pisano, Giovanni Sartor. Addressing causal puzzles in law through argumentation. JURIX 2024, Frontiers in Artificial Intelligence and Applications 395:381–383, 2024.

Distributed reasoning

The cooperative, multi-agent evaluation behind the distributed solver:

Giuseppe Pisano, Roberta Calegari, Andrea Omicini. Multi-agent cooperative argumentation in Arg2P. AIxIA 2022, Lecture Notes in Computer Science, 140–153, 2023. 10.1007/978-3-031-27181-6_10

Citing Arg2P

If you use Arg2P in academic work, please cite the journal paper:

@article{arg2p,
  author  = {Calegari, Roberta and Omicini, Andrea and Pisano, Giuseppe and Sartor, Giovanni},
  title   = {{Arg2P}: an argumentation framework for explainable intelligent systems},
  journal = {Journal of Logic and Computation},
  volume  = {32},
  number  = {2},
  pages   = {369--401},
  year    = {2022},
  doi     = {10.1093/logcom/exab089}
}