Aims and Scope
As in previous editions, NMR 2025 aims to foster connections between the different subareas of nonmonotonic reasoning and provide a forum for emerging topics. We especially invite papers on systems and applications, as well as position papers addressing benchmark issues. The workshop will be structured by topical sessions fitting to the scopes of accepted papers. Workshop activities will include invited talks and presentations of technical papers.
Submission Details
There are two types of submissions:
- Full papers. Full papers should be at most
10 pages14 pages including references, figures and appendices, if any. Papers already published or accepted for publication at other conferences are also welcome, provided that the original publication is mentioned in a footnote on the first page and the submission at NMR falls within the authors’ rights. In the same vein, papers under review for other conferences can be submitted with a similar indication on their front page. - Extended Abstracts. Extended abstracts should be at most 3 pages (excluding references and acknowledgements). The abstracts should introduce work that has recently been published or is under review, or ongoing research at an advanced stage. We highly encourage to attach to the submission a preprint/postprint or a technical report. Such extra material will be read at the discretion of the reviewers. Submitting already published material may require a permission by the copyright holder.
Submission will be through the EasyChair conference system. Please submit via Easychair to: https://easychair.org/my/conference?conf=nmr2025
Inivited Speakers
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Gabriele Kern-Isberner TU Dortmund, Germany
Nonmonotonic Logics as a Key to Cognitive LogicsClassical logics like propositional or predicate logic have been considered as the gold standard for rational human reasoning, and hence as a solid, desirable norm on which all human knowledge and decision making should be based, ideally. For instance, Boolean logic was set up as kind of an algebraic framework that should help make rational reasoning computable in an objective way, similar to the arithmetics of numbers. Computer scientists adopted this view to (literally) implement objective knowledge and rational deduction, in particular for AI applications. Psychologists have used classical logics as norms to assess the rationality of human commonsense reasoning. However, both disciplines could not ignore the severe limitations of classical logics, e.g., computational complexity and undecidedness, failures of logic-based AI systems in practice, and lots of logical paradoxes and failures observed in psychological experiments. Many of these problems are caused by the inability of classical logics to deal with uncertainty in an adequate way. Both computer science/AI and psychologiy have used probabilities as a way out of this dilemma, hoping that numbers and the Kolmogoroff axioms can do a better job (somehow). However, psychologists have been observing also lots of paradoxes here (maybe even more). So then, are humans hopelessly irrational? Is human reasoning incompatible with formal, axiomatic logics? In the end, should computer-based knowledge and information processing based on classical logics be considered as superior to human reasoning in terms of objectivity and rationality? Cognitive logics aim at overcoming the limitations of classical logics and resolving the observed paradoxes by proposing logic-based approaches that can model human reasoning consistently and coherently in benchmark examples. The basic idea is to reverse the normative way of assessing human reasoning in terms of logics resp. probabilities, and to use typical human reasoning patterns as norms for assessing the cognitive quality of logics. Cognitive logics explore the broad field of logic-based approaches between the extreme points marked by classical logics and probability theory with the goal to find more suitable logics for AI applications, on the one hand, and to gain more insights into the structures of human rationality, on the other. This talk features conditionals and preferential nonmonotonic reasoning as a powerful framework to explore characteristics of human rational reasoning. We show that interpreting common-sense rules in terms of conditionals and processing them with basic techniques of nonmonotonic logics provides a key to formalize human rationality in a much broader and more adequate way, resolving in particular lots of paradoxes in psychology. |
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Réka Markovich Université du Luxembourg
Computational Legal Theory for Discretionary ReasoningJudicial discretion is a much-discussed phenomenon in the law; what seems to be the common ground among theorists and practitioners is that it is a limitation of the so-called norm-based derivation, which is otherwise the default operation mode of a civil law judge. I have been intrigued by the question whether this limitation implies a limitation on its logical modeling as well, hence this is what we investigate in the Formal Analysis of Discretionary Reasoning (DISCREASON) project. In the talk, I will discuss a few child custody cases from the Hungarian case law (child custody decisions being a paradigmatic example of discretionary decision making), and I will show how we – with Josephine Dik and Liuwen Yu – have tried to grasp some of the main characteristics and issues of discretion with various formalism: modal logic, answer set programing, and formal argumentation. |