Marco Wilhelm

Alexander Hahn

Proceedings of the 22nd International Workshop on Nonmonotonic Reasoning (NMR 2024) co-located with 21st International Conference on Principles of Knowledge Representation and Reasoning (KR 2024), 2024

Conditional Splittings of Belief Bases and Nonmonotonic Inference with c-Representations.

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[DOI]

Proceedings of the 21st International Conference on Principles of Knowledge Representation and Reasoning, 2024

Core c-Representations and c-Core Closure for Conditional Belief Bases.

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[DOI]

Proceedings of the Foundations of Information and Knowledge Systems, 2024

Intrinsic Prioritization in Answer Set Programming Based on an Adapted Notion of Tolerance.

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[DOI]

Lars-Phillip Spiegel

Proceedings of the Thirty-Seventh International Florida Artificial Intelligence Research Society Conference, 2024

Decomposing Constraint Networks for Calculating c-Representations.

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[DOI]

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Activation-based Conditional Inference.

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[DOI]

FLAP, 2023

Integrating Linear Arithmetic Constraints Into Conditional Maximum Entropy Reasoning.

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[DOI]

Proceedings of the 20th International Conference on Principles of Knowledge Representation and Reasoning, 2023

Splitting Techniques for Conditional Belief Bases in the Context of c-Representations.

[BibT_eX]

[DOI]

Meliha Sezgin

Jonas Haldimann

Jesse Heyninck

Proceedings of the Logics in Artificial Intelligence - 18th European Conference, 2023

Prioritizing Answer Sets Based on Conditional Expert Knowledge.

[BibT_eX]

[DOI]

Andre Thevapalan

Proceedings of the Thirty-Sixth International Florida Artificial Intelligence Research Society Conference, 2023

An Interactive Modelling Environment for Designing Warehouse Layouts Based on ASP.

[BibT_eX]

[DOI]

Andre Thevapalan

Pascal Kaiser

Moritz Roidl

Proceedings of the Thirty-Sixth International Florida Artificial Intelligence Research Society Conference, 2023

2022

Integrating Cognitive Principles From ACT-R Into Probabilistic Conditional Reasoning by Taking the Example of Maximum Entropy Reasoning.

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[DOI]

Proceedings of the Thirty-Fifth International Florida Artificial Intelligence Research Society Conference, 2022

2021

Conditional Inference and Activation of Knowledge Entities in ACT-R.

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[DOI]

CoRR, 2021

A Brief Introduction Into Activation-Based Conditional Inference.

[BibT_eX]

[DOI]

Proceedings of the 7th Workshop on Formal and Cognitive Reasoning co-located with the 44th German Conference on Artificial Intelligence (KI 2021), 2021

Focused Inference and System P.

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[DOI]

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Context-Based Inferences from Probabilistic Conditionals with Default Negation at Maximum Entropy.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third International Florida Artificial Intelligence Research Society Conference, 2020

2019

Counting Strategies for the Probabilistic Description Logic 𝓐ℒ𝒞<sup>ME</sup> Under the Principle of Maximum Entropy.

[BibT_eX]

[DOI]

Franz Baader

Proceedings of the Logics in Artificial Intelligence - 16th European Conference, 2019

The Complexity of the Consistency Problem in the Probabilistic Description Logic 𝓐ℒ𝒞<sup>ME</sup>.

[BibT_eX]

[DOI]

Franz Baader

Proceedings of the Frontiers of Combining Systems - 12th International Symposium, 2019

Integrating Typed Model Counting into First-Order Maximum Entropy Computations and the Connection to Markov Logic Networks.

[BibT_eX]

[DOI]

Marc Finthammer

Proceedings of the Thirty-Second International Florida Artificial Intelligence Research Society Conference, 2019

Maximum Entropy Calculations for the Probabilistic Description Logic $$\mathcal {ALC}^{\mathsf{ME}}$$.

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[DOI]

Proceedings of the Description Logic, Theory Combination, and All That, 2019

2018

A Generalized Iterative Scaling Algorithm for Maximum Entropy Model Computations Respecting Probabilistic Independencies.

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[DOI]

Marc Finthammer

Proceedings of the Foundations of Information and Knowledge Systems, 2018

2017

Probabilistic knowledge representation using the principle of maximum entropy and Gröbner basis theory.

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[DOI]

Ann. Math. Artif. Intell., 2017

First-Order Typed Model Counting for Probabilistic Conditional Reasoning at Maximum Entropy.

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[DOI]

Marc Finthammer

Proceedings of the Scalable Uncertainty Management - 11th International Conference, 2017

Basic Independence Results for Maximum Entropy Reasoning Based on Relational Conditionals.

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[DOI]

Proceedings of the GCAI 2017, 2017

Typed Model Counting and Its Application to Probabilistic Conditional Reasoning at Maximum Entropy.

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[DOI]

Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference, 2017

A Semantics for Conditionals with Default Negation.

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[DOI]

Christian Eichhorn

Richard Niland

Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2017

2016

Propositional Probabilistic Reasoning at Maximum Entropy Modulo Theories.

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[DOI]

Proceedings of the Twenty-Ninth International Florida Artificial Intelligence Research Society Conference, 2016

2014

Probabilistic Knowledge Representation Using Gröbner Basis Theory.

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[DOI]

Proceedings of the International Symposium on Artificial Intelligence and Mathematics, 2014

A Novel Methodology for Processing Probabilistic Knowledge Bases Under Maximum Entropy.

[BibT_eX]

[DOI]