The 20th International Conference on Principles of Knowledge Representation and Reasoning (KR 2023) was held this September in Rhodes, Greece. The KR conference series is the leading forum for the comprehensive and up-to-date exploration of progress in the theory and principles underlying the representation and computational management of knowledge.

The KR 2023 was very successful for Agata Ciabattoni, head of the CAIML Special Interest Group Logics, and Thomas Eiter, CAIML board member, who were awarded with the Ray Reiter Best Paper Prize and Marco Cadoli Distinguished Student Paper Award respectively.

Ray Reiter Best Paper Prize

The Ray Reiter Best Paper Prize was introduced in 2004 in honor of the contributions made by Canadian computer scientist and logician Ray Reiter. The prize is sponsored by the Artificial Intelligence Journal. This year, the prize was presented to Agata Ciabattoni and Dmitry Rozplokhas for their paper “Streamlining Input/Output Logics with Sequent Calculi”.

Abstract: Input/Output (I/O) logic is a general framework for reasoning about conditional norms and/or causal relations. We streamline Bochman’s causal I/O logics via proof-search-oriented sequent calculi. Our calculi establish a natural syntactic link between the derivability in these logics and in the original I/O logics. As a consequence of our results, we obtain new, simple semantics for all these logics, complexity bounds, embeddings into normal modal logics, and efficient deduction methods. Our work encompasses many scattered results and provides uniform solutions to various unresolved problems.

Marco Cadoli Distinguished Student Paper Award

This year’s Marco Cadoli Distinguished Student Paper Award was presented to Rafael Kiesel and Thomas Eiter for their paper “Knowledge Compilation and more with SharpSAT-TD”.

Abstract: SharpSAT-TD is a recently published exact model counter that performed exceptionally well in the recent editions of the Model Counting Competition. Notably, it additionally features weighted model counting capabilities over any semiring. In this work, we show how to exploit this fact to use SharpSAT-TD as a knowledge compiler to the class of sd-DNNF circuits. Our experimental evaluation shows that the efficiency of SharpSAT-TD for (weighted) model counting transfers to knowledge compilation, since it outperforms other state of the art knowledge compilers on standard benchmark sets. Additionally, we generalized SharpSAT-TD’s preprocessing to support arbitrary semirings and consider the utility of auxiliary variables in this setting.