Publications

Info

This page lists all scientific outputs from our project. All publications will be available for download as soon as they are released. Our first results are currently in preparation and will appear here soon.

Recent publications

Optimality-preserving Logic-Based Benders Decomposition of Answer Set Programs
  • Authors: Carmine Dodaro, Antonio Ielo, Marco Maratea, Cinzia Marte, Alice Tarzariol
  • Conference: Proceedings of the Thirty-fiveth International Joint Conference on Artificial Intelligence, (IJCAI 2026)
  • Date: Sep 22, 2026
  • AbstractBender Decomposition is a well-known solving technique in Operation Research that decomposes a problem into a master and a subproblem, which interact via "cuts". This technique has been extended to Logic-Based Bender Decomposition (LBBD), solving problems specified by logic-based languages and enabling a wider applicability. However, while Bender Decomposition guarantees optimality, LBBD does not: this property is problem-specific and depends on the defined decomposition and cuts. In this paper, we present a theoretical analysis of the conditions under which LBBD, including cuts, can preserve optimality in the context of Answer Set Programming (ASP), a prominent logic-based language in the field of Artificial Intelligence. We also introduce a general-purpose algorithm that employs both minimal unsatisfiable subsets and minimal correction subsets to define cuts in a fully automated, problem-independent way. The algorithm preserves the optimality guarantees of Bender Decomposition. An empirical evaluation on real-world scheduling instances shows that our approach can find significantly more solutions, and more optimal ones, compared to a standard direct ASP encoding, while also consistently reducing the execution time.
  • Access: [PDF]
ALM–ASP: A Functional Agentic Architecture for Answer Set Programming
  • Authors: Luis Angel Rodriguez Reiners, Alice Tarzariol, Mario Alviano, Manuel Borroto Santana, Konstantin Schekotihin
  • Conference: Proceedings of the 23rd International Conference on Principles of Knowledge Representation and Reasoning, (KR 2026)
  • Date: Jul 20, 2026
  • AbstractAnswer Set Programming (ASP) is a declarative formalism widely used in knowledge representation and reasoning for modeling and solving combinatorial problems, yet current Large Language Models (LLMs) often struggle to generate correct programs from natural language specifications. This difficulty stems both from the limited presence of ASP in training corpora and from the strict syntactic and semantic constraints imposed by stable model semantics. We introduce ALM–ASP (Agentic Loop for Modeling in ASP), a multi-agent architecture for automatic ASP modeling grounded in a functional model of language agents equipped with tools and persistent state. ALM–ASP instantiates this model via two interacting agents: a Modeler, which incrementally constructs candidate ASP programs, and a Validator, which assesses their alignment with the original specification and provides feedback for refinement. The agents interact through a shared ASP execution environment backed by the CLINGO engine, yielding an iterative construct–validate loop. An empirical evaluation on a challenging subset of CP–Bench and on problems from recent LP/CP Programming Contests shows that ALM–ASP significantly improves both syntactic validity and end-to-end correctness over general-purpose LLM baselines, and also achieves improved instance coverage compared to the closest agentic alternative, CP–Agent.
  • Access: [PDF]
Streamliners for Answer Set Programming
  • Authors: Florentina Voboril, Martin Gebser, Stefan Szeider, Alice Tarzariol
  • Conference: In Proceedings 42nd International Conference on Logic Programming, (ICLP 2026)
  • Date: Jul 23, 2026
  • AbstractStreamliner constraints reduce the search space of combinatorial problems by ruling out portions of the solution space. We adapt the StreamLLM approach, which uses Large Language Models (LLMs) to generate streamliners for Constraint Programming, to Answer Set Programming (ASP). Given an ASP encoding and a few small training instances, we prompt multiple LLMs to propose candidate constraints. Candidates that cause syntax errors, render satisfiable instances unsatisfiable, or degrade performance on all training instances are discarded. The surviving streamliners are evaluated together with the original encoding, and we report results for a virtual best encoding (VBE) that, for each instance, selects the fastest among the original encoding and its streamlined variants. On three ASP competition benchmarks (Partner Units Problem, Sokoban, Towers of Hanoi), the VBE achieves speedups of up to 4 to 5 times over the original encoding.
  • Access: [PDF]

Previous publications

Lifting symmetry breaking constraints with inductive logic programming
  • Authors: Alice Tarzariol, Martin Gebser, Konstantin Schekotihin
  • Journal: Machine Learning
  • Date: Apr 19, 2022
  • AbstractEfficient omission of symmetric solution candidates is essential for combinatorial problem-solving. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constraints (SBCs) for each given problem instance. However, the application of such approaches to large-scale instances or advanced problem encodings might be problematic since the computed SBCs are propositional and, therefore, can neither be meaningfully interpreted nor transferred to other instances. As a result, a time-consuming recomputation of SBCs must be done before every invocation of a solver. To overcome these limitations, we introduce a new model-oriented approach for Answer Set Programming that lifts the SBCs of small problem instances into a set of interpretable first-order constraints using the Inductive Logic Programming paradigm. Experiments demonstrate the ability of our framework to learn general constraints from instance-specific SBCs for a collection of combinatorial problems. The obtained results indicate that our approach significantly outperforms a state-of-the-art instance-specific method as well as the direct application of a solver.
  • Access: [PDF]
Learning to break symmetries for efficient optimization in answer set programming
  • Authors: Alice Tarzariol, Martin Gebser, Konstantin Schekotihin, Mark Law
  • Conference: Proceedings of the AAAI Conference on Artificial Intelligence
  • Date: Jun 26, 2023
  • AbstractThe ability to efficiently solve hard combinatorial optimization problems is a key prerequisite to various applications of declarative programming paradigms. Symmetries in solution candidates pose a significant challenge to modern optimization algorithms since the enumeration of such candidates might substantially reduce their performance. This paper proposes a novel approach using Inductive Logic Programming (ILP) to lift symmetry-breaking constraints for optimization problems modeled in Answer Set Programming (ASP). Given an ASP encoding with optimization statements and a set of small representative instances, our method augments ground ASP programs with auxiliary normal rules enabling the identification of symmetries using existing tools, like SBASS. Then, the obtained symmetries are lifted to first-order constraints with ILP. We prove the correctness of our method and evaluate it on real-world optimization problems from the domain of automated configuration. Our experiments show significant improvements of optimization performance due to the learned first-order constraints.
  • Access: [PDF]
Efficient lifting of symmetry breaking constraints for complex combinatorial problems
  • Authors: Alice Tarzariol, Martin Gebser, Mark Law, Konstantin Schekotihin
  • Journal: Theory and Practice of Logic Programming
  • Date: May 14, 2022
  • AbstractMany industrial applications require finding solutions to challenging combinatorial problems. Efficient elimination of symmetric solution candidates is one of the key enablers for high-performance solving. However, existing model-based approaches for symmetry breaking are limited to problems for which a set of representative and easily solvable instances is available, which is often not the case in practical applications. This work extends the learning framework and implementation of a model-based approach for Answer Set Programming to overcome these limitations and address challenging problems, such as the Partner Units Problem. In particular, we incorporate a new conflict analysis algorithm in the Inductive Logic Programming system ILASP, redefine the learning task, and suggest a new example generation method to scale up the approach. The experiments conducted for different kinds of Partner Units Problem instances demonstrate the applicability of our approach and the computational benefits due to the first-order constraints learned.
  • Access: [PDF]