The detailed schedule of the program is available at the following link:
⮺ Detailed schedule
Invited speakers
Chitta Baral - LPNMR in the Age of LLMs: Verification, Policy, and Trustworthy Agency
Large language models have dramatically expanded the apparent reach of AI systems, enabling fluent interaction, flexible task execution, and increasingly sophisticated agentic behavior. Yet their impressive performance also exposes a fundamental limitation: generated outputs are not, by themselves, justified conclusions. LLMs may hallucinate facts, overlook constraints, fail to respect preferences, or produce reasoning that is persuasive but invalid. This is especially problematic when LLM-based agents are used in settings where correctness, compliance, explainability, and safety matter.
In this talk, I will argue that traditional research in logic programming, non-monotonic reasoning, and knowledge representation is not merely relevant to the current wave of AI systems; it is likely to be essential for building reliable ones. Logic programming provides declarative ways to specify rules, constraints, policies, defaults, exceptions, and goals. Non-monotonic reasoning provides principled mechanisms for reasoning with incomplete information, revising conclusions in light of new evidence, handling defaults and exceptions, and representing competing preferences. These are precisely the kinds of reasoning challenges that arise in LLM-based agents.
I will discuss two complementary roles for logic programming and non-monotonic reasoning in future AI architectures. First, they can help train, evaluate, and structure models so that they acquire more disciplined forms of reasoning. Second, and perhaps more importantly for safety-critical applications, they can serve as external verification and control layers: checking LLM-generated plans, explanations, tool calls, policy decisions, and intermediate conclusions before these are shown to users or passed to subsequent stages of an agent pipeline.
The talk will highlight scenarios in which such techniques are particularly valuable, including agents that must satisfy policy constraints, respect policy preferences, reason under incomplete information, justify their conclusions, and avoid unsafe or non-compliant actions. I will suggest that the future of high-quality AI agents is not a choice between neural and symbolic methods, but a careful integration of generative capabilities with declarative specifications, non-monotonic reasoning, and formal verification. For the logic programming and non-monotonic reasoning community, this moment presents both a challenge and an opportunity: to bring decades of work on formal reasoning into the design of trustworthy AI systems.
Elena Bellodi - Probabilistic Logics for Declarative Process Mining
Business processes underpin many real-world domains, from healthcare and manufacturing to e-commerce, by defining how activities are coordinated to achieve organizational goals. Process Mining (PM) aims at discovering, analyzing and monitoring business processes from event logs. While classical approaches assume complete observations in logs and deterministic process models, real-world applications are inherently affected by uncertainty. Event logs may contain missing or ambiguous events, while process models may need to distinguish between mandatory requirements and soft recommendations rather than strict Boolean constraints.
This talk introduces the foundations of “Declarative” Process Mining based on the DECLARE language and reviews the current state of the art on uncertainty management in this field. It then shows how probabilistic logics, and in particular Probabilistic Logic Programming (PLP), provide a natural semantic framework for modeling uncertainty in both process models and log activities.
By transferring the Distribution Semantics from PLP to Declarative PM, probabilities can be associated with process models’ constraints and uncertain events, yielding a unified framework for the main tasks in PM: (probabilistic) conformance checking and (probabilistic) process discovery. Recent developments on efficient conformance checking algorithms and PLP-based discovery are also presented.
The talk concludes by discussing current research directions, including scalable inference, causal process discovery, and richer forms of uncertainty over event data.
Thomas Eiter - Answer Set Programming for Reinforcement Learning
Reinforcement Learning (RL) is a highly successful branch of machine learning for training an agent to maximize rewards in a given environment. RL comes in different incarnations and flavors, and is actively advanced. In the recent years, there has been increasing interest in using symbolic AI approaches, including Answer Set Programming (ASP), for improving the expected behaviour of RL trained agents, especially with regard to trustworthiness and guarantees.
In this talk, we review different usages of ASP for RL, where we put a focus on enforcing ethical behavior or norms based on deontic constraints that the agent should adhere to. Notably, such constraints can emerge after the training and require instant action, such as fixing the agent’s policy for staying operational, and retraining the agent, possibly on the fly. Real-time requirements and large state spaces make this a challenging application domain for ASP, which needs to be addressed with further advancing ASP technology.
Workshops
⮺ Datalog 2.0 2026
- 6th International Workshop on the Resurgence of Datalog in Academia and Industry
Datalog 2.0 is a workshop for Datalog researchers, implementors, and users. Its aim is to bring together researchers and practitioners interested in different aspects of Datalog to share research experiences, promote collaboration and identify directions for joint future research.
⮺ HYDRA 2026
- 5th International Workshop on HYbrid Models for Coupling Deductive and Inductive ReAsoning
The HYDRA workshop aims to bridge the gap between deductive and inductive reasoning, two powerful yet fundamentally distinct paradigms in Artificial Intelligence (AI). Deductive reasoning relies on explicit premises and logical inference rules to derive specific conclusions, whereas inductive reasoning generalizes from observations, often leveraging Machine Learning and Deep Learning techniques. The integration of these approaches offers the potential to develop more robust and flexible AI systems capable of reasoning effectively across diverse contexts. However, neither deductive nor inductive methods alone can be considered fully comprehensive solutions to AI challenges. Investigating how these paradigms can be effectively combined is therefore essential for developing novel solutions that exploit the strengths and address the limitations of both.
Accepted papers
Sebastian Adam, Francesco Calimeri, Thomas Eiter, Luca Laboccetta, Simona Perri and Giorgio Terracina - Emergency Planning for Norm Violations via ASP-Based Stream Reasoning
Mario Alviano, Pietro Macrì, Luis Angel Rodriguez Reiners and Pierpaolo Sestito - ASP Sous-Chef
Denise Angilica, Tayyab Ateeq, Pierangela Bruno, Emanuele Galardo and Giovambattista Ianni - From Pixels to Predicates: A Robust Perception Front-end for ASP-based Game Reasoning
Damiano Azzolini and Markus Hecher - Abduction in the Landscape of Probabilistic Logic Programming
Oscar Barreca, Thomas Eiter and Davide Soldà - On the Complexity of Model Checking for Temporal Equilibrium Logic
Joachim Baumeister, Konstantin Herud, Jan Heuer, Jochen Reutelshöfer, Nicolas Rühling, Abdallah Saffidine and Torsten Schaub - Solving configuration problems with unbounded cardinalities in COOM
Alexander Beiser, Markus Hecher, Nysret Musliu and Stefan Woltran - Joint Air Traffic Flow and Capacity Management via Answer Set Programming
Manuel Alejandro Borroto Santana, Erica Coppolillo, Francesco Calimeri, Giuseppe Manco, Simona Perri and Francesco Ricca - BLAST: Benchmarking LLMs with ASP-based Structured Testing
Giovanni Buraglio, Wolfgang Dvořák and Stefan Woltran - On Strong Equivalence Notions in Logic Programming and Abstract Argumentation
Pedro Cabalar, Thomas Eiter, David Pearce and Davide Soldà - Brief Temporal Equilibrium Logic
Pedro Cabalar, Ovidio Manteiga and Concepción Vidal - Deontic Equilibrium Logic with Paraconsistent Modality
Stefania Costantini, Valentina Pitoni and Lorenzo De Lauretis - Operationalizing L-DINF in DALI2 for Trust- and Probability-Aware Adaptive Scheduling
Francesco Doria, Gabriele Ramagnano, Alice Tarzariol, Marco Maratea and Mauro Vallati - Multi-shot Solving and Domain Heuristics for CASP-based Traffic Signal Optimization
Thomas Eiter, Tobias Geibinger and Dave Pfliegler - Bayesian Hyperparameter Optimization with ALASPO
Jorge Fandinno, Jan Heuer, Tomi Janhunen, Javier Romero and Torsten Schaub - Finding Counterexamples for External Equivalence
Giray Havur, Francesco Zuccato, Rafael Philipp Bankosegger, Richard Comploi-Taupe and Patrick Rodler - Theory-Level Heuristics and Multi-Level Optimization for clingcon
Tomi Janhunen - Equivalence under Stable-Unstable Semantics
Irfan Kareem, Manuel Borroto, Francesco Ricca and Michele Vitale - Lightweight Language Models for Encoding ASP from Natural Language
Giuseppe Mazzotta, Francesco Ricca and Mirek Truszczynski - Computational Complexity of Disjunctive Answer Set Programming with Quantifiers
Maximilian Moeller, Alexander Ek and Bart Bogaerts - Step-Wise Explanations for Sudoku Puzzles Using ASP(Q)
Julius Monsen, Jakob Suchan, Mehul Bhatt and Lars Karlsson - Reasonable Motion: A General ASP Foundation for Environment Constrained Movement Trajectory Computation
Ryan Murphy, Amadé Nemes, Javier Romero and Torsten Schaub - Flaspland: A Test-bed for Routing and Scheduling in Answer Set Programming
Amadé Nemes and Torsten Schaub - aspen-tree: A system for non-ground meta-programming in ASP
Tuan Nguyen, Katsumi Inoue and Chiaki Sakama - Enumerating Stable Models of Logic Programs using Linear Algebraic Approaches
Chiaki Sakama - LLMs Find New Classes of Logic Programs
Torsten Schaub, Klaus Strauch and Jiří Švancara - Improving Suboptimal Reduction-based Approaches for Multi-agent Pathfinding
Irumi Sugimori, Maki Miyashita, Hidetomo Nabeshima, Torsten Schaub, Takehide Soh, Naoyuki Tamura and Mutsunori Banbara - Adaptive Large Neighborhood Prioritized Search for Optimization in Answer Set Programming
Lucas Van Laer, Joost Vennekens and Bart Bogaerts - Efficient Grounding with Bounds using Bit Vectors
Pedro Cabalar, Stefania Costantini and Alina Vozna - Here-and-There with Constraints for Description Logic
Jorge Fandinno, Zachary Hansen, Yuliya Lierler and Vladimir Lifschitz - Strong Equivalence of Logic Programs Relative to a User Guide
Susana Hahn and Dominik Gerndt - clindocs: A system for automatic documentation of ASP programs
Zeynep G. Saribatur, Kaan Unalan and Stefan Woltran - Model-Theoretic Characterization of Programs under Specific Inputs
Best Paper & Best Student Paper Award
Oscar Barreca, Thomas Eiter and Davide Soldà - On the Complexity of Model Checking for Temporal Equilibrium Logic
Pedro Cabalar, Ovidio Manteiga and Concepción Vidal - Deontic Equilibrium Logic with Paraconsistent Modality
