ABSTRACT. We propose Adaptive Large Neighborhood Prioritized Search (ALNPS), a metaheuristic for solving combinatorial optimization problems. ALNPS iteratively seeks better solutions by alternately performing variability-guided destruction and prioritized search on a solution at hand. By exploiting neighborhood variability, ALNPS enables flexible and robust search while reducing the dependence on manually tuned or pre-defined parameters, especially the percentage of destruction. We present an implementation of ALNPS based on Answer Set Programming (ASP). Experimental results on course timetabling demonstrate that ALNPS significantly enhances the performance of standard ASP optimization and achieves competitive performance compared to existing ASP-based LNS methods, including ALNS and LNPS.

ABSTRACT. The ALASPO framework applies Large Neighbourhood Search (LNS) to Answer Set Programming (ASP), and has proven to be effective in tackling a number of different optimization problems. To control the behaviour of the LNS loop, ALASPO offers many configuration options, that, when combined with all of the possible solving and grounding parameters of the underlying ASP solver, result in a vast configuration space.

To address this challenge, we integrate the SMAC3 framework for Bayesian Optimization into ALASPO to enable the automatic tuning of ALASPO configurations. Additionally, we cluster the provided instances based on their characteristics, allowing us to obtain multiple optimal configurations, one for each cluster of similar instances. After tuning, the result can be used for future ALASPO runs on unseen instances to automatically select and apply an appropriate configuration based on the characteristics of the given instance.

We evaluate our methodology on a set of four benchmark problems and show that tuning can, for some of them, provide significant improvements over using the default configuration, while also demonstrating its limitations whenever there are insufficient opportunities for advancement.

ABSTRACT. 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.

ABSTRACT. In this short paper we introduce a theoretical framework for hybrid systems based on Answer Set Programming (ASP) modulo Description Logic (DL) theories. To this aim, we extend the formalism of Here-and-There with Constraints, mainly used before for integrating ASP with arithmetic constraints, to incorporate now theory atoms that may assert axioms or perform queries and reasoning on an external DL knowledge base. This paves the way for an alternative implementation of hybrid ASP systems interacting with large-scale existing ontologies that treated as external theories.

ABSTRACT. Answer Set Programming (ASP) is widely recognized as a powerful paradigm for knowledge representation and reasoning, yet broadening its adoption beyond the core community remains a challenge. A contributing factor is the historical lack of tooling that developers take for granted in mainstream languages; such as linters, code completion, and documentation generators. This work addresses the documentation gap with mkdoclingo, an automated tool that analyzes clingo ASP encodings and generates structured, navigable documentation from the source code and lightweight comment annotations. Built as an extension of the popular MkDocs ecosystem, it integrates seamlessly into Markdown-based documentation sites. Its main features include rendered and annotated encoding sections, predicate classification into input and output, dependency graph visualization, and a navigable glossary with per-predicate argument descriptions and file references.

ABSTRACT. Bridging the gap between raw visual perception and symbolic reasoning remains a central challenge for general-purpose AI agents. While deep learning models excel at object recognition, they often require task-specific training data and lack the structural guarantees needed by downstream logical solvers. We present an agnostic grid reconstruction framework that transforms 2D images of structured environments, specifically game boards, into logical assertions without pretrained models or manual labeling. We propose two complementary methods: Grid-First Detection (GFD), based on classical image processing and genetic autotuning, and Object-First Detection (OFD), based on zero-shot segmentation for repeated-object discovery. Both produce a lightweight symbolic abstraction of the board that can be directly consumed by Answer Set Programming (ASP) encodings. We further embed this abstraction in a closed-loop sense–abstract–reason–act–verify architecture for dynamic visual environments. After an action is selected and executed, the system monitors the environment until the symbolic state stabilizes, avoiding decisions based on transient visual effects such as animations, cascades, or delayed updates. This supports robust replanning even when exact post-action prediction is unavailable. Overall, the approach enables rapid deployment of symbolic reasoning agents in visually rich, dynamic environments where data-driven training is impractical.

ABSTRACT. Multi-Agent Pathfinding (MAPF) is a fundamental problem in robotics and AI, where multiple agents must navigate a shared environment without colliding. This paper presents an approach inspired by the Rolling Horizon Collision Resolution (RHCR) algorithm, in which agent paths consider collisions only for a limited number of steps ahead, treating the remainder of each path as a single-agent problem. We reframe this approach in the context of a reduction-based solver — specifically, Answer Set Programming (ASP). This allows us to overcome a significant disadvantage of previous suboptimal reduction-based approaches: the need to ground the entire problem at once. Instead, for each agent, we ground only the steps within the current window and guide their movement via optimization statements. By significantly reducing the planning horizon, our method improves runtime over previous suboptimal reduction-based approaches on instances with large horizons, at the cost of solution quality.

ABSTRACT. Abstract Air Traffic Flow and Capacity Management (ATFCM) is the mathematical model for balancing flight demand with available sector capacity, serving as the core mechanism within Air Traffic Management (ATM) to enable safe and efficient operations. Current state-of-the-art (SOTA) approaches frame ATFCM as an optimization problem by maximizing efficiency while ensuring safety and adhering to side constraints. However, SOTA research optimizes the aircraft trajectories (called ATFM) or the sector configuration (called DAC) separately. This leaves a research gap of whether joint optimization of ATFM and DAC can bring benefits. We partially address this limitation by introducing a unified model with an encoding in Answer Set Programming (ASP) for joint optimization. The ASP implementation is evaluated against two baselines applied to our unified model: a SOTA Mixed Integer Programming (MIP) model and an iterative CASA-based heuristic. Computational experiments utilize an instance generator fitted to historical OpenSky Network flight data. Our results indicate that the ASP model outperforms the MIP model, while ASP remains competitive against heuristics on small instances. The ablation study captures the effect of DAC inclusion into ATFM on our ASP model, where we see significant improvements.

ABSTRACT. Industrial product configuration often involves complex partonomic structures with intricate, interdependent constraints such that the required number of components is unknown in advance. This paper addresses the challenge of determining minimal bounds for unbounded cardinalities within the CoomSuite workbench. While previous methods relied on a “naive” single-shot approach, requiring grounding and solving from scratch for every iteration, we introduce an extended workflow that leverages clingo’s multi-shot capabilities. We evaluate our approach across three benchmark domains demonstrating the efficiency of our incremental method compared to traditional single-shot strategies in scenarios with unbounded cardinalities.

ABSTRACT. We present flaspland, an Answer Set Programming (ASP) test-bed for tackling complex routing and scheduling problems based on the Flatland railway simulation environment. To establish a solid theoretical foundation, we provide a rigorous formalization of the Flatland environment and its navigational dynamics. Furthermore, we explore three distinct graph-based representations to effectively encode the underlying routing and scheduling constraints in ASP.

ABSTRACT. We present a general \emph{answer set programming} based hybrid quantitative-qualitative method for computing constrained branching trajectory modes for moving objects in real-world settings. The method performs constrained traversal of an environment graph, enumerating geometrically admissible motion behaviours as stable models, each constituting a distinct trajectory mode characterised by both domain-dependent and independent factors such as derived event sequence, map topology, and domain norms. The hybrid trajectory computation method is generally applicable across motion characteristics typically encountered in diverse dynamic domains with moving objects, e.g., autonomous driving. We demonstrate applicability and highlight how computed trajectories are traceable to their underlying stable model, thereby affording verifiable interpretability that purely learned approaches cannot provide. We also perform an empirical evaluation with Argoverse~2, a large-scale real-world autonomous driving benchmark representative of the class of dynamic domains within the scope of the proposed method.

ABSTRACT. We present an executable operationalization of L-DINF, an epistemic logic for cooperative agents, in the DALI2 multi-agent system framework. Building on recent trust-aware and probabilistic extensions of L-DINF, we show how explicit beliefs, intentions, feasibility, preferences, inter-group delegation, trust thresholds, and probability-aware action selection can be represented through DALI2 beliefs, reactive rules, message passing, and execution traces. The implementation is demonstrated on a healthcare scheduling case study in which runtime disruptions trigger belief revision, local repair attempts, trust-sensitive decisions, mediated lending, and selection among equivalent actions according to reliability, preference, and cost. The resulting system produces explanation traces that connect DALI2 events with the corresponding L-DINF-style transitions, showing how an epistemic decision layer can be made executable in a practical multi-agent platform.

ABSTRACT. Reinforcement learning (RL) is widely used to train decision-making agents in domains such as autonomous driving, healthcare, and game playing. However, RL agents typically optimize numerical rewards without explicitly accounting for ethical, social, or legal norms, which is problematic in safety-critical and dynamic settings. Policy fixing addresses this issue by augmenting RL agents with symbolic reasoning for detecting or anticipating norm violations and applying emergency plans minimally altering the learned policy. Answer Set Programming (ASP) enables fixes that balance reward optimization with norm satisfaction. Existing approaches, however, rely on batch reasoning, solving each step from scratch and struggling with temporal norms requiring information over time. In this paper, we propose an ASP-based Stream Reasoning extension supporting incremental, stateful processing of observations. The approach improves efficiency and naturally supports dynamic, time-bound normative obligations. Experimental results show improved scalability in demanding scenarios and effective temporal norm management.