We address the challenge of product configuration in the context of increasing customer demand for diverse and complex products. We propose a solution through a curated selection of product model benchmarks formulated in the Coom language, divided into three fragments of increasing complexity. Each fragment is accompanied by a corresponding example on bike configuration, and additional scalable product models are included in the CoomSuite, along with relevant resources. We outline an ASP-based workflow for solving Coom-based configuration problems, highlighting its adaptability to different paradigms and alternative ASP solutions. The CoomSuite aims to provide a comprehensive, accessible, and representative set of examples that can serve as a common ground for stakeholders in the field of product configuration.

We present a new use of Answer Set Programming (ASP) to discover the molecular structure of chemical samples based on the relative abundance of elements and structural fragments, as measured in mass spectrometry. To constrain the exponential search space for this combinatorial problem, we develop canonical representations of molecular structures and an ASP implementation that uses these definitions. We evaluate the correctness of our implementation over a large set of known molecular structures, and we compare its quality and performance to other ASP symmetry-breaking methods and to a commercial tool from analytical chemistry.

Approximation fixpoint theory (AFT) is an algebraic framework designed to study the semantics of non-monotonic logics. Despite its success, AFT is not readily applicable to higher-order definitions. To solve such an issue, we devise a formal mathematical framework employing concepts drawn from category theory. In particular, we make use of the notion of Cartesian closed category to inductively construct higher-order approximation spaces while preserving the structures necessary for the correct application of AFT. We show that this novel theoretical approach extends standard AFT to a higher-order environment and generalizes the AFT setting of Charalambidis et al. (2018).

This paper presents a logic programming-based framework for policy-aware autonomous agents that can reason about potential penalties for noncompliance and act accordingly. While prior work has primarily focused on ensuring compliance, our approach considers scenarios where deviating from policies may be necessary to achieve high-stakes goals. Additionally, modeling noncompliant behavior can assist policymakers by simulating realistic human decision-making. Our framework extends Gelfond and Lobo’s Authorization and Obligation Policy Language ($\mathscr{AOPL}$) to incorporate penalties and integrates Answer Set Programming (ASP) for reasoning. Compared to previous approaches, our method ensures well-formed policies, accounts for policy priorities, and enhances explainability by explicitly identifying rule violations and their consequences. Building on the work of Harders and Inclezan, we introduce penalty-based reasoning to distinguish between noncompliant plans, prioritizing those with minimal repercussions. To support this, we develop an automated translation from the extended $\mathscr{AOPL}$ into ASP and refine ASP-based planning algorithms to account for incurred penalties. Experiments in two domains demonstrate that our framework generates higher-quality plans that avoid harmful actions while, in some cases, also improving computational efficiency. These findings underscore its potential for enhancing autonomous decision-making and informing policy refinement.

The relationship between intuitionistic or intermediate logics and logic programming has been extensively studied, prominently featuring Pearce’s equilibrium logic and Osorio’s safe beliefs. Equilibrium logic admits a fixpoint characterization based on the logic of here-and-there, akin to theory completion in default and autoepistemic logics. Safe beliefs are similarly defined via a fixpoint operator, albeit under the semantics of intuitionistic or other intermediate logics. In this paper, we investigate the logical foundations of Temporal Answer Set Programming through the lens of Temporal Equilibrium Logic, a formalism combining equilibrium logic with linear-time temporal operators. We lift the seminal approaches of Pearce and Osorio to the temporal setting, establishing a formal correspondence between temporal intuitionistic logic and temporal logic programming. Our results deepen the theoretical underpinnings of Temporal Answer Set Programming and provide new avenues for research in temporal reasoning.

Large language models (LLMs) excel at understanding natural language but struggle with explicit commonsense reasoning. A recent trend of research suggests that the combination of LLM with robust symbolic reasoning systems can overcome this problem on story-based question answering (Q&A) tasks. In this setting, existing approaches typically depend on human expertise to manually craft the symbolic component. We argue, however, that this component can also be automatically learned from examples. In this work, we introduce LLM2LAS, a hybrid system that effectively combines the natural language understanding capabilities of LLMs, the rule induction power of the learning from answer sets (LAS) system ILASP, and the formal reasoning strengths of answer set programming (ASP). LLMs are used to extract semantic structures from text, which ILASP then transforms into interpretable logic rules. These rules allow an ASP solver to perform precise and consistent reasoning, enabling correct answers to previously unseen questions. Empirical results outline the strengths and weaknesses of our automatic approach for learning and reasoning in a story-based Q&A benchmark.

$\mathcal{E}$rgoAI is a high-level, multi-paradigm logic programming language and system developed by Coherent Knowledge Systems as an enhancement of and a successor to the popular $\mathcal{F}$lora-2 system. $\mathcal{E}$rgoAI is oriented toward scalable knowledge representation and reasoning and can exploit = structured knowledge as well as knowledge derived from external sources such as vector databases. From their inception, $\mathcal{E}$rgoAI (and $\mathcal{F}$lora-2 before it) were designed to exploit the well-founded semantics for reasoning in a multi-paradigm environment, including object-based logic (F-logic) with non-monotonic inheritance; higher-order syntax in the style of HiLog; defeasibility of rules; semantically clean transactional updates; subgoal delay for handling unsafe queries and for better performance; and optional support for bounded rationality at a module level. Although $\mathcal{E}$rgoAI programs are compiled into XSB and adopt many Prolog features, $\mathcal{E}$rgoAI is altogether a different language and system. Under consideration in Theory and Practice of Logic Programming (TPLP).

We develop a computational approach to Metric Answer Set Programming (ASP) to express quantitative temporal constrains, such as durations and deadlines. We investigate two specific fragments: plain metric logic programs, restricted to local temporal constraints, and general metric logic programs, which allow for arbitrary metric formulas. A central challenge in this context is maintaining scalability when dealing with fine-grained timing constraints, which can significantly exacerbate grounding bottleneck of ASP. To address this issue, we propose translations of both fragments into standard ASP and ASP extended with difference constraints, a simplified form of linear constraints, and prove their correctness and completeness. Our implementation, realized via meta-encodings, effectively decouples metric ASP from the granularity of time, resulting in a solution that is independent of time precision.