The transition to the circular economy in the built environment requires decision support systems that can turn product data into operational End-of-Life (EoL) strategies. Digital Product Passports (DPPs), emerging as structured records of product lifecycle data, offer strong potential to support such decisions. However, methods for transforming DPP data into practical EoL recommendations remain an important area for research and development. To fill this gap, this position paper explores the integration of Answer-Set Programming (ASP), which is a declarative AI technique for complex rule-based reasoning that can be applied to DPPs. This study proposes a conceptual framework in which structured ontology- and knowledge graph-based DPP data, along with unstructured data processed using Large Language Models (LLMs), inform ASP-based models to recommend reuse, recycling, or disposal strategies under regulatory and environmental constraints. ASP’s strengths in knowledge representation and optimization are identified as making it a promising candidate for advancing intelligent and practical EoL management. The proposed system provides stakeholders with useful information to improve EoL strategies for building components by targeting measurable outcomes such as higher material recovery rates and better compliance with circular economy regulations.

Reinforcement Learning (RL) enables autonomous agents to learn policies from experience, but realistic problems often involve enormous state spaces, making learning and generalisation challenging. Abstraction and approximation are therefore essential. Relational Reinforcement Learning (RRL) offers a way to reason about objects and their relations, and the CARCASS framework by Martijn van Otterlo demonstrates how logical representations can model Markov Decision Processes (MDPs) in first-order domains. Originally implemented in Prologue, CARCASS leverages domain knowledge to create powerful abstractions. We explore Answer-Set Programming (ASP), which is a rich and, contrary to Prologue, fully declarative modelling language, to realise CARCASS abstractions. We evaluate our ASP-based implementation in case studies of two domains, viz. Blocks World and Minigrid. Our results indicate that CARCASS with ASP provides a promising approach to constructing abstractions for RL, especially when domain knowledge is available (our implementation is available at https://github.com/rbankosegger/RLASP-core. Further material (data, encodings, extended documentation) can be found here: https://www.bankosegger.at/iclp26/.).

We present the solver asp-fzn for Constraint Answer Set Programming (CASP), which extends ASP with linear constraints. Our approach is based on translating CASP programs into the solver-independent FlatZinc language that supports several Constraint Programming and Integer Programming backend solvers. Our solver supports a rich language of linear constraints, including some common global constraints. As for evaluation, we show that asp-fzn is competitive with state-of-the-art ASP solvers on benchmarks taken from past ASP competitions. Furthermore, we evaluate it on several CASP problems from the literature and compare its performance with clingcon, which is a prominent CASP solver that supports most of the asp-fzn language. The performance of asp-fzn is very promising as it is already competitive on plain ASP and even outperforms clingcon on some CASP benchmarks.

Visual Question Answering (VQA) is the task of answering natural language questions about images, which is a challenge for AI systems. To enhance adaptability and reduce training overhead, we address VQA in a zero-shot setting by leveraging pre-trained neural modules without additional fine-tuning. Our proposed hybrid neurosymbolic framework, whose capabilities are demonstrated on the challenging GQA dataset, integrates neural and symbolic components through logic-based reasoning via Answer-Set Programming. Specifically, our pipeline employs large language models for semantic parsing of input questions, followed by the generation of a scene graph that captures relevant visual content. Interpretable rules then operate on the symbolic representations of both the question and the scene graph to derive an answer. Our framework provides a key advantage: it enables full transparency into the reasoning process. Using an existing explanation tool, we illustrate how our method fosters trust by making decisions interpretable and facilitates error analysis when predictions are incorrect. Beyond explaining its own reasoning, our framework can also explain answers from more opaque models by integrating their answers into our system, enabling broader interpretability in VQA. 

This extended abstract presents GS-VQA, a neurosymbolic system for zero-shot Visual Question Answering (VQA). GS-VQA constructs symbolic, question-conditioned scene graphs from real-world images using zero-shot vision models guided by large language models. These graphs are effectively knowledge graphs that can be used for logic-based inference using Answer-Set Programming (ASP). The system enables question answering via symbolic inference and can generate logical explanation traces using xclingo. Evaluations on the GQA benchmark demonstrate the method’s transparency and diagnostic power despite modest accuracy in comparison to state of the art neural systems. 

Visual Question Answering (VQA) is a challenging problem that requires to process multimodal input. Answer-Set Programming (ASP) has shown great potential in this regard to add interpretability and explainability to modular VQA architectures. In this work, we address the problem of how to integrate ASP with modules for vision and natural language processing to solve a new and demanding VQA variant that is concerned with images of graphs (not graphs in symbolic form). Images containing graph-based structures are an ubiquitous and popular form of visualisation. Here, we deal with the particular problem of graphs inspired by transit networks, and we introduce a novel dataset that amends an existing one by adding images of graphs that resemble metro lines. Our modular neuro-symbolic approach combines optical graph recognition for graph parsing, a pretrained optical character recognition neural network for parsing labels, Large Language Models (LLMs) for language processing, and ASP for reasoning. This method serves as a first baseline and achieves an overall average accuracy of 73% on the dataset. Our evaluation provides further evidence of the potential of modular neuro-symbolic systems, in particular with pretrained models that do not involve any further training and logic programming for reasoning, to solve complex VQA tasks.

Answer-set programming (ASP) is a prominent approach to declarative problem solving that is increasingly used to tackle challenging optimisation problems. We present an approach to leverage ASP optimisation by using large-neighbourhood search (LNS), which is a meta-heuristic where parts of a solution are iteratively destroyed and reconstructed in an attempt to improve an overall objective. In our LNS framework, neighbourhoods can be specified either declaratively as part of the ASP encoding or automatically generated by code. Furthermore, our framework is self-adaptive, i.e., it also incorporates portfolios for the LNS operators along with selection strategies to adjust search parameters on the fly. The implementation of our framework, the system ALASPO, currently supports the ASP solver clingo, as well as its extensions clingo-dl and clingcon that allow for difference and full integer constraints, respectively. It utilises multi-shot solving to efficiently realise the LNS loop and in this way avoids program regrounding. We describe our LNS framework for ASP as well as its implementation, discuss methodological aspects, and demonstrate the effectiveness of the adaptive LNS approach for ASP on different optimisation benchmarks, some of which are notoriously difficult, as well as real-world applications for shift planning, configuration of railway-safety systems, parallel machine scheduling, and test laboratory scheduling.

Visual Question Answering (VQA) is a well-known problem for which deep-learning is key. This poses a challenge for explaining answers to questions, the more if advanced notions like contrastive explanations (CEs) should be provided. The latter explain why an answer has been reached in contrast to a different one and are attractive as they focus on reasons necessary to flip a query answer. We present a CE framework for VQA that uses a neurosymbolic VQA architecture which disentangles perception from reasoning. Once the reasoning part is provided as logical theory, we use answer-set programming, in which CE generation can be framed as an abduction problem. We validate our approach on the CLEVR dataset, which we extend by more sophisticated questions to further demonstrate the robustness of the modular architecture. While we achieve top performance compared to related approaches, we can also produce CEs for explanation, model debugging, and validation tasks, showing the versatility of the declarative approach to reasoning.

Images containing graph-based structures are a ubiquitous and popular form of data representation that, to the best of our knowledge, have not yet been considered in the domain of Visual Question Answering (VQA). We use CLEGR, a graph question answering dataset with a generator that synthetically produces vertex-labelled graphs that are inspired by metro networks. Structured information about stations and lines is provided, and the task is to answer natural language questions concerning such graphs. While symbolic methods suffice to solve this dataset, we consider the more challenging problem of taking images of the graphs instead of their symbolic representations as input. Our solution takes the form of a modular neurosymbolic model that combines the use of optical graph recognition for graph parsing, a pretrained optical character recognition neural network for parsing node labels, and answer-set programming, a popular logic-based approach to declarative problem solving, for reasoning. The implementation of the model achieves an overall average accuracy of 73% on the dataset, providing further evidence of the potential of modular neurosymbolic systems in solving complex VQA tasks, in particular, the use and control of pretrained models in this architecture. 

We deal with a challenging scheduling problem on parallel machines with sequence-dependent setup times and release dates from a real-world application of semiconductor work-shop production. There, jobs can only be processed by dedicated machines, thus few machines can determine the makespan almost regardless of how jobs are scheduled on the remaining ones. This causes problems when machines fail and jobs need to be rescheduled. Instead of optimising only the makespan, we put the individual machine spans in non-ascending order and lexicographically minimise the resulting tuples. This achieves that all machines complete as early as possible and increases the robustness of the schedule. We study the application of answer-set programming (ASP) to solve this problem. While ASP eases modelling, the combination of timing constraints and the considered objective function challenges current solving technology. The former issue is addressed by using an extension of ASP by difference logic. For the latter, we devise different algorithms that use multi-shot solving. To tackle industrial-sized instances, we study different approximations and heuristics. Our experimental results show that ASP is indeed a promising knowledge representation and reasoning (KRR) paradigm for this problem and is competitive with state-of-the-art constraint programming (CP) and Mixed-Integer Programming (MIP) solvers.