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Eiter, T., Geibinger, T., Higuera Ruiz, N., Musliu, N., Oetsch, J., Pfliegler, D. & Stepanova, D. (2024). Adaptive large-neighbourhood search for optimisation in answer-set programming. Artificial Intelligence, 337, Article ID 104230.
Åpne denne publikasjonen i ny fane eller vindu >>Adaptive large-neighbourhood search for optimisation in answer-set programming
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2024 (engelsk)Inngår i: Artificial Intelligence, ISSN 0004-3702, E-ISSN 1872-7921, Vol. 337, artikkel-id 104230Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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.

sted, utgiver, år, opplag, sider
Elsevier, 2024
Emneord
Integer programming, Railroad transportation, Adaptive large neighborhood searches, Answer set programming, Automatically generated, Declarative problem solving, Encodings, Large neighbourhood searches, Metaheuristic, Neighbourhood, Optimisations, Optimization problems, Benchmarking
HSV kategori
Identifikatorer
urn:nbn:se:hj:diva-66309 (URN)10.1016/j.artint.2024.104230 (DOI)001322435400001 ()2-s2.0-85204583992 (Scopus ID)HOA;intsam;975157 (Lokal ID)HOA;intsam;975157 (Arkivnummer)HOA;intsam;975157 (OAI)
Forskningsfinansiär
EU, Horizon 2020, 101034440
Tilgjengelig fra: 2024-09-30 Laget: 2024-09-30 Sist oppdatert: 2024-10-14bibliografisk kontrollert
Eiter, T., Geibinger, T., Higuera, N. & Oetsch, J. (2023). A Logic-based Approach to Contrastive Explainability for Neurosymbolic Visual Question Answering. In: IJCAI International Joint Conference on Artificial Intelligence: . Paper presented at 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023, 19 August-25 August 2023 (pp. 3668-3676). International Joint Conferences on Artificial Intelligence
Åpne denne publikasjonen i ny fane eller vindu >>A Logic-based Approach to Contrastive Explainability for Neurosymbolic Visual Question Answering
2023 (engelsk)Inngår i: IJCAI International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence , 2023, s. 3668-3676Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

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.

sted, utgiver, år, opplag, sider
International Joint Conferences on Artificial Intelligence, 2023
Emneord
Logic programming, Query processing, Answer set programming, Logic-based approach, Logical theories, Modular architectures, Performance, Question Answering, Deep learning
HSV kategori
Identifikatorer
urn:nbn:se:hj:diva-63554 (URN)10.24963/ijcai.2023/408 (DOI)2-s2.0-85170397066 (Scopus ID)9781956792034 (ISBN)
Konferanse
32nd International Joint Conference on Artificial Intelligence, IJCAI 2023, 19 August-25 August 2023
Tilgjengelig fra: 2024-02-16 Laget: 2024-02-16 Sist oppdatert: 2024-02-16bibliografisk kontrollert
Eiter, T., Ruiz, N. H. & Oetsch, J. (2023). A Modular Neurosymbolic Approach for Visual Graph Question Answering. In: A. S. d'Avila Garcez, T. R. Besold, M. Gori & E. Jiménez-Ruiz (Ed.), Proceedings of the 17th International Workshop on Neural-Symbolic Learning and Reasoning La Certosa di Pontignano, Siena, Italy, July 3-5, 2023: . Paper presented at 17th International Workshop on Neural-Symbolic Learning and Reasoning, Siena, Italy, July 3-5, 2023 (pp. 139-149). CEUR-WS
Åpne denne publikasjonen i ny fane eller vindu >>A Modular Neurosymbolic Approach for Visual Graph Question Answering
2023 (engelsk)Inngår i: Proceedings of the 17th International Workshop on Neural-Symbolic Learning and Reasoning La Certosa di Pontignano, Siena, Italy, July 3-5, 2023 / [ed] A. S. d'Avila Garcez, T. R. Besold, M. Gori & E. Jiménez-Ruiz, CEUR-WS , 2023, s. 139-149Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

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. 

sted, utgiver, år, opplag, sider
CEUR-WS, 2023
Serie
CEUR Workshop Proceedings, ISSN 1613-0073 ; 3432
Emneord
answer-set programming, neurosymbolic computation, visual question answering, Computation theory, Graph theory, Graphic methods, Logic programming, Natural language processing systems, Text processing, Answer set programming, Data representations, Graph-based, Metro networks, Modulars, Question Answering, Vertex-labeled graphs, Visual Graph, Optical character recognition
HSV kategori
Identifikatorer
urn:nbn:se:hj:diva-63555 (URN)2-s2.0-85167445992 (Scopus ID)
Konferanse
17th International Workshop on Neural-Symbolic Learning and Reasoning, Siena, Italy, July 3-5, 2023
Tilgjengelig fra: 2024-02-16 Laget: 2024-02-16 Sist oppdatert: 2024-02-16bibliografisk kontrollert
Eiter, T., Geibinger, T., Musli, N., Oetsch, J., Skočovský, P. & Stepanova, D. (2023). Answer-Set Programming for Lexicographical Makespan Optimisation in Parallel Machine Scheduling. Theory and Practice of Logic Programming, 23(6), 1281-1306
Åpne denne publikasjonen i ny fane eller vindu >>Answer-Set Programming for Lexicographical Makespan Optimisation in Parallel Machine Scheduling
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2023 (engelsk)Inngår i: Theory and Practice of Logic Programming, ISSN 1471-0684, E-ISSN 1475-3081, Vol. 23, nr 6, s. 1281-1306Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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.

sted, utgiver, år, opplag, sider
Cambridge University Press, 2023
Emneord
answer-set programming, parallel machine scheduling, lexicographical optimisation
HSV kategori
Identifikatorer
urn:nbn:se:hj:diva-63645 (URN)10.1017/s1471068423000017 (DOI)000920784500001 ()
Tilgjengelig fra: 2024-02-21 Laget: 2024-02-21 Sist oppdatert: 2024-02-21bibliografisk kontrollert
Eiter, T., Geibinger, T. & Oetsch, J. (2023). Contrastive Explanations for Answer-Set Programs. In: S. Gaggl, M. V. Martinez & M. Ortiz (Ed.), Logics in Artificial Intelligence: 18th European Conference, JELIA 2023, Dresden, Germany, September 20–22, 2023, Proceedings. Paper presented at 18th European Conference, JELIA 2023, Dresden, Germany, September 20–22, 2023 (pp. 73-89). Springer
Åpne denne publikasjonen i ny fane eller vindu >>Contrastive Explanations for Answer-Set Programs
2023 (engelsk)Inngår i: Logics in Artificial Intelligence: 18th European Conference, JELIA 2023, Dresden, Germany, September 20–22, 2023, Proceedings / [ed] S. Gaggl, M. V. Martinez & M. Ortiz, Springer, 2023, s. 73-89Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Answer-Set Programming (ASP) is a popular declarative reasoning and problem solving formalism. Due to the increasing interest in explainability, several explanation approaches have been developed for ASP. However, while those formalisms are correct and interesting on their own, most are more technical and less oriented towards philosophical or social concepts of explanation. In this work, we study the notion of contrastive explanation, i.e., answering questions of the form “Why P instead of Q?”, in the context of ASP. In particular, we are interested in answering why atoms are included in an answer set, whereas others are not. Contrastive explainability has recently become popular due to its strong support from the philosophical, cognitive, and social sciences and its apparent ability to provide explanations that are concise and intuitive for humans. We formally define contrastive explanations for ASP based on counterfactual reasoning about programs. Furthermore, we demonstrate the usefulness of the concept on example applications and give some complexity results. The latter also provide a guideline as to how the explanations can be computed in practice.

sted, utgiver, år, opplag, sider
Springer, 2023
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 14281
Emneord
Philosophical aspects, Answer set, Answer set programming, Complexity results, Counterfactuals, Problem-solving, Reasoning about programs, Social concepts, Logic programming
HSV kategori
Identifikatorer
urn:nbn:se:hj:diva-63553 (URN)10.1007/978-3-031-43619-2_6 (DOI)2-s2.0-85174482316 (Scopus ID)9783031436185 (ISBN)
Konferanse
18th European Conference, JELIA 2023, Dresden, Germany, September 20–22, 2023
Tilgjengelig fra: 2024-02-16 Laget: 2024-02-16 Sist oppdatert: 2024-02-16bibliografisk kontrollert
Bauer, J. J., Eiter, T., Ruiz, N. H. & Oetsch, J. (2023). Neuro-symbolic Visual Graph Question Answering with LLMs for language parsing. In: : . Paper presented at TAASP 2023, Workshop on Trends and Applications of Answer Set Programming, November 20-21, 2023, Potsdam, Germany.
Åpne denne publikasjonen i ny fane eller vindu >>Neuro-symbolic Visual Graph Question Answering with LLMs for language parsing
2023 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Images containing graph-based structures are an 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 provide arespective novel dataset and present a modular neuro-symbolic approach as a first baseline. Our dataset extends CLEGR, an existing dataset for question answering on graphs inspired by metro networks. Notably, the graphs there are given in symbolic form, while we consider the more challenging problem of taking images of graphs as input. Our solution combines optical graph recognition for graph parsing, a pre-trained optical character recognition neural network for parsing node labels, and answer-set programming for reasoning. The model achieves an overall average accuracy of 73% on the dataset. While regular expressions are sufficient to parse the natural language questions, we also study various large-language models to obtain a more robust solution that also generalises well to variants of questions that are not part of the dataset. Our evaluation provides further evidence of the potential of modular neuro-symbolic systems, in particular with pre-trained models, to solve complex VQA tasks.

Emneord
neuro-symbolic computation, answer-set programming, visual question answering, large-language models
HSV kategori
Identifikatorer
urn:nbn:se:hj:diva-63647 (URN)
Konferanse
TAASP 2023, Workshop on Trends and Applications of Answer Set Programming, November 20-21, 2023, Potsdam, Germany
Tilgjengelig fra: 2024-02-21 Laget: 2024-02-21 Sist oppdatert: 2024-02-21bibliografisk kontrollert
Eiter, T., Higuera, N., Oetsch, J. & Pritz, M. (2022). A confidence-based interface for neuro-symbolic visual question answering. In: Combining learning and reasoning: Programming languages, formalisms, and representations: CLeaR-Workshop. Paper presented at CLeaR 2022, The First International Workshop on Combining Learning and Reasoning: Programming Languages, Formalisms, and Representations In conjunction with the 36th AAAI conference on artificial intelligence (AAAI-2022), February 22–March 1, 2022, Vancouver, BC, Canada.
Åpne denne publikasjonen i ny fane eller vindu >>A confidence-based interface for neuro-symbolic visual question answering
2022 (engelsk)Inngår i: Combining learning and reasoning: Programming languages, formalisms, and representations: CLeaR-Workshop, 2022Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

We present a neuro-symbolic visual question answering (VQA) approach for the CLEVR dataset that is based on the combination of deep neural networks and answer-set programming (ASP), a logic-based paradigm for declarative problem solving. We provide a translation mechanism for the questions included in CLEVR to ASP programs. By exploiting choice rules, we consider deterministic and non-deterministic scene encodings. In addition, we introduce a confidence-based interface between the ASP module and the neural network which allows us to restrict the non-determinism to objects classified by the network with high confidence. Our experiments show that the non-deterministic scene encoding achieves good results even if the neural networks are trained rather poorly in comparison with the deterministic approach. This is important for building robust VQA systems if network predictions are less-than perfect.

Emneord
neuro-symbolic reasoning, visual-question answering, answer-set programming, deep learning
HSV kategori
Identifikatorer
urn:nbn:se:hj:diva-63652 (URN)
Konferanse
CLeaR 2022, The First International Workshop on Combining Learning and Reasoning: Programming Languages, Formalisms, and Representations In conjunction with the 36th AAAI conference on artificial intelligence (AAAI-2022), February 22–March 1, 2022, Vancouver, BC, Canada
Tilgjengelig fra: 2024-02-21 Laget: 2024-02-21 Sist oppdatert: 2024-02-21bibliografisk kontrollert
Eiter, T., Higuera, N., Oetsch, J. & Pritz, M. (2022). A Neuro-Symbolic ASP Pipeline for Visual Question Answering. Theory and Practice of Logic Programming, 22(5), 739-754
Åpne denne publikasjonen i ny fane eller vindu >>A Neuro-Symbolic ASP Pipeline for Visual Question Answering
2022 (engelsk)Inngår i: Theory and Practice of Logic Programming, ISSN 1471-0684, E-ISSN 1475-3081, Vol. 22, nr 5, s. 739-754Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

We present a neuro-symbolic visual question answering (VQA) pipeline for CLEVR, which is a well-known dataset that consists of pictures showing scenes with objects and questions related to them. Our pipeline covers (i) training neural networks for object classification and bounding-box prediction of the CLEVR scenes, (ii) statistical analysis on the distribution of prediction values of the neural networks to determine a threshold for high-confidence predictions, and (iii) a translation of CLEVR questions and network predictions that pass confidence thresholds into logic programmes so that we can compute the answers using an answer-set programming solver. By exploiting choice rules, we consider deterministic and non-deterministic scene encodings. Our experiments show that the non-deterministic scene encoding achieves good results even if the neural networks are trained rather poorly in comparison with the deterministic approach. This is important for building robust VQA systems if network predictions are less-than perfect. Furthermore, we show that restricting non-determinism to reasonable choices allows for more efficient implementations in comparison with related neuro-symbolic approaches without losing much accuracy.

sted, utgiver, år, opplag, sider
Cambridge University Press, 2022
Emneord
answer-set programming, neuro-symbolic computation, visual question answering, Computation theory, Encoding (symbols), Forecasting, Logic programming, Program translators, Answer set programming, Deterministics, Encodings, Network prediction, Neural-networks, Object classification, Question Answering, Symbolic computation, Pipelines
HSV kategori
Identifikatorer
urn:nbn:se:hj:diva-63556 (URN)10.1017/S1471068422000229 (DOI)000825043500001 ()2-s2.0-85136279413 (Scopus ID)
Tilgjengelig fra: 2024-02-16 Laget: 2024-02-16 Sist oppdatert: 2024-02-16bibliografisk kontrollert
Eiter, T., Geibinger, T., Higuera, N., Musliu, N., Oetsch, J. & Stepanova, D. (2022). ALASPO: An Adaptive Large-Neighbourhood ASP Optimiser. In: G. Kern-Isberner G. Lakemeyer & T. Meyer (Ed.), KR 2022: Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning. Paper presented at 19th International Conference on Principles of Knowledge Representation and Reasoning, KR 2022, 31 July-5 August 2022, Haifa, Israel (pp. 565-569). IJCAI Organization
Åpne denne publikasjonen i ny fane eller vindu >>ALASPO: An Adaptive Large-Neighbourhood ASP Optimiser
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2022 (engelsk)Inngår i: KR 2022: Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning / [ed] G. Kern-Isberner G. Lakemeyer & T. Meyer, IJCAI Organization , 2022, s. 565-569Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

We present the system ALASPO which implements Adaptive Large-neighbourhood search for Answer Set Programming (ASP) Optimisation. Large-neighbourhood search (LNS) is a meta-heuristic where parts of a solution are destroyed and reconstructed in an attempt to improve an overall objective. ALASPO currently supports the ASP solver clingo, as well as its extensions clingo-dl and clingcon for difference and full integer constraints, and multi-shot solving for an efficient implementation of the LNS loop. Neighbourhoods can be defined in code or declaratively as part of the ASP encoding. While the method underlying ALASPO has been described in previous work, ALASPO also incorporates portfolios for the LNS operators along with self-adaptive selection strategies as a technical novelty. This improves usability considerably at no loss of solution quality, but on the contrary often yields benefits. To demonstrate this, we evaluate ALASPO on different optimisation benchmarks.

sted, utgiver, år, opplag, sider
IJCAI Organization, 2022
Emneord
Knowledge representation, Logic programming, Adaptive large neighborhood searches, Answer set programming, Efficient implementation, Integer constraints, Large neighbourhood, Large neighbourhood searches, Metaheuristic, Multi-shot, Neighbourhood, Optimisations, Optimization
HSV kategori
Identifikatorer
urn:nbn:se:hj:diva-63559 (URN)2-s2.0-85141870502 (Scopus ID)9781956792010 (ISBN)
Konferanse
19th International Conference on Principles of Knowledge Representation and Reasoning, KR 2022, 31 July-5 August 2022, Haifa, Israel
Tilgjengelig fra: 2024-02-16 Laget: 2024-02-16 Sist oppdatert: 2024-02-16bibliografisk kontrollert
Eiter, T., Geibinger, T., Gisbrecht, A., Ruiz, N. H., Musliu, N., Oetsch, J. & Stepanova, D. (2022). An open challenge for exact job scheduling with reticle batching in photolithography. In: : . Paper presented at 2022 Workshop on Knowledge Engineering for Planning and Scheduling, An ICAPS'22 Workshop, 15 June 2022, Singapore. Association for the Advancement of Artificial Intelligence
Åpne denne publikasjonen i ny fane eller vindu >>An open challenge for exact job scheduling with reticle batching in photolithography
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2022 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

We consider scheduling solutions for photolithography, an important sub-task in semi-conductor production, where patterns are transferred to wafers using reticles. The problem can be modelled as job scheduling on unrelated parallel machines with sequence-dependent setup times and release dates. The reticles add auxiliary-resource constraints for processing jobs. Equipping machines with the right reticles using transport robots from stockers in time renders this problem extremely difficult for exact solvers that use a declarative model. The latter would be attractive as such models tend to be compact and easy to maintain. We present a solver-independent MiniZinc model and provide 500 new benchmark instances. However, only small instances can be solved with state-of-the-art MIP and CP solvers. Consequently, we present this problem as an open challenge with considerable potential for driving improvements towards industrial applications.

sted, utgiver, år, opplag, sider
Association for the Advancement of Artificial Intelligence, 2022
HSV kategori
Identifikatorer
urn:nbn:se:hj:diva-63653 (URN)
Konferanse
2022 Workshop on Knowledge Engineering for Planning and Scheduling, An ICAPS'22 Workshop, 15 June 2022, Singapore
Tilgjengelig fra: 2024-02-21 Laget: 2024-02-21 Sist oppdatert: 2024-02-21bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-9902-7662