Pub Date : 2022-12-22DOI: 10.1007/978-3-031-36027-5_36
T. Nguyen, T. Dairay, Raphael Meunier, C. Millet, M. Mougeot
{"title":"Fixed-Budget Online Adaptive Learning for Physics-Informed Neural Networks. Towards Parameterized Problem Inference","authors":"T. Nguyen, T. Dairay, Raphael Meunier, C. Millet, M. Mougeot","doi":"10.1007/978-3-031-36027-5_36","DOIUrl":"https://doi.org/10.1007/978-3-031-36027-5_36","url":null,"abstract":"","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132634528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-21DOI: 10.48550/arXiv.2212.11372
Supreeth Mysore Venkatesh, A. Macaluso, M. Klusch
The problem of generating an optimal coalition structure for a given coalition game of rational agents is to find a partition that maximizes their social welfare and is known to be NP-hard. This paper proposes GCS-Q, a novel quantum-supported solution for Induced Subgraph Games (ISGs) in coalition structure generation. GCS-Q starts by considering the grand coalition as initial coalition structure and proceeds by iteratively splitting the coalitions into two nonempty subsets to obtain a coalition structure with a higher coalition value. In particular, given an $n$-agent ISG, the GCS-Q solves the optimal split problem $mathcal{O} (n)$ times using a quantum annealing device, exploring $mathcal{O}(2^n)$ partitions at each step. We show that GCS-Q outperforms the currently best classical solvers with its runtime in the order of $n^2$ and an expected worst-case approximation ratio of $93%$ on standard benchmark datasets.
{"title":"GCS-Q: Quantum Graph Coalition Structure Generation","authors":"Supreeth Mysore Venkatesh, A. Macaluso, M. Klusch","doi":"10.48550/arXiv.2212.11372","DOIUrl":"https://doi.org/10.48550/arXiv.2212.11372","url":null,"abstract":"The problem of generating an optimal coalition structure for a given coalition game of rational agents is to find a partition that maximizes their social welfare and is known to be NP-hard. This paper proposes GCS-Q, a novel quantum-supported solution for Induced Subgraph Games (ISGs) in coalition structure generation. GCS-Q starts by considering the grand coalition as initial coalition structure and proceeds by iteratively splitting the coalitions into two nonempty subsets to obtain a coalition structure with a higher coalition value. In particular, given an $n$-agent ISG, the GCS-Q solves the optimal split problem $mathcal{O} (n)$ times using a quantum annealing device, exploring $mathcal{O}(2^n)$ partitions at each step. We show that GCS-Q outperforms the currently best classical solvers with its runtime in the order of $n^2$ and an expected worst-case approximation ratio of $93%$ on standard benchmark datasets.","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123904072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-27DOI: 10.1007/978-3-031-36030-5_2
F. Phillipson, N. Neumann, R. Wezeman
{"title":"Classification of Hybrid Quantum-Classical Computing","authors":"F. Phillipson, N. Neumann, R. Wezeman","doi":"10.1007/978-3-031-36030-5_2","DOIUrl":"https://doi.org/10.1007/978-3-031-36030-5_2","url":null,"abstract":"","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122661446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-25DOI: 10.1007/978-3-031-36021-3_47
Szymon Nowakowski, P. Pokarowski, W. Rejchel
{"title":"Improving Group Lasso for High-Dimensional Categorical Data","authors":"Szymon Nowakowski, P. Pokarowski, W. Rejchel","doi":"10.1007/978-3-031-36021-3_47","DOIUrl":"https://doi.org/10.1007/978-3-031-36021-3_47","url":null,"abstract":"","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129239191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-27DOI: 10.1007/978-3-031-35995-8_37
Teresa Salazar, Miguel X. Fernandes, Helder Araújo, Pedro Abreu
{"title":"FAIR-FATE: Fair Federated Learning with Momentum","authors":"Teresa Salazar, Miguel X. Fernandes, Helder Araújo, Pedro Abreu","doi":"10.1007/978-3-031-35995-8_37","DOIUrl":"https://doi.org/10.1007/978-3-031-35995-8_37","url":null,"abstract":"","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126210706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-31DOI: 10.48550/arXiv.2208.14866
Aolong Zha, Qi Chang, Naoto Imura, K. Nishinari
This paper is a study of an application-based model in profit-maximizing multi-vehicle pickup and delivery selection problem (PPDSP). The graph-theoretic model proposed by existing studies of PPDSP is based on transport requests to define the corresponding nodes (i.e., each request corresponds to a pickup node and a delivery node). In practice, however, there are probably multiple requests coming from or going to an identical location. Considering the road networks with the integratable nodes as above, we define a new model based on the integrated nodes for the corresponding PPDSP and propose a novel mixed-integer formulation. In comparative experiments with the existing formulation, as the number of integratable nodes increases, our method has a clear advantage in terms of the number of variables as well as the number of constraints required in the generated instances, and the accuracy of the optimized solution obtained within a given time.
{"title":"A case study of the profit-maximizing multi-vehicle pickup and delivery selection problem for the road networks with the integratable nodes","authors":"Aolong Zha, Qi Chang, Naoto Imura, K. Nishinari","doi":"10.48550/arXiv.2208.14866","DOIUrl":"https://doi.org/10.48550/arXiv.2208.14866","url":null,"abstract":"This paper is a study of an application-based model in profit-maximizing multi-vehicle pickup and delivery selection problem (PPDSP). The graph-theoretic model proposed by existing studies of PPDSP is based on transport requests to define the corresponding nodes (i.e., each request corresponds to a pickup node and a delivery node). In practice, however, there are probably multiple requests coming from or going to an identical location. Considering the road networks with the integratable nodes as above, we define a new model based on the integrated nodes for the corresponding PPDSP and propose a novel mixed-integer formulation. In comparative experiments with the existing formulation, as the number of integratable nodes increases, our method has a clear advantage in terms of the number of variables as well as the number of constraints required in the generated instances, and the accuracy of the optimized solution obtained within a given time.","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127327519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-04DOI: 10.1007/978-3-031-08754-7_21
H. Huynh
{"title":"Analysis of Public Transport (in)accessibility and Land-Use Pattern in Different Areas in Singapore","authors":"H. Huynh","doi":"10.1007/978-3-031-08754-7_21","DOIUrl":"https://doi.org/10.1007/978-3-031-08754-7_21","url":null,"abstract":"","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126961695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-24DOI: 10.48550/arXiv.2206.12510
Jonas Nusslein, Christoph Roch, Thomas Gabor, Claudia Linnhoff-Popien, Sebastian Feld
Black-box optimization (BBO) can be used to optimize functions whose analytic form is unknown. A common approach to realising BBO is to learn a surrogate model which approximates the target black-box function which can then be solved via white-box optimization methods. In this paper, we present our approach BOX-QUBO, where the surrogate model is a QUBO matrix. However, unlike in previous state-of-the-art approaches, this matrix is not trained entirely by regression, but mostly by classification between 'good' and 'bad' solutions. This better accounts for the low capacity of the QUBO matrix, resulting in significantly better solutions overall. We tested our approach against the state-of-the-art on four domains and in all of them BOX-QUBO showed better results. A second contribution of this paper is the idea to also solve white-box problems, i.e. problems which could be directly formulated as QUBO, by means of black-box optimization in order to reduce the size of the QUBOs to the information-theoretic minimum. Experiments show that this significantly improves the results for MAX-k-SAT.
{"title":"Black Box Optimization Using QUBO and the Cross Entropy Method","authors":"Jonas Nusslein, Christoph Roch, Thomas Gabor, Claudia Linnhoff-Popien, Sebastian Feld","doi":"10.48550/arXiv.2206.12510","DOIUrl":"https://doi.org/10.48550/arXiv.2206.12510","url":null,"abstract":"Black-box optimization (BBO) can be used to optimize functions whose analytic form is unknown. A common approach to realising BBO is to learn a surrogate model which approximates the target black-box function which can then be solved via white-box optimization methods. In this paper, we present our approach BOX-QUBO, where the surrogate model is a QUBO matrix. However, unlike in previous state-of-the-art approaches, this matrix is not trained entirely by regression, but mostly by classification between 'good' and 'bad' solutions. This better accounts for the low capacity of the QUBO matrix, resulting in significantly better solutions overall. We tested our approach against the state-of-the-art on four domains and in all of them BOX-QUBO showed better results. A second contribution of this paper is the idea to also solve white-box problems, i.e. problems which could be directly formulated as QUBO, by means of black-box optimization in order to reduce the size of the QUBOs to the information-theoretic minimum. Experiments show that this significantly improves the results for MAX-k-SAT.","PeriodicalId":125954,"journal":{"name":"International Conference on Conceptual Structures","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124356138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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