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Constraint Programming (CP) has proved an effective paradigm to model and solve difficult combinatorial satisfaction and optimization problems from disparate domains. Many such problems arising from the commercial world are permeated by data uncertainty. Existing CP approaches that accommodate uncertainty are less suited to uncertainty arising due to incomplete and erroneous data, because they do not build reliable models and solutions guaranteed to address the user's genuine problem as she perceives it. Other fields such as reliable computation offer combinations of models and associated methods to handle these types of uncertain data, but lack an expressive framework characterizing the resolution methodology independently of the model. We present a unifying framework that extends the CP formalism in both model and solutions, to tackle ill-defined combinatorial problems with incomplete or erroneous data. The certainty closure framework brings together modeling and solving methodologies from different fields into the CP paradigm to provide reliable and efficient approches for uncertain constraint problems. We demonstrate the applicability of the framework on a case study in network diagnosis. We define resolution forms that give generic templates, and their associated operational semantics, to derive practical solution methods for reliable solutions. [ABSTRACT FROM AUTHOR]

In real-life temporal scenarios, uncertainty and preferences are often essential and coexisting aspects. We present a formalism where quantitative temporal constraints with both preferences and uncertainty can be defined. We show how three classical notions of controllability (that is, strong, weak, and dynamic), which have been developed for uncertain temporal problems, can be generalized to handle preferences as well. After defining this general framework, we focus on problems where preferences follow the fuzzy approach, and with properties that assure tractability. For such problems, we propose algorithms to check the presence of the controllability properties. In particular, we show that in such a setting dealing simultaneously with preferences and uncertainty does not increase the complexity of controllability testing. We also develop a dynamic execution algorithm, of polynomial complexity, that produces temporal plans under uncertainty that are optimal with respect to fuzzy preferences. [ABSTRACT FROM AUTHOR]

In cross-cultural business negotiation, culture is known to influence negotiation processes. As a lens to study this effect we deployed the Lemon Car Game, an online negotiation game developed for this purpose (Hofstede et al. in: Proceedings of 39th international simulation and gaming association conference (ISAGA). Technologia, Kaunas, pp 39–46, 2009a; Hofstede et al. in: David, Sichman (eds) Multi-agent-based simulation IX, international workshop, MABS 2008, revised selected papers, LNAI 5269. Springer, Berlin, pp 1–16, 2009b). In this article we report the results from the game, obtained from over 800 players from more than 70 countries. We employ several complementary analyses in a mixed-methods approach. Our findings show that to make sense of the players' actions during negotiation, economic rationality falls short. A pan-cultural individual-level analysis of actions and stated intentions also fails to yield a coherent picture. Within countries, however, actions and intentions do cohere, as shown by an ecological country-level factor analysis, from which three factors emerge for the sellers at country level: trustworthiness, opportunism, and fairness. We conclude from these findings that, in this game, players are driven by what we call relational rationality: they are rational from the perspective of the social world in which they live, with interpersonal relationships weighing heavily. Relational rationality changes players' perspective of economic rationality, and thus their observed behaviour in negotiation. Based on this evidence, we extrapolate that relational rationality significantly influences negotiation processes in all cultures. [ABSTRACT FROM AUTHOR]

• A novel surrogate model-assisted inversion method is proposed for identifying viscoelastic parameters of dam system. • The sensitivity of the training sample size, parameter range and output quantity of the MOGP surrogate model is investigated. • The computational accuracy and efficiency of the proposed inversion method are proved. • A physics-based monitoring model is calibrated for long-term deformation prediction of the concrete dam. Dam safety monitoring has become an important topic and is critical for evaluating a dam's safety status. This study focuses on identifying the mechanical properties of a concrete dam from long-term viscoelastic deformation monitoring data. A novel inversion framework is proposed in which a surrogate model, instead of the finite element model, is placed inside the optimization loop. First, a multi-output surrogate model based on Gaussian process is trained by using data from a finite element simulation in the creep regime. In order to efficiently create a high-precision and reliable surrogate model, three test instances are conducted to investigate the impact of sample size, parameter range and output quantity on the performance of the surrogate model. Subsequently, a meta-heuristic optimization, multi-verse optimizer, is employed to identify the unknown viscoelastic parameters. The results illustrate that the identified properties allow predictions on dam displacement which are consistent with the monitoring data. Compared with the traditional inversion method based on finite element modelling, the proposed inversion method based on the multi-output surrogate model not only achieves accurate estimation of mechanical parameters but also greatly improves computational efficiency. [ABSTRACT FROM AUTHOR]

Background Using knowledge representation for biomedical projects is now commonplace. In previous work, we represented the knowledge found in a college-level biology textbook in a fashion useful for answering questions. We showed that embedding the knowledge representation and question-answering abilities in an electronic textbook helped to engage student interest and improve learning. A natural question that arises from this success, and this paper's primary focus, is whether a similar approach is applicable across a range of life science textbooks. To answer that question, we considered four different textbooks, ranging from a belowintroductory college biology text to an advanced, graduate-level neuroscience textbook. For these textbooks, we investigated the following questions: (1) To what extent is knowledge shared between the different textbooks? (2) To what extent can the same upper ontology be used to represent the knowledge found in different textbooks? (3) To what extent can the questions of interest for a range of textbooks be answered by using the same reasoning mechanisms? Results Our existing modeling and reasoning methods apply especially well both to a textbook that is comparable in level to the text studied in our previous work (i.e., an introductory-level text) and to a textbook at a lower level, suggesting potential for a high degree of portability. Even for the overlapping knowledge found across the textbooks, the level of detail covered in each textbook was different, which requires that the representations must be customized for each textbook. We also found that for advanced textbooks, representing models and scientific reasoning processes was particularly important. Conclusions With some additional work, our representation methodology would be applicable to a range of textbooks. The requirements for knowledge representation are common across textbooks, suggesting that a shared semantic infrastructure for the life sciences is feasible. Because our representation overlaps heavily with those already being used for biomedical ontologies, this work suggests a natural pathway to include such representations as part of the life sciences curriculum at different grade levels. [ABSTRACT FROM AUTHOR]

DC distribution systems are a promising alternative to existing AC distribution systems. They connect customers to local energy sources without conversion, thus reducing power losses. However, the unique features of DC impose strict requirements for system operation compared to AC. Within the context of a liberalized energy market, this article demonstrates three promising market designs—an outcome of a comprehensive engineering design framework—that meet those DC requirements. They are an integrated market design, which incorporates all system costs into energy prices; a market design that passes wholesale energy prices directly to prosumers; and a locational energy market design that relieves congestion with nodal prices. An optimization model estimates the three market designs' performance by simulating a realistic DC distribution system, featuring a high share of electric vehicles. Results indicate that the integrated market design is optimal in theory but computationally infeasible in practice. The wholesale energy price design aiming at constraint-free energy trading requires substantial investments in flexibility. The locational energy market design yields nearly optimal operation in urban networks and is considered the best feasible market design for DC distribution systems. [ABSTRACT FROM AUTHOR]

Recent work has shown potential in using Mixed Integer Programming (MIP) solvers to optimize certain aspects of neural networks (NNs). However the intriguing approach of training NNs with MIP solvers is under-explored. State-of-the-art-methods to train NNs are typically gradient-based and require significant data, computation on GPUs, and extensive hyper-parameter tuning. In contrast, training with MIP solvers does not require GPUs or heavy hyper-parameter tuning, but currently cannot handle anything but small amounts of data. This article builds on recent advances that train binarized NNs using MIP solvers. We go beyond current work by formulating new MIP models which improve training efficiency and which can train the important class of integer-valued neural networks (INNs). We provide two novel methods to further the potential significance of using MIP to train NNs. The first method optimizes the number of neurons in the NN while training. This reduces the need for deciding on network architecture before training. The second method addresses the amount of training data which MIP can feasibly handle: we provide a batch training method that dramatically increases the amount of data that MIP solvers can use to train. We thus provide a promising step towards using much more data than before when training NNs using MIP models. Experimental results on two real-world data-limited datasets demonstrate that our approach strongly outperforms the previous state of the art in training NN with MIP, in terms of accuracy, training time and amount of data. Our methodology is proficient at training NNs when minimal training data is available, and at training with minimal memory requirements-which is potentially valuable for deploying to low-memory devices., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Thorbjarnarson, Yorke-Smith. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)