Active Inference is a framework that emphasizes the interaction between agents and their environment. While the framework has seen significant advancements in the development of agents, the environmental models are of...
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ISBN:
(纸本)9783031771378;9783031771385
Active Inference is a framework that emphasizes the interaction between agents and their environment. While the framework has seen significant advancements in the development of agents, the environmental models are often borrowed from reinforcement learning problems, which may not fully capture the complexity of multi-agent interactions or allow complex, conditional communication. This paper introduces Reactive Environments, a comprehensive paradigm that facilitates complex multi-agent communication. In this paradigm, both agents and environments are defined as entities encapsulated by boundaries with interfaces. This setup facilitates a robust framework for communication in nonequilibrium-Steady-State systems, allowing for complex interactions and information exchange. We present a Julia package ***, which is a specific implementation of Reactive Environments, where we utilize a Reactive programming style for efficient implementation. The flexibility of this paradigm is demonstrated through its application to several complex, multi-agent environments. These case studies highlight the potential of Reactive Environments in modeling sophisticated systems of interacting agents.
The Vehicle Routing Problem (VRP) is a classical combinatorial optimization problem. In this paper, we focus on Large-Scale VRP (LSVRP), which contains more than 200 customers. In particular, the Knowledge Guided Loca...
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ISBN:
(纸本)9783031700545;9783031700552
The Vehicle Routing Problem (VRP) is a classical combinatorial optimization problem. In this paper, we focus on Large-Scale VRP (LSVRP), which contains more than 200 customers. In particular, the Knowledge Guided Local Search (KGLS) has shown highly competitive performance for LSVRP, due to the strength of GLS for jumping out of local optima and improved utility functions of GLS. The newly discovered good or effective utility function used by KGLS suggests that the default utility function used in the traditional GLS is by no means the optimal. However, manually designing better utility function for GLS is very time-consuming and can involve much trial-and-error. To address this issue, we proposed to use Genetic programming (GP) to automatically design utility functions for GLS. We developed a GP training framework in which an individual stands for a possible utility function for GLS. To evaluate a GP individual, GLS runs on the training instances, where the GP individual is used as the utility function to identify the edges to penalize. We also designed a set of terminals to capture a wide range of possible factors for the utility function. The results on the commonly used X dataset demonstrates that GP successfully evolved significantly better GLS algorithms than the competitive KGLS on a majority of the large-scale X instances. The further analysis also shows the effectiveness of the newly learned GLS utility functions that take into account new factors which are not been considered by GLS and KGLS.
The evolution of parallel computing architectures presents new challenges for developing efficient parallelized codes. The emergence of heterogeneous systems has given rise to multiple programming models, each requiri...
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ISBN:
(纸本)9783031733697;9783031733703
The evolution of parallel computing architectures presents new challenges for developing efficient parallelized codes. The emergence of heterogeneous systems has given rise to multiple programming models, each requiring careful adaptation to maximize performance. In this context, we propose reevaluating memory layout designs for computational tasks within larger nodes by comparing various architectures. To gain insight into the performance discrepancies between shared memory and shared-address space settings, we systematically measure the bandwidth between cores and sockets using different methodologies. Our findings reveal significant differences in performance, suggesting that MPI running inside UNIX processes may not fully utilize its intranode bandwidth potential. In light of our work in the MPC thread-based MPI runtime, which can leverage shared memory to achieve higher performance due to its optimized layout, we advocate for enabling the use of shared memory within the MPI standard.
In higher education, it is challenging to cultivate non-computerscience majors’ programming concepts. This study used the GAME model (gamification, assessment, modeling, and enquiry) in a programming education cours...
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Demand response(DR)using shared energy storage systems(ESSs)is an appealing method to save electricity bills for users under demand charge and time-of-use(TOU)price.A novel Stackelberg-game-based ESS sharing scheme is...
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Demand response(DR)using shared energy storage systems(ESSs)is an appealing method to save electricity bills for users under demand charge and time-of-use(TOU)price.A novel Stackelberg-game-based ESS sharing scheme is proposed and analyzed in this *** this scheme,the interactions between selfish users and an operator are characterized as a Stackelberg *** holds a large-scale ESS that is shared among users in the form of energy *** sells energy to users and sets the selling price *** maximizes its profit through optimal pricing and ESS *** purchase some energy from operator for the reduction of their demand charges after operator's selling price is *** game-theoretic ESS sharing scheme is characterized and analyzed by formulating and solving a bi-level optimization *** upper-level optimization maximizes operator's profit and the lower-level optimization minimizes users'*** bi-level model is transformed and linearized into a mixed-integer linear programming(MILP)model using the mathematical programming with equilibrium constraints(MPEC)method and model linearizing *** studies with actual data are carried out to explore the economic performances of the proposed ESS sharing scheme.
Due to new enhancements in the field of computer architecture and the proliferation of heterogeneous computing devices, there is an increasing demand for portable and efficient programming applications. These applicat...
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In the context of Industry 4.0, which encompasses advanced technologies and interconnected systems, the integration of optimization techniques assumes a crucial role in addressing resource management challenges across...
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In the context of Industry 4.0, which encompasses advanced technologies and interconnected systems, the integration of optimization techniques assumes a crucial role in addressing resource management challenges across various sectors, including manufacturing systems. In bulk ports, which are an integral part of the manufacturing and logistics chain, one of the most critical resource management challenges is the scheduling of automated or semi-automated reclaimers. These machines are responsible for reclaiming dry bulk materials for loading onto vessels using ship-loaders. The efficient operation of reclaimers directly impacts the overall efficiency of the port. However, the current works for the reclaimer scheduling problem (RSP) overlook the necessity of maintenance activities and assume continuous machine availability. Nonetheless, the inclusion of preventive maintenance activities is critical to ensuring optimal scheduling decisions that minimize production disruptions and downtime. To address these challenges, this paper proposes a novel approach that considers periodic preventive maintenance activities of the reclaimers. We consider two cases to tackle this problem. The first case involves the optimization of material handling within a system of two stockpads and one reclaimer machine. For this case, two novel Mixed Integer programming (MIP) formulations are developed to drive efficient scheduling decisions and maximize productivity. Furthermore, for the second case representing a more complex system comprising three stockpads and two reclaimers, a novel MIP formulation is devised. The effectiveness of the proposed mathematical formulations is rigorously tested through the use of randomly generated instances based on a real-world case.
The problem of programming learning is a universal phenomenon, which poses enormous challenges in the initial phase of learning, with countless reports of difficulties and poor performance among students, which has of...
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Providing personalized assistance at scale is a long-standing challenge for computing educators, but a new generation of tools powered by large language models (LLMs) offers immense promise. Such tools can, in theory,...
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ISBN:
(纸本)9798400716195
Providing personalized assistance at scale is a long-standing challenge for computing educators, but a new generation of tools powered by large language models (LLMs) offers immense promise. Such tools can, in theory, provide on-demand help in large class settings and be configured with appropriate guardrails to prevent misuse and mitigate common concerns around learner over-reliance. However, the deployment of LLM-powered tools in authentic classroom settings is still rare, and very little is currently known about howstudents will use them in practice and what type of help they will seek. To address this, we examine students' use of an innovative LLMpowered tool that provides on-demand programming assistance without revealing solutions directly. We deployed the tool for 12 weeks in an introductory computer and data science course (.. = 52), collecting more than 2,500 queries submitted by students throughout the term. We manually categorized all student queries based on the type of assistance sought, and we automatically analyzed several additional query characteristics. We found that most queries requested immediate help with programming assignments, whereas fewer requests asked for help on related concepts or for deepening conceptual understanding. Furthermore, students often provided minimal information to the tool, suggesting this is an area in which targeted instruction would be beneficial. We also found that students who achieved more success in the course tended to have used the tool more frequently overall. Lessons from this research can be leveraged by programming educators and institutions who plan to augment their teaching with emerging LLM-powered tools.
the deepening of globalization and increasing demands for environmental sustainability, modern supply chains are faced with increasingly complex management challenges. To reduce management costs and enhance efficiency...
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the deepening of globalization and increasing demands for environmental sustainability, modern supply chains are faced with increasingly complex management challenges. To reduce management costs and enhance efficiency, an experimental approach is proposed based on a Mixed Integer programming Model, integrating heuristic algorithms with adaptive genetic algorithms. The objective is to improve both the efficiency and sustainability of supply chain management. Initially, the selection of suppliers within the supply chain is analyzed. Subsequently, heuristic algorithms and genetic algorithms are jointly employed to design, generate, and optimize initial solutions. Results indicate that during initial runs on training and validation sets, the fitness values of the research method reached as high as 99.67 and 96.77 at the 22nd and 68th iterations, respectively. Moreover, on the training set with a dataset size of 112, the accuracy of the research method was 98.56%, significantly outperforming other algorithms. With the system running five times, the time consumed for supplier selection and successful order allocation was merely 0.654s and 0.643s, respectively. In practical application analysis, when the system iterated 99 times, the research method incurred the minimum total cost of 962,700 yuan. These findings demonstrate that the research method effectively minimizes supply chain management costs while maximizing efficiency, offering practical strategies for optimizing and sustainably developing supply chain management.
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