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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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 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 concept of anti-unification refers to the process of determining the most specific generalization (msg) of two or more input program objects. In the domain of logic programming, anti-unification has primarily been...
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ISBN:
(纸本)9783031457838;9783031457845
The concept of anti-unification refers to the process of determining the most specific generalization (msg) of two or more input program objects. In the domain of logic programming, anti-unification has primarily been investigated for computing msgs of tree-like program structures such as terms, atoms, and goals (the latter typically seen as ordered sequences). In this work, we study the anti-unification of whole predicate definitions. We provide a definition of a predicate generalization that allows to characterize the problem of finding the most specific generalization of two predicates as a (computationally hard) search problem. The complexity stems from the fact that a correspondence needs to be constructed between (1) some of the arguments of each of the predicates, (2) some of the clauses in each of the predicate's definitions, and (3) some of the body atoms in each pair of associated clauses. We propose a working algorithm that simultaneously computes these correspondences in a greedy manner. While our algorithm does not necessarily compute the most specific generalization, we conjecture that it allows to compute, in general, a sufficiently good generalization in an acceptable time.
computerprogramming courses often face the challenge of high dropout rates, prompting educators to seek effective solutions to improve student engagement and retention. Student Question-Generation (SQG) have been exp...
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ISBN:
(纸本)9786269689026
computerprogramming courses often face the challenge of high dropout rates, prompting educators to seek effective solutions to improve student engagement and retention. Student Question-Generation (SQG) have been explored as potential remedy to this issue, enabling advancements in computerscience education. However, the majority of existing SQG support systems lack personalized features to enhance student learning. In response to this gap, the study introduces a personal recommender system of SQG exercise tailored for programming courses. The system leverages individual student preferences and SQG exercise complexities to personalize learning experiences within SQG activities. This paper presents the design of the system and its user interface and delves into motivations and technical bases underlying its development.
The use of subgoal labels in introduction to programming classrooms has been shown to improve student performance, learning, retention, and reduce students' drop out rates. However, creating and adding subgoal lab...
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Research exploring the connection between students' learning and their psychological factors (e.g., emotions, attitudes, and beliefs) is often grounded in models and theories from literature related to psychology ...
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ISBN:
(纸本)9798400705311
Research exploring the connection between students' learning and their psychological factors (e.g., emotions, attitudes, and beliefs) is often grounded in models and theories from literature related to psychology and learning sciences. These theories provide insights into how psychological factors influence students' learning, motivation, and academic performance. To deepen our understanding of the interplay between these factors and students' learning and performance, this paper provides findings from a systematic literature review (SLR) of research studies about theories and methods used to understand the emotions and self-efficacy of undergraduate computing and engineering students. We examined thirty studies published between 2005 and 2023 in top-tier academic venues for computing and engineering education research. These studies leverage diverse methodologies, including validated surveys, physiological data, and grounded theory approaches, to explore the nuances of students' emotions and self-efficacy in computing and engineering education. We discuss how these factors are defined in the literature, the methods applied to measure and analyze them, and the implications for future research and educational practice. This SLR could assist computing and engineering education researchers in designing rigorous research studies focused on exploring these factors in students' learning. Furthermore, this may provide educators with a reference for devising effective teaching strategies to improve students' perceptions of computing, thereby enhancing their academic achievement.
TreeEvolver, a genetic programming algorithm, is used to make continuous mathematical functions that give rise to 3D landscapes. These are then empirically tested for hardness by a simple evolutionary algorithm, after...
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