Complementing Solutions to Optimization Problems via Crowdsourcing on Video Game Plays

Vargas-Santiago, Mariano; Monroy, Raúl; Ramírez-Márquez, José Emmanuel; Zhang, Chi; León-Velasco, D. Assaely; Zhu, Huaxing

doi:10.3390/app10238410

Cited by 4 publications

(5 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this context, the principal aim is to identify optimal facility locations that minimize costs while concurrently ensuring resilience and robustness in the face of uncertainties. [2], [4]. To address the challenge of participant motivation in crowdsourcing applications, various methodologies have been introduced.…”

Section: Related Work 1) Crowdsourcing Video Games For Different Comp...mentioning

confidence: 99%

“…In our prior scholarly contributions, we have explicated the design and utilization of a video game that serves as a computational model for the Facility Location Problem (FLP) [4], [29]. These articles have employed an abstract representation framework for depicting essential elements, including demand centers, facilities, associated weights, and assignments.…”

Section: Game Implementation Detailsmentioning

confidence: 99%

“…This platform served as the conduit through which participation was sought from a predetermined and quantifiable cohort, comprising 51 players, who engaged in the FLP-solving task via the medium of crowdsourcing. It is noteworthy that prior research endeavors have demonstrated the complementary nature of crowdsourcing when employed in conjunction with advanced techniques, such as genetic algorithms [2], [4].…”

Section: A Experimental Setupmentioning

confidence: 99%

“…The focus of our paper is on the development of effective strategies for solving Facility Location, a complex optimization problem with multiple objectives. In this problem, we are given a set of demand centers and must determine the optimal number of facilities to open to meet the overall demand while minimizing the total costs associated with facility opening and transportation [2], [4]. Specifically, we seek to improve existing algorithms for solving this problem by prioritizing the development of effective strategies.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Video Game-Crowdsourcing Approach to Discover a Player’s Strategy for Problem Solution to Facility Location

Vargas-Santiago,

León-Velasco,

Maldonado-Sifuentes

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

In recent times, there has been a growing interest in the domain of computationally challenging problem solving within both scientific and organizational contexts. This study is primarily concerned with the extraction and comprehension of the methodologies and strategies employed by individuals when confronted with intricate problems, specifically those falling under the purview of NP-hard problems. The Facility Location Problem (FLP) serves as a prominent exemplar within this study's framework. Traditionally, the handling of such complex problems has leaned upon intuitive reasoning and visual perception as the primary tools. However, these conventional approaches tend to provide only limited insight into the underlying processes employed in solving such problems. The present research seeks to bridge this knowledge gap through the utilization of advanced machine learning techniques for the purpose of categorizing and scrutinizing the strategies deployed by individuals in their attempts to tackle computationally challenging problems. The analysis conducted as part of this study unveils discernible and well-defined patterns and strategies that are employed by participants, some of whom have achieved notable levels of success. Remarkably, in certain instances, the outcomes achieved by these individuals have demonstrated a competitive edge when compared to the results produced by sophisticated computational methods, such as genetic algorithms. A fundamental component of our research methodology involves the application of heatmaps and clustering techniques. Through the normalization of results, our findings distinctly delineate two primary categories of games: those characterized by uniform player strategies and those characterized by a multitude of diverse and individualized tactics. Furthermore, our research employs a systematic approach to represent games by clustering them based on inherent similarities, utilizing cosine similarity as a metric for this purpose. By computing the averages of vectors within each cluster, we derive centroids that encapsulate the central tendencies exhibited by games belonging to that cluster. These centroids are then visually presented in a three-dimensional format, complemented by proportional spheres. These visual representations serve to vividly illustrate the dispersion and influence associated with each cluster. Our research significantly contributes to the understanding of human problem-solving strategies when confronted with computationally challenging problems. It unearths valuable insights regarding the potential for harnessing human intuition and expertise in addressing complex computational challenges. Through the integration of machine learning methodologies and intuitive visualizations, this work advances our comprehension of the approaches individuals employ to excel in solving computationally intricate problems.

show abstract

Section: Related Work 1) Crowdsourcing Video Games For Different Comp...mentioning

confidence: 99%

Section: Game Implementation Detailsmentioning

confidence: 99%

Section: A Experimental Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Video Game-Crowdsourcing Approach to Discover a Player’s Strategy for Problem Solution to Facility Location

Vargas-Santiago,

León-Velasco,

Maldonado-Sifuentes

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similarly, the authors of [35] developed a worker selection method with multiple objects including bug detection and cost minimization. Recently, the authors of [36] suggested a generic algorithm that uses video games as a way to gather novel solutions to optimization problems, and the authors of [37] introduced an expertise estimation as a meta-heuristic optimization harmony search problem.…”

Section: Related Workmentioning

confidence: 99%

Iterative Learning for K-Approval Votes in Crowdsourcing Systems

2021

View full text Add to dashboard Cite

Crowdsourcing systems have emerged as cornerstones to collect large amounts of qualified data in various human-powered problems with a relatively low budget. In eliciting the wisdom of crowds, many web-based crowdsourcing platforms have encouraged workers to select top-K alternatives rather than just one choice, which is called “K-approval voting”. This kind of setting has the advantage of inducing workers to make fewer mistakes when they respond to target tasks. However, there is not much work on inferring the correct answer from crowd-sourced data via a K-approval voting. In this paper, we propose a novel and efficient iterative algorithm to infer correct answers for a K-approval voting, which can be directly applied to real-world crowdsourcing systems. We analyze the average performance of our algorithm, and prove the theoretical error bound that decays exponentially in terms of the quality of workers and the number of queries. Through extensive experiments including the mixed case with various types of tasks, we show that our algorithm outperforms Expectation and Maximization (EM) and existing baseline algorithms.

show abstract