Pub Date : 2021-12-05DOI: 10.1109/SSCI50451.2021.9660157
Rachel Brown, D. Ashlock
Real parameter estimation is typically performed by an algorithm that operates directly on vectors of real parameters. This study presents an extension of a representation for real parameter optimization that is discrete and based on the iterated partition of simplices, known as the Walking Triangle Representation (WTR), and pairs it with Monte Carlo Tree Search (MCTS)-like algorithms. The number of moves allowed to the WTR is reduced to only its centering move, where a vertex of the simplex is replaced by its center of mass. This representation converts a real parameter optimization to a discrete form, which can then be paired with MCTS-like algorithms. The tree structure of MCTS allows one to keep track of and exploit information from previous attempts (tree extensions) when choosing the next set of moves to try. Six real parameter optimization problems were used to test the algorithm. Four parameters in the algorithm were studied, including: minimum gene length, maximum gene length, number of tree extensions, and probability of exploration (chance). The algorithm regularly performed consistently well, even with a low number of fitness evaluations (typical number of fitness evaluations is up to 3750 per run). This paper focuses on the ability of the Non-Walking Triangle Representation to convert real parameter optimization problems into discrete representations. This concept is demonstrated through the evaluation of the Non-Walking Triangle Monte Carlo Tree Search (MCNon-Walk) algorithm's ability to find optima in a variety of real parameter optimization problems, using differential evolution as a baseline for comparison.
{"title":"The Non-Walking Triangle Optimization Representation: Enabling Monte Carlo Tree Search-like Methods for Real Parameter Optimization Problems","authors":"Rachel Brown, D. Ashlock","doi":"10.1109/SSCI50451.2021.9660157","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660157","url":null,"abstract":"Real parameter estimation is typically performed by an algorithm that operates directly on vectors of real parameters. This study presents an extension of a representation for real parameter optimization that is discrete and based on the iterated partition of simplices, known as the Walking Triangle Representation (WTR), and pairs it with Monte Carlo Tree Search (MCTS)-like algorithms. The number of moves allowed to the WTR is reduced to only its centering move, where a vertex of the simplex is replaced by its center of mass. This representation converts a real parameter optimization to a discrete form, which can then be paired with MCTS-like algorithms. The tree structure of MCTS allows one to keep track of and exploit information from previous attempts (tree extensions) when choosing the next set of moves to try. Six real parameter optimization problems were used to test the algorithm. Four parameters in the algorithm were studied, including: minimum gene length, maximum gene length, number of tree extensions, and probability of exploration (chance). The algorithm regularly performed consistently well, even with a low number of fitness evaluations (typical number of fitness evaluations is up to 3750 per run). This paper focuses on the ability of the Non-Walking Triangle Representation to convert real parameter optimization problems into discrete representations. This concept is demonstrated through the evaluation of the Non-Walking Triangle Monte Carlo Tree Search (MCNon-Walk) algorithm's ability to find optima in a variety of real parameter optimization problems, using differential evolution as a baseline for comparison.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123279277","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 : 2021-12-05DOI: 10.1109/SSCI50451.2021.9660016
Omar Qasem, Weinan Gao, T. Bian
In this paper, we propose a novel dynamic programming (DP) algorithm, under the name of hybrid iteration (HI), for continuous-time linear systems. The proposed HI approach combines the advantages of two well-known DP algorithms, i.e., policy iteration (PI) and value iteration (VI). In particular, HI drops the need of an initial stabilizing control policy required in PI, and at the same time it maintains a faster convergence rate compared with VI. Based on the proposed HI algorithm, a data-driven adaptive optimal controller design is also proposed. Simulation results for randomly generated continuous-time linear systems with different system orders demonstrate that the proposed HI approach can save CPU time up to 73% and reduce the number of iterations to converge up to 98% comparing with the VI approach.
{"title":"Adaptive Optimal Control of Continuous-Time Linear Systems via Hybrid Iteration","authors":"Omar Qasem, Weinan Gao, T. Bian","doi":"10.1109/SSCI50451.2021.9660016","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660016","url":null,"abstract":"In this paper, we propose a novel dynamic programming (DP) algorithm, under the name of hybrid iteration (HI), for continuous-time linear systems. The proposed HI approach combines the advantages of two well-known DP algorithms, i.e., policy iteration (PI) and value iteration (VI). In particular, HI drops the need of an initial stabilizing control policy required in PI, and at the same time it maintains a faster convergence rate compared with VI. Based on the proposed HI algorithm, a data-driven adaptive optimal controller design is also proposed. Simulation results for randomly generated continuous-time linear systems with different system orders demonstrate that the proposed HI approach can save CPU time up to 73% and reduce the number of iterations to converge up to 98% comparing with the VI approach.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123263639","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 : 2021-12-05DOI: 10.1109/SSCI50451.2021.9660163
J. Gallagher, Monica Sam
Small Flapping-Wing Micro-Air Vehicles (FW-MA Vs) may experience wing damage and wear while in service with even small amounts introducing significant deficits in maintaining path control. Previous work employed a custom Evolutionary Algorithm (EA) that adapted wing motion patterns, while in flight and in normal online service, to compensate for wing damage. Although generally successful in finding solutions to this challenging online non-stationary problem, the previous methods would very often require hours of flight time to reach full success and sometime failed altogether in cases of extreme wing damage. This paper details a new approach that reduces the required learning time by an order of magnitude and extends the range of damage over which one can expect suitable performance. A discussion of what changes were made and why they were made will be provided along with extensive simulation results demonstrating the claims of success. The paper will also provide discussion of what additional work is possible now that both speed and efficacy have been sufficiently improved to support practical in-flight learning in real vehicles.
{"title":"Improvements to Speed and Efficacy in Non-Stationary Learning in a Flapping-Wing Air Vehicle: Constrained and Unconstrained Flight","authors":"J. Gallagher, Monica Sam","doi":"10.1109/SSCI50451.2021.9660163","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660163","url":null,"abstract":"Small Flapping-Wing Micro-Air Vehicles (FW-MA Vs) may experience wing damage and wear while in service with even small amounts introducing significant deficits in maintaining path control. Previous work employed a custom Evolutionary Algorithm (EA) that adapted wing motion patterns, while in flight and in normal online service, to compensate for wing damage. Although generally successful in finding solutions to this challenging online non-stationary problem, the previous methods would very often require hours of flight time to reach full success and sometime failed altogether in cases of extreme wing damage. This paper details a new approach that reduces the required learning time by an order of magnitude and extends the range of damage over which one can expect suitable performance. A discussion of what changes were made and why they were made will be provided along with extensive simulation results demonstrating the claims of success. The paper will also provide discussion of what additional work is possible now that both speed and efficacy have been sufficiently improved to support practical in-flight learning in real vehicles.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123650366","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 : 2021-12-05DOI: 10.1109/SSCI50451.2021.9660097
Cheng Gong, Lie Meng Pang, H. Ishibuchi
A good initial population generation method is of necessity to improve the performance of evolutionary multiobjective optimization (EMO) algorithms. However, until now only a few methods for generating an initial population have been proposed for EMO algorithms. In this paper, we propose a simple idea of generating an initial population for a popular decomposition-based algorithm, i.e., MOEA/D with the penalty-based boundary intersection (PBI) function, and demonstrate its effectiveness. The basic idea is to generate more initial solutions than the population size and to assign an appropriate solution to each weight vector. Firstly, we modify the initialization phase of MOEA/D through two different strategies based on this idea. Then, the modified MOEA/D algorithms are compared with the original MOEA/D on frequently-used many-objective test problems: DTLZ1, DTLZ3 and DTLZ4. Our experimental results clearly show that the proposed initial population generation method can significantly improve the performance of the original MOEA/D.
{"title":"Initial Population Generation Method and its Effects on MOEA/D","authors":"Cheng Gong, Lie Meng Pang, H. Ishibuchi","doi":"10.1109/SSCI50451.2021.9660097","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660097","url":null,"abstract":"A good initial population generation method is of necessity to improve the performance of evolutionary multiobjective optimization (EMO) algorithms. However, until now only a few methods for generating an initial population have been proposed for EMO algorithms. In this paper, we propose a simple idea of generating an initial population for a popular decomposition-based algorithm, i.e., MOEA/D with the penalty-based boundary intersection (PBI) function, and demonstrate its effectiveness. The basic idea is to generate more initial solutions than the population size and to assign an appropriate solution to each weight vector. Firstly, we modify the initialization phase of MOEA/D through two different strategies based on this idea. Then, the modified MOEA/D algorithms are compared with the original MOEA/D on frequently-used many-objective test problems: DTLZ1, DTLZ3 and DTLZ4. Our experimental results clearly show that the proposed initial population generation method can significantly improve the performance of the original MOEA/D.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125518876","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 : 2021-12-05DOI: 10.1109/SSCI50451.2021.9660140
H. C. R. Oliveira, V. Shmerko, S. Yanushkevich
This paper focuses on designing a CI decision support to address rare events such as disease outbreaks in a ‘closed’ environment such as a cruise ship. We focus on a case study of the COVID-19 outbreak that happened on board the Diamond Princess cruise ship in 2020. It considers a graphical probabilistic model such as Bayesian Network. We consider this causal model to be a core of an intelligent decision support tool to help in emergency management. To prove this hypothesis, the prototype of a decision support tool was implemented and used to evaluate different scenarios. The results show that such system equipped with a reasoning engine is capable of evaluating the pandemic scenario risks, thus helping assess the impacts of certain preventive measures, and damages.
{"title":"Decision Support for Infection Outbreak Analysis: the case of the Diamond Princess cruise ship","authors":"H. C. R. Oliveira, V. Shmerko, S. Yanushkevich","doi":"10.1109/SSCI50451.2021.9660140","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660140","url":null,"abstract":"This paper focuses on designing a CI decision support to address rare events such as disease outbreaks in a ‘closed’ environment such as a cruise ship. We focus on a case study of the COVID-19 outbreak that happened on board the Diamond Princess cruise ship in 2020. It considers a graphical probabilistic model such as Bayesian Network. We consider this causal model to be a core of an intelligent decision support tool to help in emergency management. To prove this hypothesis, the prototype of a decision support tool was implemented and used to evaluate different scenarios. The results show that such system equipped with a reasoning engine is capable of evaluating the pandemic scenario risks, thus helping assess the impacts of certain preventive measures, and damages.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114615739","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 : 2021-12-05DOI: 10.1109/SSCI50451.2021.9660186
Mustafa Atay, Hailey Gipson, Tony Gwyn, K. Roy
The prevalent commercial deployment of automated facial analysis systems such as face recognition as a robust authentication method has increasingly fueled scientific attention. Current machine learning algorithms allow for a relatively reliable detection, recognition, and categorization of face images comprised of age, race, and gender. Algorithms with such biased data are bound to produce skewed results. It leads to a significant decrease in the performance of state-of-the-art models when applied to images of gender or ethnicity groups. In this paper, we study the gender bias in facial recognition with gender balanced and imbalanced training sets using five traditional machine learning algorithms. We aim to report the machine learning classifiers which are inclined towards gender bias and the ones which mitigate it. Miss rates metric is effective in finding out potential bias in predictions. Our study utilizes miss rates metric along with a standard metric such as accuracy, precision or recall to evaluate possible gender bias effectively.
{"title":"Evaluation of Gender Bias in Facial Recognition with Traditional Machine Learning Algorithms","authors":"Mustafa Atay, Hailey Gipson, Tony Gwyn, K. Roy","doi":"10.1109/SSCI50451.2021.9660186","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9660186","url":null,"abstract":"The prevalent commercial deployment of automated facial analysis systems such as face recognition as a robust authentication method has increasingly fueled scientific attention. Current machine learning algorithms allow for a relatively reliable detection, recognition, and categorization of face images comprised of age, race, and gender. Algorithms with such biased data are bound to produce skewed results. It leads to a significant decrease in the performance of state-of-the-art models when applied to images of gender or ethnicity groups. In this paper, we study the gender bias in facial recognition with gender balanced and imbalanced training sets using five traditional machine learning algorithms. We aim to report the machine learning classifiers which are inclined towards gender bias and the ones which mitigate it. Miss rates metric is effective in finding out potential bias in predictions. Our study utilizes miss rates metric along with a standard metric such as accuracy, precision or recall to evaluate possible gender bias effectively.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122285661","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 : 2021-12-05DOI: 10.1109/SSCI50451.2021.9659838
Somayeh Bakhtiari Ramezani, Brad Killen, Logan Cummins, S. Rahimi, A. Amirlatifi, Maria Seale
Early detection of faulty patterns and timely scheduling of maintenance events can minimize risk to the underlying processes and increase the system's lifespan, reliability, and availability. Different techniques are used in the literature to determine the health state of the system, one of which is the Hidden Markov Models (HMMs). This class of algorithms is very well suited for modeling the health condition dictated by the latent states of the system. HMMs can reveal transitions from one state to another, thus highlighting degradation in a system's health and the right time for maintenance. While many extensions and variations of the HMM are studied for a variety of applications, the present study aims to evaluate and compare the state-of-the-art HMM-based research in predictive maintenance only. This study also aims to discuss the capabilities and limitations of such algorithms and future directions to tackle the current limitations.
{"title":"A Survey of HMM-based Algorithms in Machinery Fault Prediction","authors":"Somayeh Bakhtiari Ramezani, Brad Killen, Logan Cummins, S. Rahimi, A. Amirlatifi, Maria Seale","doi":"10.1109/SSCI50451.2021.9659838","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659838","url":null,"abstract":"Early detection of faulty patterns and timely scheduling of maintenance events can minimize risk to the underlying processes and increase the system's lifespan, reliability, and availability. Different techniques are used in the literature to determine the health state of the system, one of which is the Hidden Markov Models (HMMs). This class of algorithms is very well suited for modeling the health condition dictated by the latent states of the system. HMMs can reveal transitions from one state to another, thus highlighting degradation in a system's health and the right time for maintenance. While many extensions and variations of the HMM are studied for a variety of applications, the present study aims to evaluate and compare the state-of-the-art HMM-based research in predictive maintenance only. This study also aims to discuss the capabilities and limitations of such algorithms and future directions to tackle the current limitations.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127999600","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 : 2021-12-05DOI: 10.1109/SSCI50451.2021.9659932
Campbell Gorman, Yu-kai Wang
Both Augmented Reality (AR) and Brain-Computer Interfaces (BCI) have drawn a lot of attention in recent applications. These two new technologies will significantly impact and develop interactions between human and intelligent agents. While there are several studies already conducted in the control of devices using AR based, steady state visually evoked potentials (SSVEP) control systems in a lab environment, this study seeks to implement a portable, closed-loop, AR-based BCI to assess the feasibility of controlling a physical device through SSVEP. This portable, closed-loop AR-based BCI provides users with the unique opportunity to simultaneously interact with the surrounding environment and control autonomous agents with an 88% accuracy. The potential benefits of this application include reduced restrictions on handicapped individuals or concurrent control of multiple devices through a single AR interface. Ultimately, we hope this outcome can bridge the BCI field with further real-world, practical applications.
{"title":"A Closed-Loop AR-based BCI for Real-World System Control","authors":"Campbell Gorman, Yu-kai Wang","doi":"10.1109/SSCI50451.2021.9659932","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659932","url":null,"abstract":"Both Augmented Reality (AR) and Brain-Computer Interfaces (BCI) have drawn a lot of attention in recent applications. These two new technologies will significantly impact and develop interactions between human and intelligent agents. While there are several studies already conducted in the control of devices using AR based, steady state visually evoked potentials (SSVEP) control systems in a lab environment, this study seeks to implement a portable, closed-loop, AR-based BCI to assess the feasibility of controlling a physical device through SSVEP. This portable, closed-loop AR-based BCI provides users with the unique opportunity to simultaneously interact with the surrounding environment and control autonomous agents with an 88% accuracy. The potential benefits of this application include reduced restrictions on handicapped individuals or concurrent control of multiple devices through a single AR interface. Ultimately, we hope this outcome can bridge the BCI field with further real-world, practical applications.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130349926","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 : 2021-12-05DOI: 10.1109/SSCI50451.2021.9659928
Shirsha Bose, Sayantani Ghosh, A. Konar, A. Nagar
The paper introduces an innovative methodology for the automatic discrimination of multiple choice answers chosen by merit and random guess by analyzing the confidence level of examinees using an Electroencephalographic system. The acquired brain signals of the subjects participating in the experiment are first examined using the eLORETA software which portrays the active participation of the middle frontal gyrus and precuneus when a subject is fully confident regarding the choice of the correct answer. In the next phase, the signals are pre-processed and converted to spectrogram plots using Short Time Fourier Transform (STFT) which reveal the enhanced activation of theta and lower alpha bands when a subject attempts an answer with his/her merit. On the other hand, the afore-said frequency bands portray reduced activation when a subject tries to choose an answer by a mere guess. The acquired spectrogram plots are transferred to a novel Capsule network model that aids in categorizing the two degrees of confidence level: High and Low. The novelty in the design of the Capsule based classifier lies in the introduction of a depthwise separable convolution layer, a squeeze and excitation attention mechanism and a Sigmoid-Weighted Linear Unit (SiLU) based dynamic routing algorithm. The proposed classifier demonstrates promising results in categorizing the two classes of confidence level and also outperforms its conventional counterparts. Thus, the proposed scheme can be utilized to improve the quality of assessment in multiple choice based examinations.
{"title":"Decoding the Confidence Level of Subjects in Answering Multiple Choice Questions Using EEG Induced Capsule Network","authors":"Shirsha Bose, Sayantani Ghosh, A. Konar, A. Nagar","doi":"10.1109/SSCI50451.2021.9659928","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659928","url":null,"abstract":"The paper introduces an innovative methodology for the automatic discrimination of multiple choice answers chosen by merit and random guess by analyzing the confidence level of examinees using an Electroencephalographic system. The acquired brain signals of the subjects participating in the experiment are first examined using the eLORETA software which portrays the active participation of the middle frontal gyrus and precuneus when a subject is fully confident regarding the choice of the correct answer. In the next phase, the signals are pre-processed and converted to spectrogram plots using Short Time Fourier Transform (STFT) which reveal the enhanced activation of theta and lower alpha bands when a subject attempts an answer with his/her merit. On the other hand, the afore-said frequency bands portray reduced activation when a subject tries to choose an answer by a mere guess. The acquired spectrogram plots are transferred to a novel Capsule network model that aids in categorizing the two degrees of confidence level: High and Low. The novelty in the design of the Capsule based classifier lies in the introduction of a depthwise separable convolution layer, a squeeze and excitation attention mechanism and a Sigmoid-Weighted Linear Unit (SiLU) based dynamic routing algorithm. The proposed classifier demonstrates promising results in categorizing the two classes of confidence level and also outperforms its conventional counterparts. Thus, the proposed scheme can be utilized to improve the quality of assessment in multiple choice based examinations.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130421846","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 : 2021-12-05DOI: 10.1109/SSCI50451.2021.9659551
Kaito Kimura, Yuan Tu, Riku Tanji, M. Mozgovoy
Interaction with opponents is a core element in video sports games. Thus, user experience in single-player matches heavily depends on the quality of AI opponents, who are expected to vary in their skill level and play styles. One way to achieve this goal is to learn game-playing behavior from real human players and to improve it if necessary with an automated optimization method. Monte-Carlo tree search (MCTS) has been successfully used for this purpose in several card and board games, such as chess and poker. We explore the possibility to apply MCTS in an action sports game of 3D tennis, and show how a dataset of pre-recorded tennis games can be used to train an MCTS-based AI system, exhibiting believable and reasonably skillful behavior.
{"title":"Creating Adjustable Human-like AI Behavior in a 3D Tennis Game with Monte-Carlo Tree Search","authors":"Kaito Kimura, Yuan Tu, Riku Tanji, M. Mozgovoy","doi":"10.1109/SSCI50451.2021.9659551","DOIUrl":"https://doi.org/10.1109/SSCI50451.2021.9659551","url":null,"abstract":"Interaction with opponents is a core element in video sports games. Thus, user experience in single-player matches heavily depends on the quality of AI opponents, who are expected to vary in their skill level and play styles. One way to achieve this goal is to learn game-playing behavior from real human players and to improve it if necessary with an automated optimization method. Monte-Carlo tree search (MCTS) has been successfully used for this purpose in several card and board games, such as chess and poker. We explore the possibility to apply MCTS in an action sports game of 3D tennis, and show how a dataset of pre-recorded tennis games can be used to train an MCTS-based AI system, exhibiting believable and reasonably skillful behavior.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129557147","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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