Pub Date : 2001-10-30DOI: 10.1109/ICCIMA.2001.970459
T. Takayanagi, A. Ohuchi
Human immunodeficiency virus (HIV) infection and acquired immunodeficiency syndrome (AIDS) have been very important problems all over the world. The progression of HIV infection into AIDS is controlled with anti-HIV drugs, but the treatments with the anti-HIV drugs have some problems. Hence, the research and development of more effective anti-HIV treatments have been performed. As we consider it important to understand the dynamics of HIV infection, we propose a new mathematical model of HIV infection. The model is characterized by the calculations of responses against stimuli; that is, the experimental phenomena (when the values of responses are plotted against the logarithm of the values of stimuli, a sigmoid curve is obtained) are incorporated into the model. By using the calculations of the model, we obtain the simulation results which show a slow increase in the viral load and a slow decrease in non-infected CD4/sup +/ T cells after the acute phase.
人类免疫缺陷病毒（HIV）感染和获得性免疫缺陷综合征（AIDS）一直是全世界非常重要的问题。抗艾滋病毒药物可以控制艾滋病毒感染向艾滋病的发展，但抗艾滋病毒药物的治疗存在一些问题。因此，人们开始研究和开发更有效的抗艾滋病毒疗法。我们认为了解艾滋病病毒感染的动态变化非常重要，因此我们提出了一个新的艾滋病病毒感染数学模型。该模型的特点是计算反应与刺激的关系，即把实验现象（当反应值与刺激值的对数作图时，得到一条sigmoid曲线）纳入模型。通过使用该模型的计算，我们得到的模拟结果显示，在急性期之后，病毒载量缓慢增加，未感染的 CD4/sup +/ T 细胞缓慢减少。
{"title":"Simulations of human immunodeficiency virus infection","authors":"T. Takayanagi, A. Ohuchi","doi":"10.1109/ICCIMA.2001.970459","DOIUrl":"https://doi.org/10.1109/ICCIMA.2001.970459","url":null,"abstract":"Human immunodeficiency virus (HIV) infection and acquired immunodeficiency syndrome (AIDS) have been very important problems all over the world. The progression of HIV infection into AIDS is controlled with anti-HIV drugs, but the treatments with the anti-HIV drugs have some problems. Hence, the research and development of more effective anti-HIV treatments have been performed. As we consider it important to understand the dynamics of HIV infection, we propose a new mathematical model of HIV infection. The model is characterized by the calculations of responses against stimuli; that is, the experimental phenomena (when the values of responses are plotted against the logarithm of the values of stimuli, a sigmoid curve is obtained) are incorporated into the model. By using the calculations of the model, we obtain the simulation results which show a slow increase in the viral load and a slow decrease in non-infected CD4/sup +/ T cells after the acute phase.","PeriodicalId":232504,"journal":{"name":"Proceedings Fourth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2001","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131525365","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 : 2001-10-30DOI: 10.1109/ICCIMA.2001.970474
S. Johansson
We introduce the notion of Characteristic Distributions which is a way of representing information about the payoffs of different behaviors in a Multi-agent System. We discuss how they can be used to simplify and structure the analysis of strategies and prove i) the existence of optimal environments, given a certain behavior, and ii) that all behaviors payoff equally, when taken over all possible environments (no free lunch theorem for strategies).
{"title":"No free lunches in multi-agent systems,-a characteristic distribution approach to game theoretic modelling","authors":"S. Johansson","doi":"10.1109/ICCIMA.2001.970474","DOIUrl":"https://doi.org/10.1109/ICCIMA.2001.970474","url":null,"abstract":"We introduce the notion of Characteristic Distributions which is a way of representing information about the payoffs of different behaviors in a Multi-agent System. We discuss how they can be used to simplify and structure the analysis of strategies and prove i) the existence of optimal environments, given a certain behavior, and ii) that all behaviors payoff equally, when taken over all possible environments (no free lunch theorem for strategies).","PeriodicalId":232504,"journal":{"name":"Proceedings Fourth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2001","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131103878","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 : 2001-10-30DOI: 10.1109/ICCIMA.2001.970478
M. Valena, Teresa B Ludermir
This paper presents a constructive neural network model for seasonal streamflow forecasting. This surface water hydrology is basic to the design and operation of the reservoir. A good example is the operation of a reservoir with an uncontrolled inflow but having a means of regulating the outflow. If information on the nature of the inflow is determinable in advance, then the reservoir can be operated by some decision rule to minimize downstream flood damage. For this reasons, several companies in the Brazilian Electrical Sector use the linear time-series models such as PARMA (Periodic Auto regressive Moving Average) models developed by Box-Jenkins. This paper provides for river flow prediction a numerical comparison between neural networks, called nonlinear sigmoidal regression blocks networks (NSRBN) and PARMA models. The model was implemented to forecast weekly average inflow on an step-ahead basis. It was tested on four hydroelectric plants located in different river basins in Brazil. The results obtained in the evaluation of the performance of NSRBN were better than the results obtained with PARMA models.
{"title":"Constructive neural networks in forecasting weekly river flow","authors":"M. Valena, Teresa B Ludermir","doi":"10.1109/ICCIMA.2001.970478","DOIUrl":"https://doi.org/10.1109/ICCIMA.2001.970478","url":null,"abstract":"This paper presents a constructive neural network model for seasonal streamflow forecasting. This surface water hydrology is basic to the design and operation of the reservoir. A good example is the operation of a reservoir with an uncontrolled inflow but having a means of regulating the outflow. If information on the nature of the inflow is determinable in advance, then the reservoir can be operated by some decision rule to minimize downstream flood damage. For this reasons, several companies in the Brazilian Electrical Sector use the linear time-series models such as PARMA (Periodic Auto regressive Moving Average) models developed by Box-Jenkins. This paper provides for river flow prediction a numerical comparison between neural networks, called nonlinear sigmoidal regression blocks networks (NSRBN) and PARMA models. The model was implemented to forecast weekly average inflow on an step-ahead basis. It was tested on four hydroelectric plants located in different river basins in Brazil. The results obtained in the evaluation of the performance of NSRBN were better than the results obtained with PARMA models.","PeriodicalId":232504,"journal":{"name":"Proceedings Fourth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2001","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122887431","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 : 2001-10-30DOI: 10.1109/ICCIMA.2001.970460
N. Toma, S. Endo, Koji Yamada, H. Miyagi
The purposes of the paper are to propose and evaluate an immune optimization algorithm inspired by biological immune cell-cooperation, and this algorithm solves the division-of-labor problems in a multi-agent system (MAS). The proposed algorithm solves the problem through interactions between agents, and between agents and the environment. The interactions are performed by division-and-integration processing, inspired by immune cell-cooperation and a similar co-evolutionary approach. The division-and-integration processing optimizes the work domain, and the similar co-evolutionary approach performs equal divisions. To investigate the validity, this algorithm is applied to "N-th agent's Travelling Salesmen Problem" as a typical problem of MAS. The best property for solving via MAS is clarified with some simulations.
{"title":"An immune optimization inspired by biological immune cell-cooperation for division-and-labor problem","authors":"N. Toma, S. Endo, Koji Yamada, H. Miyagi","doi":"10.1109/ICCIMA.2001.970460","DOIUrl":"https://doi.org/10.1109/ICCIMA.2001.970460","url":null,"abstract":"The purposes of the paper are to propose and evaluate an immune optimization algorithm inspired by biological immune cell-cooperation, and this algorithm solves the division-of-labor problems in a multi-agent system (MAS). The proposed algorithm solves the problem through interactions between agents, and between agents and the environment. The interactions are performed by division-and-integration processing, inspired by immune cell-cooperation and a similar co-evolutionary approach. The division-and-integration processing optimizes the work domain, and the similar co-evolutionary approach performs equal divisions. To investigate the validity, this algorithm is applied to \"N-th agent's Travelling Salesmen Problem\" as a typical problem of MAS. The best property for solving via MAS is clarified with some simulations.","PeriodicalId":232504,"journal":{"name":"Proceedings Fourth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2001","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125603899","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 : 2001-01-01DOI: 10.1109/ICCIMA.2001.970491
Jong-Ha Lee, Hee-Su Kim, Sung-Bae Cho
In usual evolutionary computation (EC) is not effective for local search, but efficient for global search due to its probabilistic operators. This problem becomes worse in the interactive EC (IEC) applications, which have the generation length limitation caused by user evaluation. To solve that, this paper proposes direct manipulation (DM) method, well known in HCI field, of evolution for IEC. It allows the user to manipulate individuals directly, instead of using evolutionary operators as an interface to each individual. Through this approach, the DM overcomes the shortcoming of EC, letting alone the ability of global search to the original operators. We have applied the DM concept to the fashion design system based on IEC, and shown that the application is promising with two experiments.
{"title":"Accelerating evolution by direct manipulation for interactive fashion design","authors":"Jong-Ha Lee, Hee-Su Kim, Sung-Bae Cho","doi":"10.1109/ICCIMA.2001.970491","DOIUrl":"https://doi.org/10.1109/ICCIMA.2001.970491","url":null,"abstract":"In usual evolutionary computation (EC) is not effective for local search, but efficient for global search due to its probabilistic operators. This problem becomes worse in the interactive EC (IEC) applications, which have the generation length limitation caused by user evaluation. To solve that, this paper proposes direct manipulation (DM) method, well known in HCI field, of evolution for IEC. It allows the user to manipulate individuals directly, instead of using evolutionary operators as an interface to each individual. Through this approach, the DM overcomes the shortcoming of EC, letting alone the ability of global search to the original operators. We have applied the DM concept to the fashion design system based on IEC, and shown that the application is promising with two experiments.","PeriodicalId":232504,"journal":{"name":"Proceedings Fourth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2001","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121161882","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 : 1900-01-01DOI: 10.1109/ICCIMA.2001.970506
K. Miyazaki, S. Kobayashi
Reinforcement learning is a kind of machine learning. It aims to adapt an agent to an unknown environment according to rewards. Traditionally, from theoretical point of view, many reinforcement learning systems assume that the environment has Markovian properties. However it is important to treat non-Markovian environments in multi-agent reinforcement learning systems. In this paper, we use Profit Sharing (PS) as a reinforcement learning system and discuss the rationality of PS in multi-agent environments. Especially, we classify non-Markovian environments and discuss how to share a reward among reinforcement learning agents. Through cranes control problem, we confirm the effectiveness of PS in multi-agent environments.
{"title":"On the rationality of profit sharing in multi-agent reinforcement learning","authors":"K. Miyazaki, S. Kobayashi","doi":"10.1109/ICCIMA.2001.970506","DOIUrl":"https://doi.org/10.1109/ICCIMA.2001.970506","url":null,"abstract":"Reinforcement learning is a kind of machine learning. It aims to adapt an agent to an unknown environment according to rewards. Traditionally, from theoretical point of view, many reinforcement learning systems assume that the environment has Markovian properties. However it is important to treat non-Markovian environments in multi-agent reinforcement learning systems. In this paper, we use Profit Sharing (PS) as a reinforcement learning system and discuss the rationality of PS in multi-agent environments. Especially, we classify non-Markovian environments and discuss how to share a reward among reinforcement learning agents. Through cranes control problem, we confirm the effectiveness of PS in multi-agent environments.","PeriodicalId":232504,"journal":{"name":"Proceedings Fourth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2001","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123331921","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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