We propose the multi-state commitment (MSC) method to speed-up heuristic search algorithms for semi-optimal solutions. The real-time A* (RTA*) and the weighted A* (WA*) are representative heuristic search algorithms for semi-optimal solutions and can be viewed as single-state and an all-state commitment search algorithms respectively. In these algorithms, there is a tradeoff between the risk of making wrong choices in search process and the amount of memory for the recovery, with RTA* and WA* being the extremes. The MSC method introduces a moderate and flexible characteristic into these algorithms and can increase the performance dramatically in problems such as the N-puzzle. In this paper, by introducing a commitment-list, we show a modification of RTA* and WA* to their MSC versions without violating their completeness. Then, we experiment with their performance in maze and N-puzzle problems, and discuss conditions that the MSC method is effective.
{"title":"Multi-state commitment search","authors":"Y. Kitamura, M. Yokoo, T. Miyaji, S. Tatsumi","doi":"10.1109/TAI.1998.744882","DOIUrl":"https://doi.org/10.1109/TAI.1998.744882","url":null,"abstract":"We propose the multi-state commitment (MSC) method to speed-up heuristic search algorithms for semi-optimal solutions. The real-time A* (RTA*) and the weighted A* (WA*) are representative heuristic search algorithms for semi-optimal solutions and can be viewed as single-state and an all-state commitment search algorithms respectively. In these algorithms, there is a tradeoff between the risk of making wrong choices in search process and the amount of memory for the recovery, with RTA* and WA* being the extremes. The MSC method introduces a moderate and flexible characteristic into these algorithms and can increase the performance dramatically in problems such as the N-puzzle. In this paper, by introducing a commitment-list, we show a modification of RTA* and WA* to their MSC versions without violating their completeness. Then, we experiment with their performance in maze and N-puzzle problems, and discuss conditions that the MSC method is effective.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122553987","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}
N. Bourbakis, W. Meng, Zonghuan Wu, J. Salerno, S. Borek
This paper describes a methodology for removing (partially or totally) redundant information received from different documents in an effort to synthesize new documents. In particular, information retrieved from different databases may have various forms, such as images, natural language text, data, etc. These pieces of information may be parts of one or more documents related with a specific subject. This means that a number of text-paragraphs and images may occur (or retrieved) more than once, by creating redundancy in the storage space. Thus, in order to create a new redundant-less document the duplicated parts of information have to be removed. The methodology presented analyzes text-paragraphs and images received from different DBs by using a set of similarity criteria in order to make a decision for the removal of the duplicated ones. Illustrative examples are provided.
{"title":"Removal of redundancy in documents retrieved from different resources","authors":"N. Bourbakis, W. Meng, Zonghuan Wu, J. Salerno, S. Borek","doi":"10.1109/TAI.1998.744799","DOIUrl":"https://doi.org/10.1109/TAI.1998.744799","url":null,"abstract":"This paper describes a methodology for removing (partially or totally) redundant information received from different documents in an effort to synthesize new documents. In particular, information retrieved from different databases may have various forms, such as images, natural language text, data, etc. These pieces of information may be parts of one or more documents related with a specific subject. This means that a number of text-paragraphs and images may occur (or retrieved) more than once, by creating redundancy in the storage space. Thus, in order to create a new redundant-less document the duplicated parts of information have to be removed. The methodology presented analyzes text-paragraphs and images received from different DBs by using a set of similarity criteria in order to make a decision for the removal of the duplicated ones. Illustrative examples are provided.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116671831","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}
Techniques for constructing classifier committees including boosting and bagging have demonstrated great success, especially boosting for decision tree learning. This type of technique generates several classifiers to form a committee by repeated application of a single base learning algorithm. The committee members vote to decide the final classification. Boosting and bagging create different classifiers by modifying the distribution of the training set. SASC (Stochastic Attribute Selection Committees) uses an alternative approach to generating classifier committees by stochastic manipulation of the set of attributes considered at each node during tree induction, but keeping the distribution of the training set unchanged. We propose a method for improving the performance of boosting. This technique combines boosting and SASC. It builds classifier committees by manipulating both the distribution of the training set and the set of attributes available during induction. In the synergy SASC effectively increases the model diversity of boosting. Experiments with a representative collection of natural domains show that, on average, the combined technique outperforms either boosting or SASC alone in terms of reducing the error rate of decision tree learning.
{"title":"Integrating boosting and stochastic attribute selection committees for further improving the performance of decision tree learning","authors":"Zijian Zheng, Geoffrey I. Webb, K. Ting","doi":"10.1109/TAI.1998.744846","DOIUrl":"https://doi.org/10.1109/TAI.1998.744846","url":null,"abstract":"Techniques for constructing classifier committees including boosting and bagging have demonstrated great success, especially boosting for decision tree learning. This type of technique generates several classifiers to form a committee by repeated application of a single base learning algorithm. The committee members vote to decide the final classification. Boosting and bagging create different classifiers by modifying the distribution of the training set. SASC (Stochastic Attribute Selection Committees) uses an alternative approach to generating classifier committees by stochastic manipulation of the set of attributes considered at each node during tree induction, but keeping the distribution of the training set unchanged. We propose a method for improving the performance of boosting. This technique combines boosting and SASC. It builds classifier committees by manipulating both the distribution of the training set and the set of attributes available during induction. In the synergy SASC effectively increases the model diversity of boosting. Experiments with a representative collection of natural domains show that, on average, the combined technique outperforms either boosting or SASC alone in terms of reducing the error rate of decision tree learning.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125790672","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}
For mobile robot navigation, controlling the components such as sensors and motors is not enough. Besides controlling such components, a navigation control system needs to provide decisions in choosing a path to go and carrying out requests from the user. There are many tasks which need to be performed concurrently. Intelligent agents are suitable for being responsible for carrying out each task and performing cooperation between the agents. The paper describes an architecture which uses agents in a cooperative environment.
{"title":"A multi-agent architecture for mobile robot navigation control","authors":"Jia-Houng Shyu, Alan Liu, Kao-Shing Hwang","doi":"10.1109/TAI.1998.744761","DOIUrl":"https://doi.org/10.1109/TAI.1998.744761","url":null,"abstract":"For mobile robot navigation, controlling the components such as sensors and motors is not enough. Besides controlling such components, a navigation control system needs to provide decisions in choosing a path to go and carrying out requests from the user. There are many tasks which need to be performed concurrently. Intelligent agents are suitable for being responsible for carrying out each task and performing cooperation between the agents. The paper describes an architecture which uses agents in a cooperative environment.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132745382","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}
Selective induction algorithms are efficient in learning target concepts but inherit a major limitation each time only one feature is used to partition the data until the data is divided into uniform segments. This limitation results in problems like replication, repetition, and fragmentation. Constructive induction has been an effective means to overcome some of the problems. The underlying idea is to construct compound features that increase the representation power so as to enhance the learning algorithm's capability in partitioning data. Unfortunately, many constructive operators are often manually designed and choosing which one to apply poses a serious problem itself. We propose an automatic way of constructing compound features. The method can be applied to both continuous and discrete data and thus all the three problems can be eliminated or alleviated. Our empirical results indicate the effectiveness of the proposed method.
{"title":"Fragmentation problem and automated feature construction","authors":"R. Setiono, Huan Liu","doi":"10.1109/TAI.1998.744845","DOIUrl":"https://doi.org/10.1109/TAI.1998.744845","url":null,"abstract":"Selective induction algorithms are efficient in learning target concepts but inherit a major limitation each time only one feature is used to partition the data until the data is divided into uniform segments. This limitation results in problems like replication, repetition, and fragmentation. Constructive induction has been an effective means to overcome some of the problems. The underlying idea is to construct compound features that increase the representation power so as to enhance the learning algorithm's capability in partitioning data. Unfortunately, many constructive operators are often manually designed and choosing which one to apply poses a serious problem itself. We propose an automatic way of constructing compound features. The method can be applied to both continuous and discrete data and thus all the three problems can be eliminated or alleviated. Our empirical results indicate the effectiveness of the proposed method.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115356812","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}
A neural fuzzy system controlling home appliances is proposed. The central goal of home automation is to provide an efficient and convenient integration and inter-operation among appliances in households. The necessary software tools should present a comfortable user interface. One suitable programming method for home automation systems is the definition of linguistic rules that can be processed by a fuzzy system. In our approach it is assumed that the home system adapts itself to the occupants' lifestyle. Based on this idea, we present an appropriate neuro fuzzy controller. An implementation of this artificial intelligent based controller under the MATLAB/SIMULINK development environment is shown. It consists of functions that upgrade MATLAB/SIMULINK to a tool with hardware and Internet access. This tool is not only restricted to home automation, it can also be applied to control non time-critical processes.
{"title":"An artificial intelligence based tool for home automation using MATLAB","authors":"Harald J. Zainzinger","doi":"10.1109/TAI.1998.744852","DOIUrl":"https://doi.org/10.1109/TAI.1998.744852","url":null,"abstract":"A neural fuzzy system controlling home appliances is proposed. The central goal of home automation is to provide an efficient and convenient integration and inter-operation among appliances in households. The necessary software tools should present a comfortable user interface. One suitable programming method for home automation systems is the definition of linguistic rules that can be processed by a fuzzy system. In our approach it is assumed that the home system adapts itself to the occupants' lifestyle. Based on this idea, we present an appropriate neuro fuzzy controller. An implementation of this artificial intelligent based controller under the MATLAB/SIMULINK development environment is shown. It consists of functions that upgrade MATLAB/SIMULINK to a tool with hardware and Internet access. This tool is not only restricted to home automation, it can also be applied to control non time-critical processes.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115458249","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}
The operation of hot strip mill rolling scheduling (HSMRS) at China Steel Corporation (CSC), Taiwan is an extremely difficult and time consuming process due to the complexity of the problem. The paper explores how this problem can be solved through the use of a genetic algorithm. One of the key aspects of this approach is the use of specially designed representations for such scheduling problems. The representations explicitly encode a schedule by encoding information for building cycles. We have found that this representation cooperates with a stochastic violation directed mutation operator and suitable fitness function and can quickly produce results comparable to human scheduling. The efficient and flexible GA approach presented is potentially widely useful in other similar rolling cycle scheduling applications in large steel companies.
{"title":"A genetic algorithm approach to hot strip mill rolling scheduling problems","authors":"H. Fang, C. Tsai","doi":"10.1109/TAI.1998.744853","DOIUrl":"https://doi.org/10.1109/TAI.1998.744853","url":null,"abstract":"The operation of hot strip mill rolling scheduling (HSMRS) at China Steel Corporation (CSC), Taiwan is an extremely difficult and time consuming process due to the complexity of the problem. The paper explores how this problem can be solved through the use of a genetic algorithm. One of the key aspects of this approach is the use of specially designed representations for such scheduling problems. The representations explicitly encode a schedule by encoding information for building cycles. We have found that this representation cooperates with a stochastic violation directed mutation operator and suitable fitness function and can quickly produce results comparable to human scheduling. The efficient and flexible GA approach presented is potentially widely useful in other similar rolling cycle scheduling applications in large steel companies.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132370111","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}
The paper presents the design and implementation of PHYSIMC, which supports case based learning of elementary physics in a computer assisted simulation environment. The PHYSIMC system facilitates physics problem solving by providing: 1) a user friendly interface for problem specification via direct manipulation of physical objects; 2) 2D motion simulation of primitive physical objects; 3) a case library of successful problem solving episodes; and 4) a browsing tool for relevant problems and their corresponding solutions. As a result, a student can make use of past problem solving experiences in attempting to solve a new problem. In a knowledge based simulation environment, such case based learning tools help narrow the gaps due to incomplete domain knowledge.
{"title":"PHYSIMC: an intelligent assistant for case-based learning","authors":"Jane Yung-jen Hsu, C. Ting","doi":"10.1109/TAI.1998.744857","DOIUrl":"https://doi.org/10.1109/TAI.1998.744857","url":null,"abstract":"The paper presents the design and implementation of PHYSIMC, which supports case based learning of elementary physics in a computer assisted simulation environment. The PHYSIMC system facilitates physics problem solving by providing: 1) a user friendly interface for problem specification via direct manipulation of physical objects; 2) 2D motion simulation of primitive physical objects; 3) a case library of successful problem solving episodes; and 4) a browsing tool for relevant problems and their corresponding solutions. As a result, a student can make use of past problem solving experiences in attempting to solve a new problem. In a knowledge based simulation environment, such case based learning tools help narrow the gaps due to incomplete domain knowledge.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121382471","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}
The expressiveness of conceptual graphs allows for accurate representation of knowledge in the domain of information retrieval. However, the complexity of the operators introduced by this formalism are in contradiction with the necessary speed of a practicable information retrieval system. In particular, the projection operator is considered to be time-consuming. We propose a new logic-based organization of conceptual graphs. Considering conceptual graphs from a logical point of view leads to a form of conceptual graphs that we call standard logical form. In this case, the complexity of our retrieval algorithm is polynomial. Retrieval exploits the logic-based organization, and the iterative application of the projection operator in each graph of the collection is changed by a single application of an algorithm to the whole collection. The algorithm is presented in detail and its complexity is studied. Experimentation on a collection of 650 images, by using an already existent conceptual graph platform, shows net improvement in retrieval time performance.
{"title":"Organizing conceptual graphs for fast knowledge retrieval","authors":"I. Ounis","doi":"10.1109/TAI.1998.744801","DOIUrl":"https://doi.org/10.1109/TAI.1998.744801","url":null,"abstract":"The expressiveness of conceptual graphs allows for accurate representation of knowledge in the domain of information retrieval. However, the complexity of the operators introduced by this formalism are in contradiction with the necessary speed of a practicable information retrieval system. In particular, the projection operator is considered to be time-consuming. We propose a new logic-based organization of conceptual graphs. Considering conceptual graphs from a logical point of view leads to a form of conceptual graphs that we call standard logical form. In this case, the complexity of our retrieval algorithm is polynomial. Retrieval exploits the logic-based organization, and the iterative application of the projection operator in each graph of the collection is changed by a single application of an algorithm to the whole collection. The algorithm is presented in detail and its complexity is studied. Experimentation on a collection of 650 images, by using an already existent conceptual graph platform, shows net improvement in retrieval time performance.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123507627","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}
The Processor Configuration Problem (PCP) is an NP hard real life problem. The goal involves designing a network of a finite set of processors, such that the maximum distance between any two processors that a parcel of data needs to travel is kept to a minimum. Since each processor has a limited number of communication channels, a carefully designed layout would assist in reducing the overhead for message switching in the entire network. The Guided Genetic Algorithm (GGA) is a hybrid of genetic algorithm and meta heuristic search algorithm: Guided Local Search. As the search progresses, GGA modifies both the fitness function and fitness templates of the candidate solutions based on feedback from the constraints. We are interested in generating processor configurations between eight and 128 processors. GGA is used as a tool to generate these configurations, and is shown to have considerable advantages over published results.
{"title":"Solving large processor configuration problems with the guided genetic algorithm","authors":"T. Lau, E. Tsang","doi":"10.1109/TAI.1998.744860","DOIUrl":"https://doi.org/10.1109/TAI.1998.744860","url":null,"abstract":"The Processor Configuration Problem (PCP) is an NP hard real life problem. The goal involves designing a network of a finite set of processors, such that the maximum distance between any two processors that a parcel of data needs to travel is kept to a minimum. Since each processor has a limited number of communication channels, a carefully designed layout would assist in reducing the overhead for message switching in the entire network. The Guided Genetic Algorithm (GGA) is a hybrid of genetic algorithm and meta heuristic search algorithm: Guided Local Search. As the search progresses, GGA modifies both the fitness function and fitness templates of the candidate solutions based on feedback from the constraints. We are interested in generating processor configurations between eight and 128 processors. GGA is used as a tool to generate these configurations, and is shown to have considerable advantages over published results.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1998-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129248212","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：