Default logic is a prominent rigorous method of reasoning with incomplete information based on assumptions. It is a static reasoning approach, in the sense that it doesn't reason about changes and their consequences. On the other hand, its nonmonotonic behaviour appears when a change to a default theory is made. This paper studies the dynamic behaviour of default logic in the face of changes, a concept that we motivate by a reference to requirements engineering. The paper defines a contraction and a revision operator, and studies their properties. This work is part of an ongoing project whose aim is to build an integrated, domain-independent toolkit of logical methods for reasoning with changing and incomplete information. The techniques described in this paper will be implemented as part of the toolkit.
{"title":"Revising default theories","authors":"G. Antoniou, Mary-Anne Williams","doi":"10.1109/TAI.1998.744881","DOIUrl":"https://doi.org/10.1109/TAI.1998.744881","url":null,"abstract":"Default logic is a prominent rigorous method of reasoning with incomplete information based on assumptions. It is a static reasoning approach, in the sense that it doesn't reason about changes and their consequences. On the other hand, its nonmonotonic behaviour appears when a change to a default theory is made. This paper studies the dynamic behaviour of default logic in the face of changes, a concept that we motivate by a reference to requirements engineering. The paper defines a contraction and a revision operator, and studies their properties. This work is part of an ongoing project whose aim is to build an integrated, domain-independent toolkit of logical methods for reasoning with changing and incomplete information. The techniques described in this paper will be implemented as part of the toolkit.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"599 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":"123131944","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}
We investigate image segmentation by region merging. Given any similarity measure between regions, satisfying some weak constraints, we give a general predicate for answering if two regions are to be merged or not during the segmentation process. Our predicate is generic and has six properties. The first one is its independence with respect to the similarity measure, that leads to a user-independent and adaptative predicate. Second, it is non-parametric, and does not rely on any assumption concerning the image. Third, due to its weak constraints, knowledge may be included in the predicate to fit better to the user's behaviour. Fourth, provided the similarity is well chosen by the user, we are able to upperbound one type of error made during the image segmentation. Fifth, it does not rely on a particular segmentation algorithm and can be used with almost all region merging algorithms in various application domains. Sixth, it is calculated quickly, and can lead with appropriated algorithms to very efficient segmentation.
{"title":"Image segmentation using a generic, fast and non-parametric approach","authors":"C. Fiorio, R. Nock","doi":"10.1109/TAI.1998.744885","DOIUrl":"https://doi.org/10.1109/TAI.1998.744885","url":null,"abstract":"We investigate image segmentation by region merging. Given any similarity measure between regions, satisfying some weak constraints, we give a general predicate for answering if two regions are to be merged or not during the segmentation process. Our predicate is generic and has six properties. The first one is its independence with respect to the similarity measure, that leads to a user-independent and adaptative predicate. Second, it is non-parametric, and does not rely on any assumption concerning the image. Third, due to its weak constraints, knowledge may be included in the predicate to fit better to the user's behaviour. Fourth, provided the similarity is well chosen by the user, we are able to upperbound one type of error made during the image segmentation. Fifth, it does not rely on a particular segmentation algorithm and can be used with almost all region merging algorithms in various application domains. Sixth, it is calculated quickly, and can lead with appropriated algorithms to very efficient segmentation.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"7 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":"128243768","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}
Learning interface agents search regularities in the user behavior and use them to predict user's actions. We propose a new inductive concept learning approach, called IBHYS, to learn such regularities. This approach limits the hypothesis search to a small portion of the hypothesis space by letting each training example build a local approximation of the global target function. It allows to simultaneously search several hypothesis spaces and to simultaneously handle hypotheses described in different languages. This approach is particularly suited for learning interface agents because it provides an incremental algorithm with low training time and decision time, which does not require the designer of the interface agent to describe in advance and quite carefully the repetitive patterns searched. We illustrate our approach with two autonomous software agents, the Apprentice and the Assistant, devoted to assist users of interactive programming environments and implemented in Objectworks Smalltalk-80. The Apprentice learns user's work habits using an IBHYS algorithm and the Assistant, based on what has been learnt, proposes to the programmer sequences of actions the user might want to redo. We show, with experimental results on real data, that IBHYS outperforms ID3 both in computing time and predictive accuracy.
{"title":"IBHYS: a new approach to learn users habits","authors":"Jean-David Ruvini, C. Fagot","doi":"10.1109/TAI.1998.744843","DOIUrl":"https://doi.org/10.1109/TAI.1998.744843","url":null,"abstract":"Learning interface agents search regularities in the user behavior and use them to predict user's actions. We propose a new inductive concept learning approach, called IBHYS, to learn such regularities. This approach limits the hypothesis search to a small portion of the hypothesis space by letting each training example build a local approximation of the global target function. It allows to simultaneously search several hypothesis spaces and to simultaneously handle hypotheses described in different languages. This approach is particularly suited for learning interface agents because it provides an incremental algorithm with low training time and decision time, which does not require the designer of the interface agent to describe in advance and quite carefully the repetitive patterns searched. We illustrate our approach with two autonomous software agents, the Apprentice and the Assistant, devoted to assist users of interactive programming environments and implemented in Objectworks Smalltalk-80. The Apprentice learns user's work habits using an IBHYS algorithm and the Assistant, based on what has been learnt, proposes to the programmer sequences of actions the user might want to redo. We show, with experimental results on real data, that IBHYS outperforms ID3 both in computing time and predictive accuracy.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"5 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":"128977633","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}
We have proposed the discrete Lagrange-multiplier method (DLM) to solve satisfiability problems. Instead of restarting from a new starting point when the search reaches a local minimum in the objective space, the Lagrange multipliers of violated constraints in DLM provide a force to lead the search out of the local minimum and move it in a direction provided by the multipliers. We present the theoretical foundation of DLM for solving SAT problems and discuss some implementation issues. We study the performance of DLM on a set of hard satisfiability benchmark instances, and show the importance of dynamic scaling of Lagrange multipliers and the flat-move strategy. We show that DLM can perform better than competing local-search methods when its parameters are selected properly.
{"title":"Improving the performance of discrete Lagrange-multiplier search for solving hard SAT problems","authors":"Yi Shang, B. Wah","doi":"10.1109/TAI.1998.744839","DOIUrl":"https://doi.org/10.1109/TAI.1998.744839","url":null,"abstract":"We have proposed the discrete Lagrange-multiplier method (DLM) to solve satisfiability problems. Instead of restarting from a new starting point when the search reaches a local minimum in the objective space, the Lagrange multipliers of violated constraints in DLM provide a force to lead the search out of the local minimum and move it in a direction provided by the multipliers. We present the theoretical foundation of DLM for solving SAT problems and discuss some implementation issues. We study the performance of DLM on a set of hard satisfiability benchmark instances, and show the importance of dynamic scaling of Lagrange multipliers and the flat-move strategy. We show that DLM can perform better than competing local-search methods when its parameters are selected properly.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"167 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":"130983670","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}
Constraint satisfaction is well known to be applicable in modeling AI problems. Despite their extensive literature, the framework is sometimes inflexible and the results are not very satisfactory when applied to real-life problems. With the incorporation of the theory of fuzzy sets, fuzzy constraint satisfaction problems (FCSP's) have been exploited. FCSP's model real-life problems better by allowing both full and partial satisfaction of individual constraints. GENET, which has been shown to be efficient and effective in solving certain traditional CSPs, has been extended to handle FCSPs. Through transforming FCSPs into 0-1 integer programming problems, Wong and Leung (1998) displayed the equivalence between the underlying working mechanism of fuzzy GENET and the discrete Lagrangian method. We focus on the performance of fuzzy GENET in attacking large-scale and real-life over-constrained problems. An efficient simulator of fuzzy GENET for single-processor machines is implemented. Benchmarking results confirm its feasibility, flexibility, and superb efficiency in tackling both CSPs and FCSPs.
{"title":"Solving fuzzy constraint satisfaction problems with fuzzy GENET","authors":"Jason H. Y. Wong, Ho-fung Leung","doi":"10.1109/TAI.1998.744840","DOIUrl":"https://doi.org/10.1109/TAI.1998.744840","url":null,"abstract":"Constraint satisfaction is well known to be applicable in modeling AI problems. Despite their extensive literature, the framework is sometimes inflexible and the results are not very satisfactory when applied to real-life problems. With the incorporation of the theory of fuzzy sets, fuzzy constraint satisfaction problems (FCSP's) have been exploited. FCSP's model real-life problems better by allowing both full and partial satisfaction of individual constraints. GENET, which has been shown to be efficient and effective in solving certain traditional CSPs, has been extended to handle FCSPs. Through transforming FCSPs into 0-1 integer programming problems, Wong and Leung (1998) displayed the equivalence between the underlying working mechanism of fuzzy GENET and the discrete Lagrangian method. We focus on the performance of fuzzy GENET in attacking large-scale and real-life over-constrained problems. An efficient simulator of fuzzy GENET for single-processor machines is implemented. Benchmarking results confirm its feasibility, flexibility, and superb efficiency in tackling both CSPs and FCSPs.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"11 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":"125710017","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 a methodology for document processing and understanding. The methodology assumes that text paragraphs have been separated from the text images and that the necessary interrelationships are for a possible reconstruction of the original page. Each text line forms a natural language text which requires analysis in order to contribute to text understanding. The analysis is based on the formal representation and processing of natural language text by using stochastic Petri nets (SPNs). Text understanding is based on the semantic analysis/synthesis of SPN forms for the appropriate interpretations. These interpretations in combinations with image descriptions and/or objects extracted from images contribute to document understanding.
{"title":"An SPN based methodology for document understanding","authors":"N. Bourbakis, B. Manaris","doi":"10.1109/TAI.1998.744741","DOIUrl":"https://doi.org/10.1109/TAI.1998.744741","url":null,"abstract":"The paper presents a methodology for document processing and understanding. The methodology assumes that text paragraphs have been separated from the text images and that the necessary interrelationships are for a possible reconstruction of the original page. Each text line forms a natural language text which requires analysis in order to contribute to text understanding. The analysis is based on the formal representation and processing of natural language text by using stochastic Petri nets (SPNs). Text understanding is based on the semantic analysis/synthesis of SPN forms for the appropriate interpretations. These interpretations in combinations with image descriptions and/or objects extracted from images contribute to document understanding.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"1 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":"130568870","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}
Given a database D of three dimensional (3D) objects and a target object Q, the similarity search problem (also known as good-match retrieval) is defined as finding the objects D in D that approximately match Q, possibly in the presence of rotation, translation, node insert, delete and relabeling in D or Q. This type of query arises in many AI applications. We study the similarity search problem and a class of related queries. We present a computer vision based technique called geometric hashing for processing these queries. Experimental results on a database of 3D molecules obtained from the National Cancer Institute indicate the good performance of the presented technique.
{"title":"Fast similarity search in databases of 3D objects","authors":"Xiong Wang, J. Wang","doi":"10.1109/TAI.1998.744746","DOIUrl":"https://doi.org/10.1109/TAI.1998.744746","url":null,"abstract":"Given a database D of three dimensional (3D) objects and a target object Q, the similarity search problem (also known as good-match retrieval) is defined as finding the objects D in D that approximately match Q, possibly in the presence of rotation, translation, node insert, delete and relabeling in D or Q. This type of query arises in many AI applications. We study the similarity search problem and a class of related queries. We present a computer vision based technique called geometric hashing for processing these queries. Experimental results on a database of 3D molecules obtained from the National Cancer Institute indicate the good performance of the presented technique.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"1 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":"131379878","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}
We propose a GA based fuzzy knowledge integration framework that can simultaneously integrate multiple fuzzy rule sets and their membership function sets. The proposed approach includes fuzzy knowledge encoding and fuzzy knowledge integration. In the encoding phase, each fuzzy rule set with its associated membership functions is first transformed into an intermediary representation, and further encoded as a string. In the knowledge integration phase, a genetic algorithm is used to generate an optimal or nearly optimal set of fuzzy rules and membership functions from the initial knowledge population. The hepatitis diagnostic problem was used to show the performance of the proposed knowledge integration approach.
{"title":"Genetic-fuzzy knowledge-integration strategies","authors":"Ching-Hung Wang, T. Hong, S. Tseng","doi":"10.1109/TAI.1998.744851","DOIUrl":"https://doi.org/10.1109/TAI.1998.744851","url":null,"abstract":"We propose a GA based fuzzy knowledge integration framework that can simultaneously integrate multiple fuzzy rule sets and their membership function sets. The proposed approach includes fuzzy knowledge encoding and fuzzy knowledge integration. In the encoding phase, each fuzzy rule set with its associated membership functions is first transformed into an intermediary representation, and further encoded as a string. In the knowledge integration phase, a genetic algorithm is used to generate an optimal or nearly optimal set of fuzzy rules and membership functions from the initial knowledge population. The hepatitis diagnostic problem was used to show the performance of the proposed knowledge integration approach.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"5 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":"121875898","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}
Deriving inference rules from training examples is one of the most common machine-learning approaches. Fuzzy systems that can automatically derive fuzzy if-then rules and membership functions from numeric data have recently been developed. In this paper, we propose a new hierarchical representation for membership functions, and design a procedure to derive them. Experimental results on the Iris data show that our method can achieve high accuracy. The proposed method is thus useful in constructing membership functions and in managing uncertainty and vagueness.
{"title":"Building a hierarchical representation of membership functions","authors":"T. Hong, Jyh-Bin Chen","doi":"10.1109/TAI.1998.744849","DOIUrl":"https://doi.org/10.1109/TAI.1998.744849","url":null,"abstract":"Deriving inference rules from training examples is one of the most common machine-learning approaches. Fuzzy systems that can automatically derive fuzzy if-then rules and membership functions from numeric data have recently been developed. In this paper, we propose a new hierarchical representation for membership functions, and design a procedure to derive them. Experimental results on the Iris data show that our method can achieve high accuracy. The proposed method is thus useful in constructing membership functions and in managing uncertainty and vagueness.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"22 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":"134280302","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}
Knowledge-Based Neural Network with Trapezoidal Fuzzy Set (KBNN/TFS) is a fuzzy neural network model, which handles trapezoidal fuzzy inputs with the abilities of fuzzy rule revision, verification and generation. Based on KBNN/TFS, an efficiency validation method is proposed to evaluate the rule inference complexity on KBNN/TFS. Besides, three methods that simplify the structure of this fuzzy rule-based neural network model are provided to enhance the inference efficiency. Fuzzy tabulation method, the first method, is performed to do rule combination by modeling the antecedents of some specific rules and then to eliminate the don't care variables in the rules. The second method, named transitive fuzzy rule compacting method, combines the rules with the transitive relations to decrease the computational load of inference. The third method, called identical antecedent unifying method, simplifies the redundant antecedents of rules by replacing the identical antecedents of the rules with a single specific antecedent. By these methods, the structure of rules can be simplified without changing the results of its inference. The proposed efficiency validation method is used to analyze and support the results of performing these three efficiency enhancing methods. Also the simulation results show that the efficiency is enhanced after performing these three efficiency enhancing methods.
{"title":"Efficiency validation of fuzzy domain theories using a neural network model","authors":"Hahn-Ming Lee, Jyh-Ming Chen, E. Chang","doi":"10.1109/TAI.1998.744778","DOIUrl":"https://doi.org/10.1109/TAI.1998.744778","url":null,"abstract":"Knowledge-Based Neural Network with Trapezoidal Fuzzy Set (KBNN/TFS) is a fuzzy neural network model, which handles trapezoidal fuzzy inputs with the abilities of fuzzy rule revision, verification and generation. Based on KBNN/TFS, an efficiency validation method is proposed to evaluate the rule inference complexity on KBNN/TFS. Besides, three methods that simplify the structure of this fuzzy rule-based neural network model are provided to enhance the inference efficiency. Fuzzy tabulation method, the first method, is performed to do rule combination by modeling the antecedents of some specific rules and then to eliminate the don't care variables in the rules. The second method, named transitive fuzzy rule compacting method, combines the rules with the transitive relations to decrease the computational load of inference. The third method, called identical antecedent unifying method, simplifies the redundant antecedents of rules by replacing the identical antecedents of the rules with a single specific antecedent. By these methods, the structure of rules can be simplified without changing the results of its inference. The proposed efficiency validation method is used to analyze and support the results of performing these three efficiency enhancing methods. Also the simulation results show that the efficiency is enhanced after performing these three efficiency enhancing methods.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"4 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":"134322832","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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