Some important issues in engineering the requirements of a distributed software system and methods that facilitate software system design for distributed or parallel implementations are discussed. The issues are presented from a knowledge engineering perspective and are divided into four levels: acquisition; representation; structuring; and design. The acquisition level entails the methods for eliciting system requirements data (attributes and relationships of software entities) from the end-user group using a model of context classes. The representation level deals with the language paradigm for representing the attributes and relationships of the software entities. The structuring level addresses methods for rearranging and grouping the software objects of the context classes into related clusters. The design level deals with methods for mapping or transforming the clusters of software objects into specification modules to facilitate distributed design. To this end, the design level uses an object-based paradigm for specifying the attributes and abstract behavior of the objects within the modules.<>
{"title":"A knowledge-driven methodology for eliciting and restructuring software requirements for distributed design","authors":"P. Bobbie, J. E. Urban","doi":"10.1109/TAI.1990.130404","DOIUrl":"https://doi.org/10.1109/TAI.1990.130404","url":null,"abstract":"Some important issues in engineering the requirements of a distributed software system and methods that facilitate software system design for distributed or parallel implementations are discussed. The issues are presented from a knowledge engineering perspective and are divided into four levels: acquisition; representation; structuring; and design. The acquisition level entails the methods for eliciting system requirements data (attributes and relationships of software entities) from the end-user group using a model of context classes. The representation level deals with the language paradigm for representing the attributes and relationships of the software entities. The structuring level addresses methods for rearranging and grouping the software objects of the context classes into related clusters. The design level deals with methods for mapping or transforming the clusters of software objects into specification modules to facilitate distributed design. To this end, the design level uses an object-based paradigm for specifying the attributes and abstract behavior of the objects within the modules.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122327579","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}
An agent-oriented programming metaphor is used to extend the analytic capabilities of a constraint logic programming system, such as CLP(R), to the domain of multi-agent constraint satisfaction problems. The resulting implementation provides a set of system primitives, which support at a rudimentary level, the maintenance of private knowledge bases, inter-agent communications, constraint driven multi-agent consensus formation, functional inheritance via 'cloning' and a choice of inheritance lattice search optimization mechanisms, allowing knowledge engineers to make speed vs. flexibility and functional dependence vs. independence trade-offs. A general architecture for agent-oriented programming systems is presented, and some of the more salient aspects of its CLP(R) implementation are summarized.<>
{"title":"A/sup 2/: an agent-oriented programming architecture for multi-agent constraint satisfaction problems","authors":"E. Freeman","doi":"10.1109/TAI.1990.130446","DOIUrl":"https://doi.org/10.1109/TAI.1990.130446","url":null,"abstract":"An agent-oriented programming metaphor is used to extend the analytic capabilities of a constraint logic programming system, such as CLP(R), to the domain of multi-agent constraint satisfaction problems. The resulting implementation provides a set of system primitives, which support at a rudimentary level, the maintenance of private knowledge bases, inter-agent communications, constraint driven multi-agent consensus formation, functional inheritance via 'cloning' and a choice of inheritance lattice search optimization mechanisms, allowing knowledge engineers to make speed vs. flexibility and functional dependence vs. independence trade-offs. A general architecture for agent-oriented programming systems is presented, and some of the more salient aspects of its CLP(R) implementation are summarized.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130559226","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 system of many-valued logical equations and its solving algorithm are presented. Based on this work, the authors generalize SLD resolution into many-valued logic and establish the corresponding truth-value calculus. As a result, M, an approximate reasoning system is constructed. Language and inference rules in M are presented. Inconsistencies of assignments and solving strategies are also analyzed in detail.<>
{"title":"M: An approximate reasoning system","authors":"Qinping Zhao, Bo Li","doi":"10.1109/TAI.1990.130337","DOIUrl":"https://doi.org/10.1109/TAI.1990.130337","url":null,"abstract":"A system of many-valued logical equations and its solving algorithm are presented. Based on this work, the authors generalize SLD resolution into many-valued logic and establish the corresponding truth-value calculus. As a result, M, an approximate reasoning system is constructed. Language and inference rules in M are presented. Inconsistencies of assignments and solving strategies are also analyzed in detail.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122321240","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 first results from the development of a genetic algorithm-based ACS are presented. The ACS is a result of mapping the inherent parallelism in classifier systems to a program which executes on a PC-based associative processor. The associative algorithms of the ACS for the coherent processor are presented. It is demonstrated that this associative implementation of the BOOLE classifier system learns as well as results published for serial implementations. It is shown that the use of an associative processor as a co-processor can decrease classifier system response time, particularly for classifier systems with a large number of rules. In fact, when the number of rules in the ACS was increased by an order of magnitude, the response time of the system increased only 25% after DOS overhead was removed.<>
{"title":"Implementation of a genetic algorithm based associative classifier system (ACS)","authors":"Kirk Twardowski","doi":"10.1109/TAI.1990.130308","DOIUrl":"https://doi.org/10.1109/TAI.1990.130308","url":null,"abstract":"The first results from the development of a genetic algorithm-based ACS are presented. The ACS is a result of mapping the inherent parallelism in classifier systems to a program which executes on a PC-based associative processor. The associative algorithms of the ACS for the coherent processor are presented. It is demonstrated that this associative implementation of the BOOLE classifier system learns as well as results published for serial implementations. It is shown that the use of an associative processor as a co-processor can decrease classifier system response time, particularly for classifier systems with a large number of rules. In fact, when the number of rules in the ACS was increased by an order of magnitude, the response time of the system increased only 25% after DOS overhead was removed.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127455655","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 model is presented which is designed to exploit the data parallelism present in associative computers for the efficient execution of logic programs with very large knowledge bases. A scheme is described for a logical data structure representation incorporating a direct interface between lists and vectors. This interface allows the partial integration of symbolic and numerical computation on existing associative supercomputers. A data parallel goal reduction algorithm which is almost independent of the number of clauses is discussed. This associative goal reduction scheme performs parallel clause pruning and binding of variables with a single occurrence. The associative property of the model effectively reduces the cost of shallow backtracking, deep backtracking, and garbage collection.<>
{"title":"Exploiting data parallelism for efficient execution of logic programs with large knowledge bases","authors":"A. Bansal, J. Potter","doi":"10.1109/TAI.1990.130419","DOIUrl":"https://doi.org/10.1109/TAI.1990.130419","url":null,"abstract":"A model is presented which is designed to exploit the data parallelism present in associative computers for the efficient execution of logic programs with very large knowledge bases. A scheme is described for a logical data structure representation incorporating a direct interface between lists and vectors. This interface allows the partial integration of symbolic and numerical computation on existing associative supercomputers. A data parallel goal reduction algorithm which is almost independent of the number of clauses is discussed. This associative goal reduction scheme performs parallel clause pruning and binding of variables with a single occurrence. The associative property of the model effectively reduces the cost of shallow backtracking, deep backtracking, and garbage collection.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126823365","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 importance of knowledge-based induction programs for problem solving is discussed. Desiderata for knowledge-based induction programs are given, and an example of such a program in the context learning classifications is discussed. The induction program RL4 is used as an induction tool, and several examples of its past and present uses are presented. The power of the tool comes from its flexibility and ease of use with a performance system. The use of RL4 with an inference engine that uses user-defined or default evidence gathering strategies is also discussed. Finally, the directions in which RL4 can go in the future are considered.<>
{"title":"RL4: a tool for knowledge-based induction","authors":"S. Clearwater, F. Provost","doi":"10.1109/TAI.1990.130305","DOIUrl":"https://doi.org/10.1109/TAI.1990.130305","url":null,"abstract":"The importance of knowledge-based induction programs for problem solving is discussed. Desiderata for knowledge-based induction programs are given, and an example of such a program in the context learning classifications is discussed. The induction program RL4 is used as an induction tool, and several examples of its past and present uses are presented. The power of the tool comes from its flexibility and ease of use with a performance system. The use of RL4 with an inference engine that uses user-defined or default evidence gathering strategies is also discussed. Finally, the directions in which RL4 can go in the future are considered.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115205380","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 learning approach that combines inductive learning with exemplar-based learning is described. In the method, a concept is represented by two parts: a generalized abstract description and a set of exemplars (exceptions). Generalized descriptions represent the principles of concepts, whereas exemplars represent the exceptional or rare cases. The method is an alternative for solving the problem of small disjuncts and for representing concepts with imprecise and irregular boundaries. The method for combining inductive learning and exemplar-based learning has been implemented in the flexible concept learning system. Experiments showed that the combined method has comparable performance to that of AQ16 and ASSISTANT in three natural domains.<>
{"title":"A method that combines inductive learning with exemplar-based learning","authors":"J. Zhang","doi":"10.1109/TAI.1990.130306","DOIUrl":"https://doi.org/10.1109/TAI.1990.130306","url":null,"abstract":"A learning approach that combines inductive learning with exemplar-based learning is described. In the method, a concept is represented by two parts: a generalized abstract description and a set of exemplars (exceptions). Generalized descriptions represent the principles of concepts, whereas exemplars represent the exceptional or rare cases. The method is an alternative for solving the problem of small disjuncts and for representing concepts with imprecise and irregular boundaries. The method for combining inductive learning and exemplar-based learning has been implemented in the flexible concept learning system. Experiments showed that the combined method has comparable performance to that of AQ16 and ASSISTANT in three natural domains.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128484613","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 authors present a graph model, P-graph, which supports the checking of knowledge bases for anomalies such as deadends, unreachability, cycles, inconsistency, redundancy, subsumption, and missing rules. P-graph captures the essential information needed for anomaly checks. The proposed approach differs from existing research as follows: it checks on groups of problem instances rather than on individual problem instances; it uses empirical knowledge to generate problem instances realizable in practice (only these problem instances need to be checked); and it considers the fact base as part of the knowledge base to be checked.<>
{"title":"P-graph-a graph model for anomaly checking of knowledge bases","authors":"Eng Lian Lim, J. McCallum, Kwok-Hung Chan","doi":"10.1109/TAI.1990.130452","DOIUrl":"https://doi.org/10.1109/TAI.1990.130452","url":null,"abstract":"The authors present a graph model, P-graph, which supports the checking of knowledge bases for anomalies such as deadends, unreachability, cycles, inconsistency, redundancy, subsumption, and missing rules. P-graph captures the essential information needed for anomaly checks. The proposed approach differs from existing research as follows: it checks on groups of problem instances rather than on individual problem instances; it uses empirical knowledge to generate problem instances realizable in practice (only these problem instances need to be checked); and it considers the fact base as part of the knowledge base to be checked.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122491494","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}
Research in cognitive psychology has suggested that images can be represented in terms of the spatial relationships of their meaningful parts. The author presents a formal scheme for knowledge representation based on a functional theory of arrays. Such a representation makes explicit the important features of an image by capturing both its spatial and hierarchical structure. The author also discusses the cognitive processes involved in mental imagery and how corresponding operations can be defined for the array representation.<>
{"title":"Artificial intelligence and imagery","authors":"J. Glasgow","doi":"10.1109/TAI.1990.130399","DOIUrl":"https://doi.org/10.1109/TAI.1990.130399","url":null,"abstract":"Research in cognitive psychology has suggested that images can be represented in terms of the spatial relationships of their meaningful parts. The author presents a formal scheme for knowledge representation based on a functional theory of arrays. Such a representation makes explicit the important features of an image by capturing both its spatial and hierarchical structure. The author also discusses the cognitive processes involved in mental imagery and how corresponding operations can be defined for the array representation.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122597808","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}
Two empirical studies and an analysis of natural dialogues between experts, novices and partial experts are given. From this analysis, a theory of explanation dialogues, called EST is developed. In EST, questions are interpreted by combining information from different, semantically related question types which together best capture the essence and meaning of the question. This theory is then applied to the design of an architecture and computational model of interpreting questions and generating explanations. The expert system, named EXPLAIN understands the nature of the question and is able to take account of the previous dialogue. Also, the system can tailor its responses to an individual user's characteristics, including level of expertise and depth of knowledge in the domain.<>
{"title":"Tailoring explanations to the user's level of expertise and domain knowledge","authors":"E. Sarantinos, P. Johnson","doi":"10.1109/TAI.1990.130330","DOIUrl":"https://doi.org/10.1109/TAI.1990.130330","url":null,"abstract":"Two empirical studies and an analysis of natural dialogues between experts, novices and partial experts are given. From this analysis, a theory of explanation dialogues, called EST is developed. In EST, questions are interpreted by combining information from different, semantically related question types which together best capture the essence and meaning of the question. This theory is then applied to the design of an architecture and computational model of interpreting questions and generating explanations. The expert system, named EXPLAIN understands the nature of the question and is able to take account of the previous dialogue. Also, the system can tailor its responses to an individual user's characteristics, including level of expertise and depth of knowledge in the domain.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1990-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116132452","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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