The architecture of a meta-blackboard shell is described, which can be specialized as specific blackboard shells to fit specific domains. The knowledge source structure of the shell is designed as a miniature blackboard architecture to increase the local inference capability through the generalization of knowledge representation. The control component is designed as a restricted blackboard architecture to propose a generalized focus-of-attention control strategy that searches for a set of best-matched pairs of knowledge sources and solution areas as the next focus under dynamic constraints. Various problem-solving paradigms are integrated through planning and constraint blackboards. Several high-order mechanisms, such as viewpoint-based reasoning are supported. The blackboard structuring system is designed to support both homogeneous and heterogeneous decompositions of a problem-solving space.<>
{"title":"Development of a meta-blackboard shell","authors":"Cheng-Seen Ho","doi":"10.1109/TAI.1990.130395","DOIUrl":"https://doi.org/10.1109/TAI.1990.130395","url":null,"abstract":"The architecture of a meta-blackboard shell is described, which can be specialized as specific blackboard shells to fit specific domains. The knowledge source structure of the shell is designed as a miniature blackboard architecture to increase the local inference capability through the generalization of knowledge representation. The control component is designed as a restricted blackboard architecture to propose a generalized focus-of-attention control strategy that searches for a set of best-matched pairs of knowledge sources and solution areas as the next focus under dynamic constraints. Various problem-solving paradigms are integrated through planning and constraint blackboards. Several high-order mechanisms, such as viewpoint-based reasoning are supported. The blackboard structuring system is designed to support both homogeneous and heterogeneous decompositions of a problem-solving space.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"55 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":"116637999","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}
Summary form only given. The state-of-the-art of artificial intelligence (AI) in Japan is reviewed, and attention given to the current results of the fifth-generation project. The technical aspects of AI research are examined, and the computational power of the fifth-generation computer system compared to that of the current workstation in the use of various AI frameworks. The Japanese rationale for adopting the new technology into their own industrial domains and businesses is discussed. The psychological aspects of AI applications are considered, with particular attention given to the psychological barrier that nontechnical people have in the use of AI.<>
{"title":"The state-of-art AI research in Japan","authors":"F. Mizoguchi","doi":"10.1109/TAI.1990.130397","DOIUrl":"https://doi.org/10.1109/TAI.1990.130397","url":null,"abstract":"Summary form only given. The state-of-the-art of artificial intelligence (AI) in Japan is reviewed, and attention given to the current results of the fifth-generation project. The technical aspects of AI research are examined, and the computational power of the fifth-generation computer system compared to that of the current workstation in the use of various AI frameworks. The Japanese rationale for adopting the new technology into their own industrial domains and businesses is discussed. The psychological aspects of AI applications are considered, with particular attention given to the psychological barrier that nontechnical people have in the use of AI.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"73 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":"127276199","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 novel and effective graphical approach to visually programming rules based on constraints is given. It is shown how constraints can be visualized for rule programming applications and mechanically converted to and from operational tests. User experiments are described that establish the intuitiveness of the pictorial constraint-based rule encodings over a conventional textual language such as OPS-5. A modular rule programming environment architecture is presented that uses an intermediate constraint representation. A working color computer implementation of the visual interface and programming environment is described, and its use is illustrated.<>
{"title":"A graphical constraint-based production system environment","authors":"M. Perlin, Peter Gaertner","doi":"10.1109/TAI.1990.130331","DOIUrl":"https://doi.org/10.1109/TAI.1990.130331","url":null,"abstract":"A novel and effective graphical approach to visually programming rules based on constraints is given. It is shown how constraints can be visualized for rule programming applications and mechanically converted to and from operational tests. User experiments are described that establish the intuitiveness of the pictorial constraint-based rule encodings over a conventional textual language such as OPS-5. A modular rule programming environment architecture is presented that uses an intermediate constraint representation. A working color computer implementation of the visual interface and programming environment is described, and its use is illustrated.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"37 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":"124908057","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}
Based on the Dempster-Shafer (D-S) theory of evidence and G. Yen's (1989), extension of the theory, the authors propose approaches to representing heuristic knowledge by evidential mapping and pooling the mass distribution in a complex frame by partitioning that frame using Shafter's partition technique. The authors have generalized Yen's model from Bayesian probability theory to the D-S theory of evidence. Based on such a generalized model, an extended framework for evidential reasoning systems is briefly specified in which a semi-graph method is used to describe the heuristic knowledge. The advantage of such a method is that it can avoid the complexity of graphs without losing the explicitness of graphs. The extended framework can be widely used to build expert systems.<>
{"title":"An extended framework for evidential reasoning systems","authors":"Weiru Liu, Jun-Hyeok Hong, M. McTear, J. Hughes","doi":"10.1109/TAI.1990.130429","DOIUrl":"https://doi.org/10.1109/TAI.1990.130429","url":null,"abstract":"Based on the Dempster-Shafer (D-S) theory of evidence and G. Yen's (1989), extension of the theory, the authors propose approaches to representing heuristic knowledge by evidential mapping and pooling the mass distribution in a complex frame by partitioning that frame using Shafter's partition technique. The authors have generalized Yen's model from Bayesian probability theory to the D-S theory of evidence. Based on such a generalized model, an extended framework for evidential reasoning systems is briefly specified in which a semi-graph method is used to describe the heuristic knowledge. The advantage of such a method is that it can avoid the complexity of graphs without losing the explicitness of graphs. The extended framework can be widely used to build expert systems.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"30 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":"124920560","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}
MACS is a research and development project of the European ESPRIT II program, whose objective is the definition and implementation of a software maintenance assistance system. The basis premise of MACS is to do maintenance through understanding. MACS views the application from several perspectives in specific 'worlds'. One of them is the 'reasoning world', which captures by means of a simple formalism, the problem-solution-decision formalism, the rationale underlying the design and maintenance process. MACS data integration is provided by a central base called the interconnection world. Moreover, MACS proposes to guide the maintainer in applying a strategy and in fully exploiting the possibilities of the MACS tools themselves by means of artificial-intelligence techniques and knowledge-based support.<>
{"title":"Capturing design and maintenance decisions with MACS","authors":"C. Desclaux","doi":"10.1109/TAI.1990.130325","DOIUrl":"https://doi.org/10.1109/TAI.1990.130325","url":null,"abstract":"MACS is a research and development project of the European ESPRIT II program, whose objective is the definition and implementation of a software maintenance assistance system. The basis premise of MACS is to do maintenance through understanding. MACS views the application from several perspectives in specific 'worlds'. One of them is the 'reasoning world', which captures by means of a simple formalism, the problem-solution-decision formalism, the rationale underlying the design and maintenance process. MACS data integration is provided by a central base called the interconnection world. Moreover, MACS proposes to guide the maintainer in applying a strategy and in fully exploiting the possibilities of the MACS tools themselves by means of artificial-intelligence techniques and knowledge-based support.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"64 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":"125263515","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 consider the application of a genetic algorithm (GA) to a symbolic learning task namely, supervised concept learning from examples. A GA concept learner, GABL, that learns a concept from a set of positive and negative examples is implemented. GABL is run in a batch-incremental mode to facilitate comparison with an incremental concept learner, ID5R. Preliminary results show that, despite minimal system bias, GABL is an effective concept learner and is quite competitive with ID5R as the target concept increases in complexity.<>
{"title":"Using genetic algorithms for supervised concept learning","authors":"W. Spears, K. A. Jong","doi":"10.1109/TAI.1990.130359","DOIUrl":"https://doi.org/10.1109/TAI.1990.130359","url":null,"abstract":"The authors consider the application of a genetic algorithm (GA) to a symbolic learning task namely, supervised concept learning from examples. A GA concept learner, GABL, that learns a concept from a set of positive and negative examples is implemented. GABL is run in a batch-incremental mode to facilitate comparison with an incremental concept learner, ID5R. Preliminary results show that, despite minimal system bias, GABL is an effective concept learner and is quite competitive with ID5R as the target concept increases in complexity.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"5 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":"122311435","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}
Nonmonotonic logical systems are logics in which the introduction of new axioms can invalidate old theorems. For practical applications, the authors restrict themselves to nonmonotonic systems that consist of facts and rules, and call them nonmonotonic knowledge-based systems (KBSs). Rules in a nonmonotonic KBS can be either default rules or regular rules. The syntax and semantics of these two types of rules are given. In contrast to other approaches, such as circumscription, default reasoning, assumption-based truth maintenance, and multi-valued logics, the authors directly extend the monotonic first order logic and its semantics. By using this method, one can define the validation problems for nonmonotonic KBSs similar to those for monotonic KBSs. Therefore, the validation tool already developed for monotonic KBSs can be extended to nonmonotonic KBSs.<>
{"title":"Validation of nonmonotonic knowledge-based systems","authors":"C. Chang, R. Stachowitz, J. Combs","doi":"10.1109/TAI.1990.130437","DOIUrl":"https://doi.org/10.1109/TAI.1990.130437","url":null,"abstract":"Nonmonotonic logical systems are logics in which the introduction of new axioms can invalidate old theorems. For practical applications, the authors restrict themselves to nonmonotonic systems that consist of facts and rules, and call them nonmonotonic knowledge-based systems (KBSs). Rules in a nonmonotonic KBS can be either default rules or regular rules. The syntax and semantics of these two types of rules are given. In contrast to other approaches, such as circumscription, default reasoning, assumption-based truth maintenance, and multi-valued logics, the authors directly extend the monotonic first order logic and its semantics. By using this method, one can define the validation problems for nonmonotonic KBSs similar to those for monotonic KBSs. Therefore, the validation tool already developed for monotonic KBSs can be extended to nonmonotonic KBSs.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"5 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":"114268317","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}
Most approaches to model-based diagnosis use incomplete constraint satisfaction techniques and are therefore incomplete. It is shown here how diagnostics may be considered as a general constraint satisfaction problem. Provided the system to be repaired is modeled with piecewise linear constraints on real variables, it is possible to give a clear characterization of the conflict set. Furthermore, a complete algorithm to find every conflict is described. This algorithm is an original and elegant solution to the diagnostic problem. Moreover, it can be used in order to build a compiled form of the initial model. Once this compiled model has been obtained, all the possible faults of a given system can be identified quite easily.<>
{"title":"Diagnosing systems modeled with piecewise linear constraints","authors":"H. Beringer, B. D. Backer","doi":"10.1109/TAI.1990.130354","DOIUrl":"https://doi.org/10.1109/TAI.1990.130354","url":null,"abstract":"Most approaches to model-based diagnosis use incomplete constraint satisfaction techniques and are therefore incomplete. It is shown here how diagnostics may be considered as a general constraint satisfaction problem. Provided the system to be repaired is modeled with piecewise linear constraints on real variables, it is possible to give a clear characterization of the conflict set. Furthermore, a complete algorithm to find every conflict is described. This algorithm is an original and elegant solution to the diagnostic problem. Moreover, it can be used in order to build a compiled form of the initial model. Once this compiled model has been obtained, all the possible faults of a given system can be identified quite easily.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"22 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":"129950344","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 RAND Compiler Kit (RACK), a parser generator for AI (artificial-intelligence) applications, is described. RACK parsers are unique in their ability to recognize non-LR(k) languages. RACK is implemented in C and is upwardly compatible with YACC. RACK generates parsers that interface with C or Lisp and includes features such as arbitrary look-ahead, multiple start symbols, a scanner generator, and a grammar interpreter. RACK has been fully implemented in under 20000 lines of C code.<>
{"title":"RACK: a parser generator for AI languages","authors":"J. Kipps","doi":"10.1109/TAI.1990.130375","DOIUrl":"https://doi.org/10.1109/TAI.1990.130375","url":null,"abstract":"The RAND Compiler Kit (RACK), a parser generator for AI (artificial-intelligence) applications, is described. RACK parsers are unique in their ability to recognize non-LR(k) languages. RACK is implemented in C and is upwardly compatible with YACC. RACK generates parsers that interface with C or Lisp and includes features such as arbitrary look-ahead, multiple start symbols, a scanner generator, and a grammar interpreter. RACK has been fully implemented in under 20000 lines of C code.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"3 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":"124570937","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}
F. Mateos, P. Basagoiti, G. Dégano, V. G. Echabe, D. Matauco, R. Criado, F. Lasheras, J. G. Bellido, J. Randez
The authors introduce the methodology and techniques used in constructing an expert system designed to serve as a decision-making aid for the operators of power system control centers in the area of systems security. Fundamentally, the system is designed for complete contingency analysis on an extensive power transmission network, as well as for proposing possible corrective actions aimed at taking the network to safe states. One of the main characteristics of this expert system is its close combination with the algorithmic techniques traditionally used in such control centers and heuristic methods, in order to provide solutions to problems.<>
{"title":"An expert system for contingency analysis in transmission networks","authors":"F. Mateos, P. Basagoiti, G. Dégano, V. G. Echabe, D. Matauco, R. Criado, F. Lasheras, J. G. Bellido, J. Randez","doi":"10.1109/TAI.1990.130380","DOIUrl":"https://doi.org/10.1109/TAI.1990.130380","url":null,"abstract":"The authors introduce the methodology and techniques used in constructing an expert system designed to serve as a decision-making aid for the operators of power system control centers in the area of systems security. Fundamentally, the system is designed for complete contingency analysis on an extensive power transmission network, as well as for proposing possible corrective actions aimed at taking the network to safe states. One of the main characteristics of this expert system is its close combination with the algorithmic techniques traditionally used in such control centers and heuristic methods, in order to provide solutions to problems.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"49 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":"130375943","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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