KNOWBEL is a tool offering the knowledge representation language Telos and the logic programming system MRS for the development of an expert system. Telos is a tightly integrated hybrid knowledge representation scheme, offering facilities for structuring a knowledge base as well as an assertional sublanguage for expressing deductive rules and integrity constraints. Unlike Prolog, MRS provides facilities for customizing an expert system inference engine. The KNOWBEL architecture clearly separates the knowledge and implementation levels for a knowledge base and its associated operations. KNOWBEL also supports temporal reasoning, extensive constraint enforcement, and a user-friendly window-based interface.<>
{"title":"KNOWBEL: a hybrid expert system building tool","authors":"J. Mylopoulos, Huaiqing Wang, A. Kushniruk","doi":"10.1109/TAI.1990.130451","DOIUrl":"https://doi.org/10.1109/TAI.1990.130451","url":null,"abstract":"KNOWBEL is a tool offering the knowledge representation language Telos and the logic programming system MRS for the development of an expert system. Telos is a tightly integrated hybrid knowledge representation scheme, offering facilities for structuring a knowledge base as well as an assertional sublanguage for expressing deductive rules and integrity constraints. Unlike Prolog, MRS provides facilities for customizing an expert system inference engine. The KNOWBEL architecture clearly separates the knowledge and implementation levels for a knowledge base and its associated operations. KNOWBEL also supports temporal reasoning, extensive constraint enforcement, and a user-friendly window-based interface.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"14 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":"125215150","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 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 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}
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}
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}
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}
TIMEX, a tool that extends Prolog with features for interval-based representation, is described. Because several ideas for interval-based representation exist, the tool provides the possibility of switching between different representation and propagation techniques. This is done by representing different interval graphs. Thus, one application may consist of several graphs with different attributes for propagation and one interval may exist in different graphs. The application domain is technical expert systems. An interval-based representation was used for a scheduling expert system in a steelmaking plant.<>
{"title":"TIMEX-a tool for interval-based representation in technical applications","authors":"J. Dorn","doi":"10.1109/TAI.1990.130388","DOIUrl":"https://doi.org/10.1109/TAI.1990.130388","url":null,"abstract":"TIMEX, a tool that extends Prolog with features for interval-based representation, is described. Because several ideas for interval-based representation exist, the tool provides the possibility of switching between different representation and propagation techniques. This is done by representing different interval graphs. Thus, one application may consist of several graphs with different attributes for propagation and one interval may exist in different graphs. The application domain is technical expert systems. An interval-based representation was used for a scheduling expert system in a steelmaking plant.<<ETX>>","PeriodicalId":366276,"journal":{"name":"[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence","volume":"120 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":"114562475","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}
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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