Most AI search/representation techniques are oriented toward an infinite domain of objects and arbitrary relations among them. In reality much of what needs to be represented in AI can be expressed using a finite domain and unary or binary predicates. Well-known vector- and matrix-based representations can efficiently represent finite domains and unary/binary predicates, and allow effective extraction of path information by generalized transitive closure/path matrix computations. In order to avoid space limitations in this approach, a set of abstract sparse matrix data types was developed along with a set of operations on them. This representation forms the basis of an intelligent information tool for representing and manipulating relational data. The tool is being used in developing a system that helps flight crews cope with in-flight malfunctions.<>
{"title":"A path-oriented matrix-based knowledge representation system","authors":"S. Feyock, S. T. Karamouzis","doi":"10.1109/TAI.1992.246433","DOIUrl":"https://doi.org/10.1109/TAI.1992.246433","url":null,"abstract":"Most AI search/representation techniques are oriented toward an infinite domain of objects and arbitrary relations among them. In reality much of what needs to be represented in AI can be expressed using a finite domain and unary or binary predicates. Well-known vector- and matrix-based representations can efficiently represent finite domains and unary/binary predicates, and allow effective extraction of path information by generalized transitive closure/path matrix computations. In order to avoid space limitations in this approach, a set of abstract sparse matrix data types was developed along with a set of operations on them. This representation forms the basis of an intelligent information tool for representing and manipulating relational data. The tool is being used in developing a system that helps flight crews cope with in-flight malfunctions.<<ETX>>","PeriodicalId":265283,"journal":{"name":"Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92","volume":"160 Pt 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128717382","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 problem-solving method for assignment problems and a corresponding expert system shell named COKE are presented and evaluated. The method consists of four main steps. Until all elements are assigned: (1) select an element to be assigned next, (2) propose a partner element for that element, (3) if new constraints are violated after the proposed step, try exchanging elements to remove or minimize the constraint violations from a local point of view, and (4) if the completed assignment still violates constraints, try exchanges to remove or minimize them from a global point of view and with more effort. Each step can take advantage of problem-specific knowledge. The evaluation with COKE shows that flexibility is of key importance in dealing with different kinds of assignment problems.<>
{"title":"COKE: efficient solving of complex assignment problems with the propose-and-exchange method","authors":"Karsten Poeck, F. Puppe","doi":"10.1109/TAI.1992.246362","DOIUrl":"https://doi.org/10.1109/TAI.1992.246362","url":null,"abstract":"A problem-solving method for assignment problems and a corresponding expert system shell named COKE are presented and evaluated. The method consists of four main steps. Until all elements are assigned: (1) select an element to be assigned next, (2) propose a partner element for that element, (3) if new constraints are violated after the proposed step, try exchanging elements to remove or minimize the constraint violations from a local point of view, and (4) if the completed assignment still violates constraints, try exchanges to remove or minimize them from a global point of view and with more effort. Each step can take advantage of problem-specific knowledge. The evaluation with COKE shows that flexibility is of key importance in dealing with different kinds of assignment problems.<<ETX>>","PeriodicalId":265283,"journal":{"name":"Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122306924","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}
Visual semantic networks, a representation scheme for a library of visual object models, are introduced. New models are learned in the library with the help of a knowledge engineer, who informs the system of the generic class of each new example, and then the system discovers potential cases of further classification, and gets them confirmed by the knowledge engineer. The details of discovery are discussed, and it is argued that the system behavior is more or less independent of the order of presentation of the examples. Experiments with a small number of mechanical tools confirm this.<>
{"title":"Learning object models in visual semantic networks","authors":"A. Gupta, A. Bagchi","doi":"10.1109/TAI.1992.246403","DOIUrl":"https://doi.org/10.1109/TAI.1992.246403","url":null,"abstract":"Visual semantic networks, a representation scheme for a library of visual object models, are introduced. New models are learned in the library with the help of a knowledge engineer, who informs the system of the generic class of each new example, and then the system discovers potential cases of further classification, and gets them confirmed by the knowledge engineer. The details of discovery are discussed, and it is argued that the system behavior is more or less independent of the order of presentation of the examples. Experiments with a small number of mechanical tools confirm this.<<ETX>>","PeriodicalId":265283,"journal":{"name":"Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114616056","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 application of combinatorial optimization techniques to the automatic spacecraft scheduling problem is described. The problem is to search over the candidate sequences of experiments for a sequence that maximizes the value of the science conducted while minimizing constraint conflicts. Exploratory computational results indicate that pseudorandom research techniques, such as simulated annealing, generate viable sequences in reasonable times.<>
{"title":"Combinatorial optimization for spacecraft scheduling","authors":"W. Scherer, Frank Rotman","doi":"10.1109/TAI.1992.246364","DOIUrl":"https://doi.org/10.1109/TAI.1992.246364","url":null,"abstract":"The application of combinatorial optimization techniques to the automatic spacecraft scheduling problem is described. The problem is to search over the candidate sequences of experiments for a sequence that maximizes the value of the science conducted while minimizing constraint conflicts. Exploratory computational results indicate that pseudorandom research techniques, such as simulated annealing, generate viable sequences in reasonable times.<<ETX>>","PeriodicalId":265283,"journal":{"name":"Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126745776","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}
It is shown that, using as basic building block a linear neuron with an anti-Hebb rule and restricted weights, an asymmetric network which computes the eigenvectors in the ascending order of their corresponding eigenvalues can be built. The conditions for their convergence are obtained and demonstrated by simulations.<>
{"title":"The minimum entropy network","authors":"R. Brause","doi":"10.1109/TAI.1992.246369","DOIUrl":"https://doi.org/10.1109/TAI.1992.246369","url":null,"abstract":"It is shown that, using as basic building block a linear neuron with an anti-Hebb rule and restricted weights, an asymmetric network which computes the eigenvectors in the ascending order of their corresponding eigenvalues can be built. The conditions for their convergence are obtained and demonstrated by simulations.<<ETX>>","PeriodicalId":265283,"journal":{"name":"Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132226120","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}
How database design knowledge can be used to formulate queries in response to high-level requests is described. A prototype system, QUICK, is discussed that uses a knowledge-rich conceptual schema to identify semantically reasonable subgraphs that correspond to user intent. QUICK manipulates conceptual schema subgraphs to produce database queries. Furthermore, by treating the subgraphs as knowledge constructs, QUICK can be used for interactive design evaluation. QUICK's functional architecture is described.<>
{"title":"QUICK: a system that uses conceptual design knowledge for query formulation","authors":"R. Semmel","doi":"10.1109/TAI.1992.246405","DOIUrl":"https://doi.org/10.1109/TAI.1992.246405","url":null,"abstract":"How database design knowledge can be used to formulate queries in response to high-level requests is described. A prototype system, QUICK, is discussed that uses a knowledge-rich conceptual schema to identify semantically reasonable subgraphs that correspond to user intent. QUICK manipulates conceptual schema subgraphs to produce database queries. Furthermore, by treating the subgraphs as knowledge constructs, QUICK can be used for interactive design evaluation. QUICK's functional architecture is described.<<ETX>>","PeriodicalId":265283,"journal":{"name":"Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133695662","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 software process is characterized as a transformation from a need to a software product that responds to that need. An application development environment is described that manages this process. It builds a conceptual model for the desired response (in the form of a fragment-based design) and automatically generates commercial-grade, module-based programs that implement the response. A decade of experience with a large application demonstrates the viability of this approach. Some observations on the software process are made.<>
{"title":"On the transformation from a fragment-based design into a module-based implementation","authors":"B. Blum","doi":"10.1109/TAI.1992.246373","DOIUrl":"https://doi.org/10.1109/TAI.1992.246373","url":null,"abstract":"The software process is characterized as a transformation from a need to a software product that responds to that need. An application development environment is described that manages this process. It builds a conceptual model for the desired response (in the form of a fragment-based design) and automatically generates commercial-grade, module-based programs that implement the response. A decade of experience with a large application demonstrates the viability of this approach. Some observations on the software process are made.<<ETX>>","PeriodicalId":265283,"journal":{"name":"Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133225541","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 iterative noise reduction learning algorithm is presented in which rules are learned in two phases. The first phase improves the quality of training data through a concept-driven closed-loop filtration process. In the second phase, classification rules are relearned from the filtered training data set.<>
{"title":"Iterative rule simplification for noise tolerant inductive learning","authors":"P. Pachowicz, J. Bala, Jianping Zhang","doi":"10.1109/TAI.1992.246447","DOIUrl":"https://doi.org/10.1109/TAI.1992.246447","url":null,"abstract":"An iterative noise reduction learning algorithm is presented in which rules are learned in two phases. The first phase improves the quality of training data through a concept-driven closed-loop filtration process. In the second phase, classification rules are relearned from the filtered training data set.<<ETX>>","PeriodicalId":265283,"journal":{"name":"Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116039889","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 associative data-parallel compilation model of logic programs capable of answering queries with unspecified relations concerning the given objects is described. The model benefits from the synergy resulting from associative search, data-parallelism during goal reduction, and the use of low-level code to invoke the subgoals and savings in data-transfers resulting from the presence of global registers. The use of associative tables extends the power of logic programming to answer a large class of queries and derive unspecified relation-names for the given objects. In contrast to the interpretation based on a pure data parallel model, this model does not suffer from data sequentiality caused by the presence of multiple-occurrence variables in the goals. The model also handles variable aliasing in the clauses efficiently using the associative data-parallel search and data-parallel assignment property.<>
{"title":"Data-parallel compilation and query power extension of large knowledge bases","authors":"A. Bansal, J. Potter, Lokam V. Prasad","doi":"10.1109/TAI.1992.246415","DOIUrl":"https://doi.org/10.1109/TAI.1992.246415","url":null,"abstract":"An associative data-parallel compilation model of logic programs capable of answering queries with unspecified relations concerning the given objects is described. The model benefits from the synergy resulting from associative search, data-parallelism during goal reduction, and the use of low-level code to invoke the subgoals and savings in data-transfers resulting from the presence of global registers. The use of associative tables extends the power of logic programming to answer a large class of queries and derive unspecified relation-names for the given objects. In contrast to the interpretation based on a pure data parallel model, this model does not suffer from data sequentiality caused by the presence of multiple-occurrence variables in the goals. The model also handles variable aliasing in the clauses efficiently using the associative data-parallel search and data-parallel assignment property.<<ETX>>","PeriodicalId":265283,"journal":{"name":"Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124247910","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}
In US Navy command and control systems there are increasing problems with situations in which human technicians must make quick decisions based on uncertain information. The SABER tool is being developed to use an explanation-based reasoning approach to assist in making such decisions. Explanation-based reasoning is modeled after an explanation-based decision-making process through which people are believed to perform some decision-making tasks. SABER constructs and evaluates alternative explanations to account for input data that may be incomplete or inconsistent. The explanations are evaluated according to three criteria: simplicity, completeness, and significance.<>
{"title":"Explanation-based decision support in real time situations","authors":"D. Hair, K. Pickslay, S. Chow","doi":"10.1109/TAI.1992.246377","DOIUrl":"https://doi.org/10.1109/TAI.1992.246377","url":null,"abstract":"In US Navy command and control systems there are increasing problems with situations in which human technicians must make quick decisions based on uncertain information. The SABER tool is being developed to use an explanation-based reasoning approach to assist in making such decisions. Explanation-based reasoning is modeled after an explanation-based decision-making process through which people are believed to perform some decision-making tasks. SABER constructs and evaluates alternative explanations to account for input data that may be incomplete or inconsistent. The explanations are evaluated according to three criteria: simplicity, completeness, and significance.<<ETX>>","PeriodicalId":265283,"journal":{"name":"Proceedings Fourth International Conference on Tools with Artificial Intelligence TAI '92","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124411642","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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