Pub Date : 1994-03-01DOI: 10.1109/CAIA.1994.323677
J. Berger
Roentgen is a case-based aid to radiation therapy planning. It relies on an archive of past therapy cases to suggest plans for new therapy patients. Roentgen supports therapy planning by: (1) retrieving the case which best matches the geometry and treatment constraints of the new patient; (2) tailoring the plan to the specific details of the patient; (3) evaluating the results of applying the plan; and (4) repairing the plan to avoid any discovered faults in treatment results. This final plan is the system's suggestion to the human planner. Roentgen breaks new ground in solving problems in a domain dominated by spatial reasoning and the satisfying of constraints.<>
{"title":"Roentgen: radiation therapy and case-based reasoning","authors":"J. Berger","doi":"10.1109/CAIA.1994.323677","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323677","url":null,"abstract":"Roentgen is a case-based aid to radiation therapy planning. It relies on an archive of past therapy cases to suggest plans for new therapy patients. Roentgen supports therapy planning by: (1) retrieving the case which best matches the geometry and treatment constraints of the new patient; (2) tailoring the plan to the specific details of the patient; (3) evaluating the results of applying the plan; and (4) repairing the plan to avoid any discovered faults in treatment results. This final plan is the system's suggestion to the human planner. Roentgen breaks new ground in solving problems in a domain dominated by spatial reasoning and the satisfying of constraints.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126099439","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}
Pub Date : 1994-03-01DOI: 10.1109/CAIA.1994.323638
Yui-Liang Chen, D. Hung
The authors present an algorithm for representing two-dimensional shapes that may be partially occluded or overlapped. Based on the approximated polygon, extended features that encompass neighborhoods of significant corners are defined. Two hypothetic angularities are first established to create hypothetic space. An analytic-circular-tag (ACT) which groups a specified number of consecutive hypothetic points in the sequence is used to provide a new scheme for matching technique. A hypothesis-generation-testing technique is used for contour matching. The experimental results demonstrate that the algorithms can be used to match complex industrial parts.<>
{"title":"On the recognition of industrial parts","authors":"Yui-Liang Chen, D. Hung","doi":"10.1109/CAIA.1994.323638","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323638","url":null,"abstract":"The authors present an algorithm for representing two-dimensional shapes that may be partially occluded or overlapped. Based on the approximated polygon, extended features that encompass neighborhoods of significant corners are defined. Two hypothetic angularities are first established to create hypothetic space. An analytic-circular-tag (ACT) which groups a specified number of consecutive hypothetic points in the sequence is used to provide a new scheme for matching technique. A hypothesis-generation-testing technique is used for contour matching. The experimental results demonstrate that the algorithms can be used to match complex industrial parts.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126667181","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}
Pub Date : 1994-03-01DOI: 10.1109/CAIA.1994.323678
D. Pathak, M. Perlin
Describes a system to automatically compute genetic risks. To compute genetic risk, genetic counselors consider a variety of data, including family history, disease characteristics and DNA information, within a Bayesian inference framework. However, to manually process all the information is an error-prone and tedious task. Our system provides an automation of this task. It accepts as input the case data and the specification of the risk assessment task. The output of the system is the risk value of interest. The design of the system is based on a blackboard architecture. We describe the knowledge sources making up the system and an illustrative example of the use of the system do compute genetic risks.<>
{"title":"Automatic computation of genetic risk","authors":"D. Pathak, M. Perlin","doi":"10.1109/CAIA.1994.323678","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323678","url":null,"abstract":"Describes a system to automatically compute genetic risks. To compute genetic risk, genetic counselors consider a variety of data, including family history, disease characteristics and DNA information, within a Bayesian inference framework. However, to manually process all the information is an error-prone and tedious task. Our system provides an automation of this task. It accepts as input the case data and the specification of the risk assessment task. The output of the system is the risk value of interest. The design of the system is based on a blackboard architecture. We describe the knowledge sources making up the system and an illustrative example of the use of the system do compute genetic risks.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124476138","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}
Pub Date : 1994-03-01DOI: 10.1109/CAIA.1994.323663
M. Weiss, Frank Zeyer
This paper describes the design of a case-based reasoning system for the redesign of local area networks. It introduces a mechanism for solution adaptation based on a hierarchy of possible actions, each of which is associated with background knowledge about its suitability. A novel similarity measure is used to rank actions where multiple alternative actions are found for an action that cannot be applied in the current problem context. The measure uses a heuristic weighting function between the degree of abstraction and the degree of specificity. It is shown how other measures for closeness may be derived as specializations of the one presented.<>
{"title":"Redesign of local area networks using similarity-based adaptation","authors":"M. Weiss, Frank Zeyer","doi":"10.1109/CAIA.1994.323663","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323663","url":null,"abstract":"This paper describes the design of a case-based reasoning system for the redesign of local area networks. It introduces a mechanism for solution adaptation based on a hierarchy of possible actions, each of which is associated with background knowledge about its suitability. A novel similarity measure is used to rank actions where multiple alternative actions are found for an action that cannot be applied in the current problem context. The measure uses a heuristic weighting function between the degree of abstraction and the degree of specificity. It is shown how other measures for closeness may be derived as specializations of the one presented.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126544074","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}
Pub Date : 1994-03-01DOI: 10.1109/CAIA.1994.323688
M. Malliaris
The October 1987 stock market crash challenged the prevailing financial models of a random walk and led to the emergence of a new and competing model of stock price time series. This new approach supports a nonrandom underlying structure and is labeled chaotic dynamics. If a neural network can be constructed which determines market prices better than the random walk model, it would support those who claim that they have found statistical evidence that a chaotic dynamics structure underlies the market. This paper constructs a neural network which lends support to the deterministic paradigm.<>
{"title":"Modeling the behavior of the S&P 500 index: a neural network approach","authors":"M. Malliaris","doi":"10.1109/CAIA.1994.323688","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323688","url":null,"abstract":"The October 1987 stock market crash challenged the prevailing financial models of a random walk and led to the emergence of a new and competing model of stock price time series. This new approach supports a nonrandom underlying structure and is labeled chaotic dynamics. If a neural network can be constructed which determines market prices better than the random walk model, it would support those who claim that they have found statistical evidence that a chaotic dynamics structure underlies the market. This paper constructs a neural network which lends support to the deterministic paradigm.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"167 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125980954","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}
Pub Date : 1994-03-01DOI: 10.1109/CAIA.1994.323666
S. Ito, Y. Nakayama, Y. Namioka, H. Mizutani
Proposes a model-based approach to generating graphical explanations of high-level specifications for plant control. The specifications are explained by a symbolic simulator which generates a plant animation. The animation corresponds directly to images of a machine's action which the designers have in their minds, so that the designers can easily confirm the accuracy of their specifications. Many kinds of knowledge about a plant are needed to generate the animation. This knowledge includes machine structures, machine actions, functions, materials, and so on. The knowledge about functions is the most important for the plant animation, as it allows the symbolic simulator to reason about operations on the materials. In particular, when a plant deals with solid materials, it is difficult to represent this knowledge because the machines used in such a plant usually have some functions which depend on the machines' actions and plant conditions. To solve this problem, we have developed a framework to represent such functions and their relations.<>
{"title":"Model-based explanation of specifications for sequence control","authors":"S. Ito, Y. Nakayama, Y. Namioka, H. Mizutani","doi":"10.1109/CAIA.1994.323666","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323666","url":null,"abstract":"Proposes a model-based approach to generating graphical explanations of high-level specifications for plant control. The specifications are explained by a symbolic simulator which generates a plant animation. The animation corresponds directly to images of a machine's action which the designers have in their minds, so that the designers can easily confirm the accuracy of their specifications. Many kinds of knowledge about a plant are needed to generate the animation. This knowledge includes machine structures, machine actions, functions, materials, and so on. The knowledge about functions is the most important for the plant animation, as it allows the symbolic simulator to reason about operations on the materials. In particular, when a plant deals with solid materials, it is difficult to represent this knowledge because the machines used in such a plant usually have some functions which depend on the machines' actions and plant conditions. To solve this problem, we have developed a framework to represent such functions and their relations.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131036312","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}
Pub Date : 1994-03-01DOI: 10.1109/CAIA.1994.323652
A. Chandra, C.-L. Wu, J. Abraham
An important problem of validating stochastic models is addressed. Validating stochastic models is necessary for modeling high-performance and highly dependable computers accurately. This paper develops a model validation methodology using causal reasoning. More specifically, this technique uses the structural and behavioral knowledge derived from the system specification and a causal reasoning mechanism for validation purposes. The scope of this research is limited to the conceptual validation of Markov models. Conceptual validation, as opposed to empirical validation, does not require the use of data. The validation process primarily involves generating a reference object, translating the given model into a common format, and comparing the two objects to identify holes and inconsistencies. Event trees are used as the common format. The effectiveness of this methodology is tested by validating models of five example systems. For testing purposes, errors are introduced into the models of these systems.<>
{"title":"Using causal reasoning to validate stochastic models","authors":"A. Chandra, C.-L. Wu, J. Abraham","doi":"10.1109/CAIA.1994.323652","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323652","url":null,"abstract":"An important problem of validating stochastic models is addressed. Validating stochastic models is necessary for modeling high-performance and highly dependable computers accurately. This paper develops a model validation methodology using causal reasoning. More specifically, this technique uses the structural and behavioral knowledge derived from the system specification and a causal reasoning mechanism for validation purposes. The scope of this research is limited to the conceptual validation of Markov models. Conceptual validation, as opposed to empirical validation, does not require the use of data. The validation process primarily involves generating a reference object, translating the given model into a common format, and comparing the two objects to identify holes and inconsistencies. Event trees are used as the common format. The effectiveness of this methodology is tested by validating models of five example systems. For testing purposes, errors are introduced into the models of these systems.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131102669","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}
Pub Date : 1994-03-01DOI: 10.1109/CAIA.1994.323645
Marco Lazzari, P. Salvaneschi
Describes the results of a project which aims to improve the capabilities of an information system (IS) which supports the management of dam safety. The improvement has been achieved through the incorporation of additional components developed using AI concepts and technologies. We describe the pre-existing IS (comprised of automatic monitoring systems, telemetry and databases), identify user requirements driving the evolution of the IS and explain how AI concepts and technologies may contribute. We describe the functions, the architecture and the AI techniques of two systems (MISTRAL and DAMSAFE) added to the IS. Moreover, we discuss the issue of integration of the AI components and the pre-existing system and we present the technology developed to support this process. Finally, we give the implementation status of the project (which has delivered components that have been operational since 1992) and some information about the user acceptance, development effort and applicability to other fields.<>
{"title":"Improved monitoring and surveillance through integration of artificial intelligence and information management systems","authors":"Marco Lazzari, P. Salvaneschi","doi":"10.1109/CAIA.1994.323645","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323645","url":null,"abstract":"Describes the results of a project which aims to improve the capabilities of an information system (IS) which supports the management of dam safety. The improvement has been achieved through the incorporation of additional components developed using AI concepts and technologies. We describe the pre-existing IS (comprised of automatic monitoring systems, telemetry and databases), identify user requirements driving the evolution of the IS and explain how AI concepts and technologies may contribute. We describe the functions, the architecture and the AI techniques of two systems (MISTRAL and DAMSAFE) added to the IS. Moreover, we discuss the issue of integration of the AI components and the pre-existing system and we present the technology developed to support this process. Finally, we give the implementation status of the project (which has delivered components that have been operational since 1992) and some information about the user acceptance, development effort and applicability to other fields.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132848269","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}
Pub Date : 1994-03-01DOI: 10.1109/CAIA.1994.323636
T. Daradoumis
We present a new initial approach to modeling dialogue which, based on two levels of discourse planning, builds structure and context incrementally in multiple levels as the dialogue progresses. This fact allows speaker's intentions and beliefs as well as attentional, rhetorical and pragmatic knowledge to be represented at each level in a more specific manner than previous models.<>
{"title":"Building an RST-based multi-level dialogue context and structure","authors":"T. Daradoumis","doi":"10.1109/CAIA.1994.323636","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323636","url":null,"abstract":"We present a new initial approach to modeling dialogue which, based on two levels of discourse planning, builds structure and context incrementally in multiple levels as the dialogue progresses. This fact allows speaker's intentions and beliefs as well as attentional, rhetorical and pragmatic knowledge to be represented at each level in a more specific manner than previous models.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"264 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133109853","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}
Pub Date : 1994-03-01DOI: 10.1109/CAIA.1994.323629
G. Matthews, R. Matthews, W. Landis
Ecological studies and multispecies ecotoxicological tests are based on the examination of a variety of physical, chemical and biological data with the intent of finding patterns in their changing relationships over time. The data sets resulting from such studies are often noisy, incomplete, and difficult to envision. We have developed machine learning and visualization software to aid in the analysis, modelling, and understanding of such systems. The software is based on nonmetric conceptual clustering, which attempts to analyze the data into clusters that are strongly associated with several measured parameters. Our analysis and visualization tools not only confirmed suspected ecological patterns, but revealed aspects of the data that were unnoticed by ecologists using conventional statistical techniques.<>
{"title":"Nonmetric clustering: new approaches for ecological data","authors":"G. Matthews, R. Matthews, W. Landis","doi":"10.1109/CAIA.1994.323629","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323629","url":null,"abstract":"Ecological studies and multispecies ecotoxicological tests are based on the examination of a variety of physical, chemical and biological data with the intent of finding patterns in their changing relationships over time. The data sets resulting from such studies are often noisy, incomplete, and difficult to envision. We have developed machine learning and visualization software to aid in the analysis, modelling, and understanding of such systems. The software is based on nonmetric conceptual clustering, which attempts to analyze the data into clusters that are strongly associated with several measured parameters. Our analysis and visualization tools not only confirmed suspected ecological patterns, but revealed aspects of the data that were unnoticed by ecologists using conventional statistical techniques.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131305187","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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