Pub Date : 1994-03-01DOI: 10.1109/CAIA.1994.323699
Johannes Stein
Describes a multiagent approach to a class of layout problems, in particular that of arranging a set of electrical components in a low-voltage switchgear cabinet. A survey of known approaches has shown that the layout problems have exclusively been either of constraint optimization or constraint satisfaction. Inherent to the problem mentioned is the presence of both types of constraints. This paper shows that multiagent approaches exhibit sufficient flexibility to deal with both types of constraints. In the approach taken, the original problem is translated into a pure optimization problem which can be solved efficiently by agents behaving "reactively". However, the applied optimization technique cannot guarantee to satisfy any "hard" constraints defined in the problem. Such constraints then have to be solved by cooperative activity of agents. Initial experiences with this idea were gained by implementing a simplified example.<>
{"title":"On the solution of layout problems in multiagent systems: a preliminary report","authors":"Johannes Stein","doi":"10.1109/CAIA.1994.323699","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323699","url":null,"abstract":"Describes a multiagent approach to a class of layout problems, in particular that of arranging a set of electrical components in a low-voltage switchgear cabinet. A survey of known approaches has shown that the layout problems have exclusively been either of constraint optimization or constraint satisfaction. Inherent to the problem mentioned is the presence of both types of constraints. This paper shows that multiagent approaches exhibit sufficient flexibility to deal with both types of constraints. In the approach taken, the original problem is translated into a pure optimization problem which can be solved efficiently by agents behaving \"reactively\". However, the applied optimization technique cannot guarantee to satisfy any \"hard\" constraints defined in the problem. Such constraints then have to be solved by cooperative activity of agents. Initial experiences with this idea were gained by implementing a simplified example.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"96 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":"124691098","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.1994.323627
Y. Nagai, S. Honiden
This paper describes a mapping method using composition for software reuse in object-oriented constraint programming (OOCP) languages. We discuss the mapping of design plans using an OOCP language. We also apply this mapping, using composition in an OOCP language, to a mechanical design problem and demonstrate the effectiveness of our approach.<>
{"title":"Composition-based mapping of design plans into implementation-level architectures","authors":"Y. Nagai, S. Honiden","doi":"10.1109/CAIA.1994.323627","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323627","url":null,"abstract":"This paper describes a mapping method using composition for software reuse in object-oriented constraint programming (OOCP) languages. We discuss the mapping of design plans using an OOCP language. We also apply this mapping, using composition in an OOCP language, to a mechanical design problem and demonstrate the effectiveness of our approach.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"17 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":"126872400","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.1994.323692
M. Schwabacher, H. Hirsh, T. Ellman
The first step for most case-based design systems is to select an initial prototype from a database of previous designs. The retrieved prototype is then modified to tailor it to the given goals. For any particular design goal the selection of a starting point for the design process can have a dramatic effect both on the quality of the eventual design and on the overall design time. We present a technique for automatically constructing effective prototype-selection rules. Our technique applies a standard inductive-learning algorithm, C4.5, to a set of training data describing which particular prototype would have been the best choice for each goal encountered in a previous design session. We have tested our technique in, the domain of racing-yacht-hull design, comparing our inductively learned selection rules to several competing prototype-selection methods. Our results show that the inductive prototype-selection method leads to better final designs when the design process is guided by a noisy evaluation function, and that the inductively learned rules will often be more efficient than competing methods.<>
{"title":"Learning prototype-selection rules for case-based iterative design","authors":"M. Schwabacher, H. Hirsh, T. Ellman","doi":"10.1109/CAIA.1994.323692","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323692","url":null,"abstract":"The first step for most case-based design systems is to select an initial prototype from a database of previous designs. The retrieved prototype is then modified to tailor it to the given goals. For any particular design goal the selection of a starting point for the design process can have a dramatic effect both on the quality of the eventual design and on the overall design time. We present a technique for automatically constructing effective prototype-selection rules. Our technique applies a standard inductive-learning algorithm, C4.5, to a set of training data describing which particular prototype would have been the best choice for each goal encountered in a previous design session. We have tested our technique in, the domain of racing-yacht-hull design, comparing our inductively learned selection rules to several competing prototype-selection methods. Our results show that the inductive prototype-selection method leads to better final designs when the design process is guided by a noisy evaluation function, and that the inductively learned rules will often be more efficient than competing methods.<<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":"127102348","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.1994.323675
P. Hammond
Oncologists manage the treatment of cancer patients under a variety of protocol-based clinical trials. Each trial requires data to be collected for monitoring efficacy and toxicity. The life-threatening nature of cancer and the toxicity of therapy emphasise the safety-critical nature of oncology. OaSiS provides decision support for protocol-based treatment of cancer and contributes to better data management and safer, more consistent application of protocols. It offers a highly graphical interface, employs logic-based problem-solving and is implemented in PROLOG.<>
{"title":"OaSiS: integrating safety reasoning for decision support in oncology","authors":"P. Hammond","doi":"10.1109/CAIA.1994.323675","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323675","url":null,"abstract":"Oncologists manage the treatment of cancer patients under a variety of protocol-based clinical trials. Each trial requires data to be collected for monitoring efficacy and toxicity. The life-threatening nature of cancer and the toxicity of therapy emphasise the safety-critical nature of oncology. OaSiS provides decision support for protocol-based treatment of cancer and contributes to better data management and safer, more consistent application of protocols. It offers a highly graphical interface, employs logic-based problem-solving and is implemented in PROLOG.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"19 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1994-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114042106","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.1994.323698
G. Bezzi, C. Bolchini, I. Bolzoni, M. Bombana, G. Buonanno, S. Cantu, P. Cavalloro, D. Sciuto, G. Zaza
Describes CASTOR (Computer Aided System Testability OptimizeR), which is able to support CAD designers in order to produce testable and efficient VLSI designs. Expert system and object oriented techniques have been used to describe, in a homogeneous framework, different device architectures, formalized testability conditions and design for testability techniques. The CASTOR architecture is modular, and its I/O interfaces are based on the standard description language VHDL, to allow industrial exploitation and easy encapsulation in commercial CAD frameworks. CASTOR has been tested on an industrial telecommunication device. Results and figures of merit are included. The main contribution of this novel approach is the support provided by such an automatic tool to the common designer who does not have specific knowledge of testability items.<>
{"title":"CASTOR: an expert advisor for testability enhancement of VLSI systems","authors":"G. Bezzi, C. Bolchini, I. Bolzoni, M. Bombana, G. Buonanno, S. Cantu, P. Cavalloro, D. Sciuto, G. Zaza","doi":"10.1109/CAIA.1994.323698","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323698","url":null,"abstract":"Describes CASTOR (Computer Aided System Testability OptimizeR), which is able to support CAD designers in order to produce testable and efficient VLSI designs. Expert system and object oriented techniques have been used to describe, in a homogeneous framework, different device architectures, formalized testability conditions and design for testability techniques. The CASTOR architecture is modular, and its I/O interfaces are based on the standard description language VHDL, to allow industrial exploitation and easy encapsulation in commercial CAD frameworks. CASTOR has been tested on an industrial telecommunication device. Results and figures of merit are included. The main contribution of this novel approach is the support provided by such an automatic tool to the common designer who does not have specific knowledge of testability items.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"27 3 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":"124436944","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.1994.323662
D. Delahaye, J. Alliot, Marc Schoenauer, J. Farges
In this paper, we show how genetic algorithms can be used to compute automatically a balanced sectoring of air-space to increase air traffic control capacity in high density areas.<>
在本文中，我们展示了如何使用遗传算法来自动计算空域的平衡划分，以增加高密度区域的空中交通管制能力。
{"title":"Genetic algorithms for partitioning air space","authors":"D. Delahaye, J. Alliot, Marc Schoenauer, J. Farges","doi":"10.1109/CAIA.1994.323662","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323662","url":null,"abstract":"In this paper, we show how genetic algorithms can be used to compute automatically a balanced sectoring of air-space to increase air traffic control capacity in high density areas.<<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":"121038535","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.1994.323649
F. Ramparany, R. Zigman, R. Yap
Monitoring and controlling the processes of complex and geographically distributed systems requires a robust modeling of the behaviour of the systems in terms of causal relationships among its state variables, and the handling of temporal delays that may span an event and its causal influences throughout the system. In this paper, we explain how we have integrated the functionalities of a constraint management system and a temporal database system, to enable a model-based control of systems that exhibit large delays between the events characterizing their behaviour. Our approach has been applied to build a knowledge-based system for assisting central heating operators to optimize the efficiency and profitability of the heating process.<>
{"title":"Integrating causal and coarse grain temporal reasoning in a model based control system","authors":"F. Ramparany, R. Zigman, R. Yap","doi":"10.1109/CAIA.1994.323649","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323649","url":null,"abstract":"Monitoring and controlling the processes of complex and geographically distributed systems requires a robust modeling of the behaviour of the systems in terms of causal relationships among its state variables, and the handling of temporal delays that may span an event and its causal influences throughout the system. In this paper, we explain how we have integrated the functionalities of a constraint management system and a temporal database system, to enable a model-based control of systems that exhibit large delays between the events characterizing their behaviour. Our approach has been applied to build a knowledge-based system for assisting central heating operators to optimize the efficiency and profitability of the heating process.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"5 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":"116818724","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.1994.323691
E. Domeshek, M. Herndon, A. Bennett, J. Kolodner
This paper describes MIDAS (a memory for initial design of aircraft subsystems), a system that applies insights and techniques from case-based reasoning (CBR) to aid engineers in the design of utility subsystems early in the development of a new aircraft concept. Our goal is to demonstrate the usefulness and practicality of a particular approach to building a corporate design memory. MIDAS is an instance of a general class of systems we call Case-Based Design Aids (CBDAs). A CBDA provides a designer with convenient access to multimedia presentations that highlight the outstanding good and bad points of previous designs. MIDAS was developed as a joint project of the Georgia Tech AI Lab and Lockheed Aeronautical Systems Company's Advanced Design Division. It is the first CBDA to be built largely by domain experts; the AI team primarily provided an (evolving) tool kit, and advice.<>
{"title":"A case-based design aid for conceptual design of aircraft subsystems","authors":"E. Domeshek, M. Herndon, A. Bennett, J. Kolodner","doi":"10.1109/CAIA.1994.323691","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323691","url":null,"abstract":"This paper describes MIDAS (a memory for initial design of aircraft subsystems), a system that applies insights and techniques from case-based reasoning (CBR) to aid engineers in the design of utility subsystems early in the development of a new aircraft concept. Our goal is to demonstrate the usefulness and practicality of a particular approach to building a corporate design memory. MIDAS is an instance of a general class of systems we call Case-Based Design Aids (CBDAs). A CBDA provides a designer with convenient access to multimedia presentations that highlight the outstanding good and bad points of previous designs. MIDAS was developed as a joint project of the Georgia Tech AI Lab and Lockheed Aeronautical Systems Company's Advanced Design Division. It is the first CBDA to be built largely by domain experts; the AI team primarily provided an (evolving) tool kit, and advice.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"9 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":"125588317","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.1994.323620
J. Yen, S. Teh, W.M. Lively
One of the major issues in modeling expert systems (ESs) for enhanced reusability is capturing a high-level view of their operations. Principled knowledge representation schemes have been used to model components of complex software systems. However, the potential for applying these principled modeling techniques to explicitly capture the functional requirements of ESs has not been fully explored. This paper investigates issues and provides solutions to the use of an AI knowledge representation scheme for developing an ontology of the software components to facilitate their classification and retrieval. Its benefits are demonstrated using two real world knowledge-based systems.<>
{"title":"Principled modeling and automatic classification of functional requirements for improved reusability of the design of knowledge-based systems","authors":"J. Yen, S. Teh, W.M. Lively","doi":"10.1109/CAIA.1994.323620","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323620","url":null,"abstract":"One of the major issues in modeling expert systems (ESs) for enhanced reusability is capturing a high-level view of their operations. Principled knowledge representation schemes have been used to model components of complex software systems. However, the potential for applying these principled modeling techniques to explicitly capture the functional requirements of ESs has not been fully explored. This paper investigates issues and provides solutions to the use of an AI knowledge representation scheme for developing an ontology of the software components to facilitate their classification and retrieval. Its benefits are demonstrated using two real world knowledge-based systems.<<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":"128105468","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.1994.323694
S. Sen, E. Durfee
We present design considerations for an automated meeting scheduling agent that processes meeting requests on behalf of its associated user. In our formulation of the meeting scheduling problem, distributed meeting scheduling agents, one per user, intelligently exchange information with each other to schedule meetings without compromising user-specified constraints. In this paper, we first enumerate various strategies we have investigated to focus distributed negotiation between scheduling agents. Next, we demonstrate the necessity for such a scheduler to be adaptive in its choice of options for the various strategy dimensions, so that it can perform effectively over time. In order to build an adaptive scheduler that can effectively choose from available strategy options, we develop quantitative performance estimates of these options using detailed probabilistic analysis. Results from these analyses are used to provide guidelines to choose the most appropriate strategy combination given current environmental conditions and local problem-solving states.<>
{"title":"On the design of an adaptive meeting scheduler","authors":"S. Sen, E. Durfee","doi":"10.1109/CAIA.1994.323694","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323694","url":null,"abstract":"We present design considerations for an automated meeting scheduling agent that processes meeting requests on behalf of its associated user. In our formulation of the meeting scheduling problem, distributed meeting scheduling agents, one per user, intelligently exchange information with each other to schedule meetings without compromising user-specified constraints. In this paper, we first enumerate various strategies we have investigated to focus distributed negotiation between scheduling agents. Next, we demonstrate the necessity for such a scheduler to be adaptive in its choice of options for the various strategy dimensions, so that it can perform effectively over time. In order to build an adaptive scheduler that can effectively choose from available strategy options, we develop quantitative performance estimates of these options using detailed probabilistic analysis. Results from these analyses are used to provide guidelines to choose the most appropriate strategy combination given current environmental conditions and local problem-solving states.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"105 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":"126015252","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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