Pub Date : 1994-03-01DOI: 10.1109/CAIA.1994.323648
Cao Jian-qing, Qin Mingwan, He Ming
The integrated Taxis environment for information system development uses a semantic network framework based on work in knowledge representation, and also draws on results from databases, programming languages, and software engineering. This paper presents the first use of the Taxis environment to design a real engineering project information system after the Taxis compiler was implemented. It shows that the use of the Taxis environment and methodology has effectively and efficiently led to the development of a new management system which offers a natural correspondence between the system and the real world, and provides suitable responsiveness, thus overcoming the shortcomings of the previous engineering system, which was developed using traditional tools and methods. This experience of this study is valuable for the development of information and engineering systems.<>
{"title":"A development process for engineering project management information systems based on semantic data models","authors":"Cao Jian-qing, Qin Mingwan, He Ming","doi":"10.1109/CAIA.1994.323648","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323648","url":null,"abstract":"The integrated Taxis environment for information system development uses a semantic network framework based on work in knowledge representation, and also draws on results from databases, programming languages, and software engineering. This paper presents the first use of the Taxis environment to design a real engineering project information system after the Taxis compiler was implemented. It shows that the use of the Taxis environment and methodology has effectively and efficiently led to the development of a new management system which offers a natural correspondence between the system and the real world, and provides suitable responsiveness, thus overcoming the shortcomings of the previous engineering system, which was developed using traditional tools and methods. This experience of this study is valuable for the development of information and engineering systems.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"26 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":"126314554","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.1994.323681
J. Benton, V. S. Subrahmanian
Hybrid knowledge bases (HKBs) are a formalism for integrating multiple representations of knowledge and data. HKBs provide a uniform framework for integrating uncertain information (as is often the case in terrain reasoning), temporal information (needed for weather effects, etc.), and numeric constraint solving capabilities (for situation assessment). We show how the HKB formalism may be applied to solve the problem of placing Patriot and Hawk missile batteries in a specified terrain, subject to the requirement that various existing assets be afforded maximal protection. We formalize this problem in a clear, mathematical framework, using the HKB paradigm, and show how the problem is solved. This provides a mathematically sound, as well as a practically viable, scalable solution to the important problem of missile siting.<>
{"title":"Using hybrid knowledge bases for missile siting problems","authors":"J. Benton, V. S. Subrahmanian","doi":"10.1109/CAIA.1994.323681","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323681","url":null,"abstract":"Hybrid knowledge bases (HKBs) are a formalism for integrating multiple representations of knowledge and data. HKBs provide a uniform framework for integrating uncertain information (as is often the case in terrain reasoning), temporal information (needed for weather effects, etc.), and numeric constraint solving capabilities (for situation assessment). We show how the HKB formalism may be applied to solve the problem of placing Patriot and Hawk missile batteries in a specified terrain, subject to the requirement that various existing assets be afforded maximal protection. We formalize this problem in a clear, mathematical framework, using the HKB paradigm, and show how the problem is solved. This provides a mathematically sound, as well as a practically viable, scalable solution to the important problem of missile siting.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"92 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":"132510470","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.1994.323650
Murray Hill
Terminological knowledge representation (TKR) systems, such as KL-ONE, are widely used in AI to construct concept taxonomies based on subsumption inferences. However, current TKR systems are unable to represent temporal patterns or recognize instances of such patterns from ongoing observations. Motivated by applications such as service personnel dispatching, and plan recognition for interactive user interfaces, we extend TKR by introducing terminological QME (qualitative, metric and equality) networks. In QME networks, nodes are TKR concepts and arcs are qualitative constraints between temporal intervals associated with nodes, metric constraints between end-points of temporal intervals, and equality constraints among roles of different concepts. We use QME networks to represent patterns, and define QME network subsumption, which enables us to organize a pattern library into a taxonomy. We also develop a terminological approach to predictive pattern recognition based on subsumption and a related notion of compatibility. We assign a modality of "necessary", "optional" or "impossible" to every pattern as events and constraints are observed. We also show how to augment a pattern library for complete recognition. This work, implemented in the T-REX system, enables more sophisticated applications of TKR technology.<>
{"title":"Subsumption and recognition of heterogeneous constraint networks","authors":"Murray Hill","doi":"10.1109/CAIA.1994.323650","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323650","url":null,"abstract":"Terminological knowledge representation (TKR) systems, such as KL-ONE, are widely used in AI to construct concept taxonomies based on subsumption inferences. However, current TKR systems are unable to represent temporal patterns or recognize instances of such patterns from ongoing observations. Motivated by applications such as service personnel dispatching, and plan recognition for interactive user interfaces, we extend TKR by introducing terminological QME (qualitative, metric and equality) networks. In QME networks, nodes are TKR concepts and arcs are qualitative constraints between temporal intervals associated with nodes, metric constraints between end-points of temporal intervals, and equality constraints among roles of different concepts. We use QME networks to represent patterns, and define QME network subsumption, which enables us to organize a pattern library into a taxonomy. We also develop a terminological approach to predictive pattern recognition based on subsumption and a related notion of compatibility. We assign a modality of \"necessary\", \"optional\" or \"impossible\" to every pattern as events and constraints are observed. We also show how to augment a pattern library for complete recognition. This work, implemented in the T-REX system, enables more sophisticated applications of TKR technology.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"2018 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":"132994378","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.1994.323660
Yiqun Gu, D. Peiris, John W. Crawford, J. W. NcNicol, B. Marshall, R. A. Jefferies
Bayesian belief networks are shown to be natural and efficient knowledge representation tools for modelling and manipulating uncertainties in developing expert systems. They provide a basis for probabilistic inference, to calculate the changes in probabilistic belief as new evidence is obtained. However, their use in real problem domains is hampered by the difficulties facing the construction of such belief networks, particularly in domains where neither sufficient data nor human expertise is available. In this paper, we show that this problem can be circumvented by exploiting knowledge from existing mathematical models. An application of belief networks to assess the impact of climate change on potato production is used as an illustration. We show how the uncertainty of future climate change, variability of current weather and the knowledge about potato development can be combined in a belief network, which provides an aid for policy makers in agriculture. The model is tested using synthetic weather scenarios. The results are compared with those obtained from a conventional mathematical model.<>
{"title":"An application of belief networks to future crop production","authors":"Yiqun Gu, D. Peiris, John W. Crawford, J. W. NcNicol, B. Marshall, R. A. Jefferies","doi":"10.1109/CAIA.1994.323660","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323660","url":null,"abstract":"Bayesian belief networks are shown to be natural and efficient knowledge representation tools for modelling and manipulating uncertainties in developing expert systems. They provide a basis for probabilistic inference, to calculate the changes in probabilistic belief as new evidence is obtained. However, their use in real problem domains is hampered by the difficulties facing the construction of such belief networks, particularly in domains where neither sufficient data nor human expertise is available. In this paper, we show that this problem can be circumvented by exploiting knowledge from existing mathematical models. An application of belief networks to assess the impact of climate change on potato production is used as an illustration. We show how the uncertainty of future climate change, variability of current weather and the knowledge about potato development can be combined in a belief network, which provides an aid for policy makers in agriculture. The model is tested using synthetic weather scenarios. The results are compared with those obtained from a conventional mathematical model.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"15 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":"124954526","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.1994.323626
D. Neuhaus, M. Lusti
The leasing vs. purchasing decision involves legal, tax, accounting and financial issues. It is a multiple criteria decision problem, which takes into account quantitative and qualitative criteria. The expert knowledge is not available in most small and medium sized businesses. Companies depend on external experts like practising lawyers, finance specialists, tax and leasing advisers. An expert system such as Leasing Advisor, described in this paper, can provide the integration of knowledge from different problem areas to support the decision making process.<>
{"title":"A decision support system for leasing/purchasing decisions","authors":"D. Neuhaus, M. Lusti","doi":"10.1109/CAIA.1994.323626","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323626","url":null,"abstract":"The leasing vs. purchasing decision involves legal, tax, accounting and financial issues. It is a multiple criteria decision problem, which takes into account quantitative and qualitative criteria. The expert knowledge is not available in most small and medium sized businesses. Companies depend on external experts like practising lawyers, finance specialists, tax and leasing advisers. An expert system such as Leasing Advisor, described in this paper, can provide the integration of knowledge from different problem areas to support the decision making process.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"16 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":"125362969","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.1994.323693
K. Fischer, N. Kuhn, J. Muller
We demonstrate the use of DAI (distributed artificial intelligence) techniques to construct a distributed solution for a class of scheduling tasks within the transportation domain. We deal with the dynamic allocation of transportation orders to a set of resources (different shipping companies and their trucks), aiming to minimize transportation distance, time, and/or the number of resources (trucks). We provide a thorough description of the concepts and the implementation underlying our system. Important research work done in related fields such as operations research is reviewed. Finally, we discuss the results obtained by a series of experiments we carried through in order to compare the problem solving power of the DAI approach with standard operations research methods for solving (distributed) scheduling problems.<>
{"title":"Distributed, knowledge-based, reactive scheduling of transportation tasks","authors":"K. Fischer, N. Kuhn, J. Muller","doi":"10.1109/CAIA.1994.323693","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323693","url":null,"abstract":"We demonstrate the use of DAI (distributed artificial intelligence) techniques to construct a distributed solution for a class of scheduling tasks within the transportation domain. We deal with the dynamic allocation of transportation orders to a set of resources (different shipping companies and their trucks), aiming to minimize transportation distance, time, and/or the number of resources (trucks). We provide a thorough description of the concepts and the implementation underlying our system. Important research work done in related fields such as operations research is reviewed. Finally, we discuss the results obtained by a series of experiments we carried through in order to compare the problem solving power of the DAI approach with standard operations research methods for solving (distributed) scheduling problems.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"121 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":"126794677","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.1994.323621
S. B. Yadav, Chen Xueguang, Lu Ke, F. Qi
Presents an "inverted" approach to solve the problem of allocation and distribution. Under this approach, we start with the distribution first, and then do the allocation next. This inverted approach is supported by a system called the Intelligent Materials Allocator and Distributor (IMAD). The IMAD system has been in use for two years in China. The use of this system has resulted in a general increase of one million yuans in profit for each material transfer station.<>
{"title":"An Intelligent Materials Allocator and Distributor","authors":"S. B. Yadav, Chen Xueguang, Lu Ke, F. Qi","doi":"10.1109/CAIA.1994.323621","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323621","url":null,"abstract":"Presents an \"inverted\" approach to solve the problem of allocation and distribution. Under this approach, we start with the distribution first, and then do the allocation next. This inverted approach is supported by a system called the Intelligent Materials Allocator and Distributor (IMAD). The IMAD system has been in use for two years in China. The use of this system has resulted in a general increase of one million yuans in profit for each material transfer station.<<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":"128240901","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.1994.323639
R. J. Calistri-Yeh
Two recent ORA projects have benefited from the AI principles captured in a blackboard design. DANA, a multimedia network monitoring system, has profited by using knowledge sources to implement a policy of guaranteed update consistency. Project X, a real-time passive radar system, has used knowledge source independence and the flexibility of opportunistic control to support easy experimentation with new system designs. Along with the benefits, we learned several lessons about the difficulties and pitfalls of blackboard systems. First, opportunistic control is hard to implement, maintain, and sell to managers. Second, knowledge sources are never as neat as they first appear. Finally, it is important to treat blackboards as a programming philosophy and not an architectural mandate.<>
{"title":"Applying blackboard techniques to real-time signal processing and multimedia network management","authors":"R. J. Calistri-Yeh","doi":"10.1109/CAIA.1994.323639","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323639","url":null,"abstract":"Two recent ORA projects have benefited from the AI principles captured in a blackboard design. DANA, a multimedia network monitoring system, has profited by using knowledge sources to implement a policy of guaranteed update consistency. Project X, a real-time passive radar system, has used knowledge source independence and the flexibility of opportunistic control to support easy experimentation with new system designs. Along with the benefits, we learned several lessons about the difficulties and pitfalls of blackboard systems. First, opportunistic control is hard to implement, maintain, and sell to managers. Second, knowledge sources are never as neat as they first appear. Finally, it is important to treat blackboards as a programming philosophy and not an architectural mandate.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"184 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":"133881810","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.1994.323696
R. Arbon, G.G. Mally, T. Osborne, P.R. Riethmeier, R.L. Tharrett
The Automated Master Production Scheduler (Auto-MPS) is a hybrid expert scheduling system which performs production scheduling of thousands of assemblies in a high-volume manufacturing environment. It generates schedules based on a set of rules and constraint satisfaction algorithms which reflect the scheduling strategies created by management to meet their customer demand while still controlling inventory and shipping costs. The Auto-MPS also identifies the existence of significant situations which need to be analyzed by management. A graphical user interface that includes sophisticated graphical displays and hypertext based editors allows the user to easily understand the status of the current production schedules and rapidly identify and analyze potential problems. The Auto-MPS has been in production for nearly two years and has significantly improved the scheduling processes at AlliedSignal Safety Restraint Systems.<>
{"title":"Auto-MPS: an automated master production scheduling system for large volume manufacturing","authors":"R. Arbon, G.G. Mally, T. Osborne, P.R. Riethmeier, R.L. Tharrett","doi":"10.1109/CAIA.1994.323696","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323696","url":null,"abstract":"The Automated Master Production Scheduler (Auto-MPS) is a hybrid expert scheduling system which performs production scheduling of thousands of assemblies in a high-volume manufacturing environment. It generates schedules based on a set of rules and constraint satisfaction algorithms which reflect the scheduling strategies created by management to meet their customer demand while still controlling inventory and shipping costs. The Auto-MPS also identifies the existence of significant situations which need to be analyzed by management. A graphical user interface that includes sophisticated graphical displays and hypertext based editors allows the user to easily understand the status of the current production schedules and rapidly identify and analyze potential problems. The Auto-MPS has been in production for nearly two years and has significantly improved the scheduling processes at AlliedSignal Safety Restraint Systems.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"54 4 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":"134138979","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.1994.323669
R. Rajagopalan, B. Kuipers
Describes an ongoing project to develop a theory of qualitative spatial reasoning which merges a simple, intuitive description of the spatial extent, relative position, and orientation of objects with existing methods for qualitative reasoning about dynamically changing worlds. We are applying our theories within a system for problem solving about the magnetic fields domain. We describe methods for integrating diagram and test input to a problem solver, methods of abstraction for modeling the spatial extents of objects, and a method for modeling spatial relations between objects through inequalities on extremal points which directly allows reasoning about the effects of translational motion.<>
{"title":"Qualitative spatial reasoning about objects in motion: application to physics problem solving","authors":"R. Rajagopalan, B. Kuipers","doi":"10.1109/CAIA.1994.323669","DOIUrl":"https://doi.org/10.1109/CAIA.1994.323669","url":null,"abstract":"Describes an ongoing project to develop a theory of qualitative spatial reasoning which merges a simple, intuitive description of the spatial extent, relative position, and orientation of objects with existing methods for qualitative reasoning about dynamically changing worlds. We are applying our theories within a system for problem solving about the magnetic fields domain. We describe methods for integrating diagram and test input to a problem solver, methods of abstraction for modeling the spatial extents of objects, and a method for modeling spatial relations between objects through inequalities on extremal points which directly allows reasoning about the effects of translational motion.<<ETX>>","PeriodicalId":297396,"journal":{"name":"Proceedings of the Tenth Conference on Artificial Intelligence for Applications","volume":"125 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":"131662316","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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