Intelligent Computer Mathematics
Intelligent Computer Mathematics
复制标题
智能计算机数学
DOI:
10.1007/978-3-540-85110-3_29
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发表时间:
2008
期刊:
影响因子:
--
通讯作者:
Bundy A
中科院分区:
文献类型:
--
作者:
Bundy A
The automation of reasoning as deduction in logical theories is well established. Such logical theories are usually inherited from the literature or are built manually for a particular reasoning task. They are then regarded as fixed. We will argue that they should be regarded as fluid.1As Pólya and others have argued, appropriate representation is the key to successful problem solving [Pólya, 1945]. It follows that a successful problem solver must be able to choose or construct the representation best suited to solving the current problem. Some of the most seminal episodes in human problem solving required radical representational change.1Automated agents use logical theories calledontologies. For different agents to communicate they must align their ontologies. When a large, diverse and evolving community of autonomous agents are continually engaged in online negotiations, it is not practical to manually pre-align the ontologies of all agent pairs - it must be done dynamically and automatically.1Persistent agents must be able to cope with a changing world and changing goals. This requires evolving their ontologies as their problem solving task evolves. The W3C call thisontology evolution.
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DOI:
--
发表时间:
2013
期刊:
arXiv.org
影响因子:
--
作者:
T. Melham;Raphael Cohn;Ian Childs
通讯作者:
Ian Childs
DOI:
--
发表时间:
2015
期刊:
International Conference on Verification, Model Checking and Abstract Interpretation
影响因子:
--
作者:
Andrew Reynolds;Viktor Kunčak
通讯作者:
Viktor Kunčak
影响因子:
1.1
作者:
C. Limongelli;M. Temperini
通讯作者:
M. Temperini
影响因子:
--
作者:
Moa Johansson;Dan Rosén;Nicholas Smallbone;Koen Claessen
通讯作者:
Koen Claessen
DOI:
10.1007/978-3-319-66107-0_1
发表时间:
2017
期刊:
2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
影响因子:
--
作者:
Moa Johansson
通讯作者:
Moa Johansson