Titlebook: Artificial Intelligence in Theory and Practice; IFIP 19th World Comp Max Bramer Conference proceedings 2006 IFIP International Federation f

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书目名称Artificial Intelligence in Theory and Practice影响因子(影响力)




书目名称Artificial Intelligence in Theory and Practice影响因子(影响力)学科排名




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书目名称Artificial Intelligence in Theory and Practice网络公开度学科排名




书目名称Artificial Intelligence in Theory and Practice被引频次




书目名称Artificial Intelligence in Theory and Practice被引频次学科排名




书目名称Artificial Intelligence in Theory and Practice年度引用




书目名称Artificial Intelligence in Theory and Practice年度引用学科排名




书目名称Artificial Intelligence in Theory and Practice读者反馈




书目名称Artificial Intelligence in Theory and Practice读者反馈学科排名

carotenoids

Artificial Intelligence and Knowledge Managementy how we be able to see it AI? It doesn’t intent to create identical copy of human being, but try to find the better form to represent all the knowledge contained in our minds. The society of the information lives a great paradox, at the same time that we have access to an innumerable amount of info

附录

The , Algorithm: Learning when and when not to interruptUnwanted or irrelevant interruptions hinder the user’s work and make him dislike the agent because it is being intrusive and impolite. The . algorithm enables interface agents to learn a user’s preferences and priorities regarding interruptions. The resulting user profile is then used by the agent t

金盘是高原

ingestion

Detecting and Repairing Anomalous Evolutions in Noisy Environments: Logic Programming Formalization nvironment. However, sensors may be unreliable, that is, they may deliver wrong information (due, e.g., to hardware or software malfunctioning) and, consequently, they may cause agents to take wrong decisions, which is a scenario that should be avoided. The paper considers the problem of reasoning i

olfction

捕鲸鱼叉

Learning Browsing Patterns for Context-Aware Recommendation-relevant information. In this paper, we propose an approach to enable proactive context-aware recommendation based on the knowledge of both user interests and browsing patterns. The proposed approach analyzes the browsing behavior of users to derive a semantically enhanced context that points out t

Conspiracy

Applying Collaborative Filtering to Reputation Domain: a model for more precise reputation estimatesation domains to provide more reliable and personalized reputation estimates. Despite being solved by recommendation field researches (e.g. significance weighting [.]), the problem of selecting low-trusted neighborhoods finds new roots in the reputation domain, mostly related to different behavior b

旋转一周

团结