Augmenting the analyst via situation-dependent reasoning with trust-annotated facts
作者： Brian Ulicny , Gerald M. Powell , Chester Brown , Mieczyslaw M. Kokar , Christopher J. Matheus
DOI: 10.1109/COGSIMA.2011.5753441
关键词: Artificial intelligence 、 Computer science 、 Task (project management) 、 Cognition 、 Credibility 、 Reliability (computer networking) 、 Intelligence amplification 、 Data science 、 Multitude 、 Context (language use) 、 Semantic reasoner
摘要: We say that a computer program augments the analyst if it can infer facts are implicit in existing information, but may be relatively difficult for human to infer. Among multitude of reasons, analyst's task is because (1) reported information analyzed and reasoned about often cannot completely trusted (requiring verification attempts via further collection corroboration where not possible, and/or assumption-based reasoning) (2) evaluation trust (reliability, credibility) context (or situation) dependent. These two sources difficulty, possibility augmentation-generated false alarms, imply one wants employ capabilities an automatic reasoner, reasoner must able deal with these kinds complexities.
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