Embracing Inference as Action: A Step Towards Human-Level Reasoning

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• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9782
• 期：1
• 页码：192-201
• 全文大小：627 KB
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• 作者单位：John Licato (16)
  Maxwell Fowler (16)
  
  16. Department of Computer Science, Analogical Constructivism and Reasoning Lab (ACoRL), Indiana University/Purdue University - Fort Wayne (IPFW), Fort Wayne, IN, 46835, USA
  
• 丛书名：Artificial General Intelligence
• ISBN：978-3-319-41649-6
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9782

文摘

Human-level AI involves the ability to reason about the beliefs of other agents, even when those other agents have reasoning styles that may be very different than the AI’s. The ability to carry out reasonable inferences in such situations, as well as in situations where an agent must reason about the beliefs of another agent’s beliefs about yet another agent, is under-studied. We show how such reasoning can be carried out in a new variant of the cognitive event calculus we call \(\mathcal {CEC}_\mathtt {AC}\), by introducing several new powerful features for automated reasoning: First, the implementation of classical logic at the “system-level” and nonclassical logics at the “belief-level”; Second, \(\mathcal {CEC}_\mathtt {AC}\) treats all inferences made by agents as actions. This opens the door for two more additional features: epistemic boxes, which are a sort of frame in which the reasoning of an individual agent can be simulated, and evaluated codelets, which allow our reasoner to carry out operations beyond the limits of many current systems. We explain how these features are achieved and implemented in the MATR reasoning system, and discuss their consequences.