Titlebook: Artificial Psychology; Psychological Modeli James A. Crowder,John Carbone,Shelli Friess Book 2020 Springer Nature Switzerland AG 2020 Artif

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书目名称Artificial Psychology影响因子(影响力)




书目名称Artificial Psychology影响因子(影响力)学科排名




书目名称Artificial Psychology网络公开度




书目名称Artificial Psychology网络公开度学科排名




书目名称Artificial Psychology被引频次




书目名称Artificial Psychology被引频次学科排名




书目名称Artificial Psychology年度引用




书目名称Artificial Psychology年度引用学科排名




书目名称Artificial Psychology读者反馈




书目名称Artificial Psychology读者反馈学科排名

熔岩

Abductive Artificial Intelligence Learning Models,es of abductive reasoning to various artificial intelligence applications. Here we discuss machine learning models based on abductive learning techniques and their implications to artificial reasoning.

nonplus

闲荡

Introduction: Psychology and Technology, what is the true reality? We continually push to create increasingly intelligent systems/machines that attempt to learn, think, and reason like humans. Therefore, our first question becomes, when presented with this challenge is:

licence

Book 2020cognitive testing of advanced artificial intelligence systems. It shows how classical testing methods will reveal nothing about the cognitive nature of the systems and whether they are learning, reasoning, and evolving correctly; for these systems, the authors outline how testing techniques similar

Consensus

https://doi.org/10.1007/978-1-349-07704-5e critically influenced by implicit learning and knowledge and we need to understand how implicit vs. implicit learning and knowledge affect the ability of systems to learn and act effectively and correctly.

粘土

Conclusions and Next Steps,e critically influenced by implicit learning and knowledge and we need to understand how implicit vs. implicit learning and knowledge affect the ability of systems to learn and act effectively and correctly.

不理会

sight into the world of cognitive architectures and biologicThis book explores the subject of artificial psychology and how the field must adapt human neuro-psychological testing techniques to provide adequate cognitive testing of advanced artificial intelligence systems. It shows how classical test

sinoatrial-node

https://doi.org/10.1007/978-1-349-07704-5al intelligence in application of autonomous reasoning, knowledge assimilation, belief revision, and works well within a multi-agent AI framework. Here we present a flexible, hypothesis-driven methodology for Occam Abduction within a cognitive, artificially intelligent, system architecture.

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