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

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書目名稱Artificial Psychology影響因子(影響力)




書目名稱Artificial Psychology影響因子(影響力)學(xué)科排名




書目名稱Artificial Psychology網(wǎng)絡(luò)公開度




書目名稱Artificial Psychology網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Artificial Psychology被引頻次




書目名稱Artificial Psychology被引頻次學(xué)科排名




書目名稱Artificial Psychology年度引用




書目名稱Artificial Psychology年度引用學(xué)科排名




書目名稱Artificial Psychology讀者反饋




書目名稱Artificial Psychology讀者反饋學(xué)科排名

熔巖

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

閑蕩

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.

不理會

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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