[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31712":3,"doc-seo-31712":27},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"file_id":15,"file_url":16,"file_type":17,"file_size":18,"view_count":4,"is_deleted":4,"is_public":19,"is_downloadable":19,"audit_status":19,"page_count":20,"language":21,"language_code":22,"table_of_contents":23,"faqs":24,"seo_title":13,"seo_description":14,"update_tm":25,"read_time":26},31712,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",6,"Technology","Artificial Intelligence A Modern Approach Fourth US Edition Figures","Illustrates foundational neuroscience concepts and the connectionist basis for learning, describing neurons’ dendrites, axon length, synapses, and how electrochemical signaling supports short-term control and long-term connectivity changes in the cerebral cortex. Compares human brain computation with supercomputer performance across FLOP scales. Introduces intelligent agents through environments, percepts, actions, and PEAS, then presents canonical agent programs: table-driven, simple reflex, model-based reflex, goal-based, utility-based, and learning-agent structures with functions and internal state updates.","cbCainxIVYCwBYCQ","https://ap.wps.com/l/cbCainxIVYCwBYCQ","pdf",22470373,1,232,"English","en","# Introduction\n## Neuron structure and signaling\n## Brain versus supercomputer performance\n## Blocks world and SHRDLU\n# Intelligent Agents\n## Percepts, actions, and environments\n## PEAS task environment description\n## Agent types and agent functions","[{\"question\":\"How does the document describe how neurons enable learning?\",\"answer\":\"It explains that electrochemical signals control brain activity in the short term and also support long-term changes in neuronal connectivity, which are thought to form the basis for learning.\"},{\"question\":\"What are percepts and actions in the intelligent-agent framework?\",\"answer\":\"Agents interact with environments through sensors (to obtain percepts) and actuators (to execute actions), using those signals to decide what to do next.\"},{\"question\":\"How do model-based reflex agents differ from simple reflex agents?\",\"answer\":\"Simple reflex agents choose actions directly from condition–action rules matched to interpreted input state. 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Model-based reflex agents maintain an internal world state using transition and sensor models, then select actions based on matched rules.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":30}]