Xiao Xue – författare

A methodological framework for bridging the gap between Artificial Intelligence and Social Sciences Complex cyber-physical-social systems demand rigorous analysis, design, regulation and validation methods that traditional approaches cannot provide. Computational Experiments: A Bridge between Artificial Intelligence and Social Sciences delivers a systematic methodology spanning modeling, simulation, and validation of intelligent systems. Written by leading researchers in complex systems and artificial intelligence, this work provides both theoretical foundations and practical frameworks for studying intricate social and physical systems. The book covers the artificial society modeling framework across four levels: AI agents and prospect theory, learning mechanisms of AI agents, AI society and social networks, and integration with environmental systems. It addresses how computational experiments incorporate generative agents and large language models, and explores policy sandboxes for decision analysis and social system behavior prediction in complex contexts. The book also discusses: Comprehensive coverage of computational experiment methodology including origins, development history, and knowledge frameworks essential for practical applicationsSocial simulation technology foundations providing unique insights into simulating and deducing complex social systems from interdisciplinary research perspectivesDetailed exploration of AI agent architectures incorporating prospect theory, ??reinforcement learning mechanisms, and multi-agent coordination strategies for ??system modelingFrameworks for integrating virtual and real-world intelligence to improve predictive capabilities and support decision-making in complex operational environmentsDesigned for professors, researchers, and graduate students in computer science, artificial intelligence, social computing, and systems engineering, this book also serves professionals in policy simulation, strategic planning, and smart system development who require rigorous methods for validating intelligent system behavior.

A methodological framework for bridging the gap between Artificial Intelligence and Social Sciences Complex cyber-physical-social systems demand rigorous analysis, design, regulation and validation methods that traditional approaches cannot provide. Computational Experiments: A Bridge between Artificial Intelligence and Social Sciences delivers a systematic methodology spanning modeling, simulation, and validation of intelligent systems. Written by leading researchers in complex systems and artificial intelligence, this work provides both theoretical foundations and practical frameworks for studying intricate social and physical systems. The book covers the artificial society modeling framework across four levels: AI agents and prospect theory, learning mechanisms of AI agents, AI society and social networks, and integration with environmental systems. It addresses how computational experiments incorporate generative agents and large language models, and explores policy sandboxes for decision analysis and social system behavior prediction in complex contexts. The book also discusses: Comprehensive coverage of computational experiment methodology including origins, development history, and knowledge frameworks essential for practical applications Social simulation technology foundations providing unique insights into simulating and deducing complex social systems from interdisciplinary research perspectives Detailed exploration of AI agent architectures incorporating prospect theory, ??reinforcement learning mechanisms, and multi-agent coordination strategies for ??system modeling Frameworks for integrating virtual and real-world intelligence to improve predictive capabilities and support decision-making in complex operational environments Designed for professors, researchers, and graduate students in computer science, artificial intelligence, social computing, and systems engineering, this book also serves professionals in policy simulation, strategic planning, and smart system development who require rigorous methods for validating intelligent system behavior.

A methodological framework for bridging the gap between Artificial Intelligence and Social Sciences Complex cyber-physical-social systems demand rigorous analysis, design, regulation and validation methods that traditional approaches cannot provide. Computational Experiments: A Bridge between Artificial Intelligence and Social Sciences delivers a systematic methodology spanning modeling, simulation, and validation of intelligent systems. Written by leading researchers in complex systems and artificial intelligence, this work provides both theoretical foundations and practical frameworks for studying intricate social and physical systems. The book covers the artificial society modeling framework across four levels: AI agents and prospect theory, learning mechanisms of AI agents, AI society and social networks, and integration with environmental systems. It addresses how computational experiments incorporate generative agents and large language models, and explores policy sandboxes for decision analysis and social system behavior prediction in complex contexts. The book also discusses: Comprehensive coverage of computational experiment methodology including origins, development history, and knowledge frameworks essential for practical applications Social simulation technology foundations providing unique insights into simulating and deducing complex social systems from interdisciplinary research perspectives Detailed exploration of AI agent architectures incorporating prospect theory, ??reinforcement learning mechanisms, and multi-agent coordination strategies for ??system modeling Frameworks for integrating virtual and real-world intelligence to improve predictive capabilities and support decision-making in complex operational environments Designed for professors, researchers, and graduate students in computer science, artificial intelligence, social computing, and systems engineering, this book also serves professionals in policy simulation, strategic planning, and smart system development who require rigorous methods for validating intelligent system behavior.

This book constitutes the revised selected papers of the scientific satellite events that were held in conjunction with the 17th International Conference on Service-Oriented Computing, ICSOC 2019, held in Toulouse, France, in October 2019.The ICSOC 2019 workshop track consisted of five workshops on a wide range of topics that fall into the general area of service computing:- The 15th International Workshop on Engineering Service-Oriented Applications and Cloud Services (WESOACS). 4 papers over the 6 received submissions were accepted.                                                                                                                                                                                                                                                                              - The 4th International Workshop on Adaptive Service-oriented and Cloud Applications (ASOCA). 2 papers over the 4 received submissions were accepted. Moreover, 2 invited papers were presented in this workshop.- The 4th International IoT Systems Provisioning & Management for Context-Aware Smart Cities (ISYCC). 3 papers over the 5 received submissions were accepted. Moreover, 3 invited papers were presented in this workshop.- The 1st edition of Towards Blockchain-Based Collaborative Enterprise (TBCE). It accepted 2 papers over the 3 received submissions. - The 1st edition of Smart daTa integRation And Processing on Service based environments (STRAPS). 3 papers over the 7 received submissions were accepted. An additional invited paper was presented in this workshop.

This book constitutes the papers of eight workshops which were held in conjunction with the 23rd International Conference on Service-Oriented Computing, ICSOC 2025, held in Shenzhen, China, during December 1–4, 2025.The 40 full papers included in this book were carefully reviewed and selected from 124 submissions. They present the following eight workshops:– 10th International Workshop on Adaptive Service-oriented and Cloud Applications (ASOCA)– 6th International Workshop on AI-enabled Process Automation (AI-PA)– 3rd International Workshop on Services and Quantum Software (SQS)– 2nd International Workshop on Service-Oriented Computing for AI Applications (SOC4AI)– 1st International Workshop on Service Intelligence for Collaborative Industry (SICI)– 1st International Workshop on Reliable and Efficient Multi-agent Services (REMS)– 1st International Workshop on Data-Centric Artificial Intelligence for Intelligent Agents (DCAI4IA) – 1st International Workshop on Service-Oriented Computing, AI, and IoT for Smart Applications (SOCAISA)