Human-Machine Shared Contexts

Human-Machine Shared Contexts considers the foundations, metrics, and applications of human-machine systems. Editors and authors debate whether machines, humans, and systems should speak only to each other, only to humans, or to both and how. The book establishes the meaning and operation of "shared contexts" between humans and machines; it also explores how human-machine systems affect targeted audiences (researchers, machines, robots, users) and society, as well as future ecosystems composed of humans and machines.

This book explores how user interventions may improve the context for autonomous machines operating in unfamiliar environments or when experiencing unanticipated events; how autonomous machines can be taught to explain contexts by reasoning, inferences, or causality, and decisions to humans relying on intuition; and for mutual context, how these machines may interdependently affect human awareness, teams and society, and how these machines may be affected in turn. In short, can context be mutually constructed and shared between machines and humans? The editors are interested in whether shared context follows when machines begin to think, or, like humans, develop subjective states that allow them to monitor and report on their interpretations of reality, forcing scientists to rethink the general model of human social behavior. If dependence on machine learning continues or grows, the public will also be interested in what happens to context shared by users, teams of humans and machines, or society when these machines malfunction. As scientists and engineers think through this change in human terms, the ultimate goal is for AI to advance the performance of autonomous machines and teams of humans and machines for the betterment of society wherever these machines interact with humans or other machines.

This book will be essential reading for professional, industrial, and military computer scientists and engineers; machine learning (ML) and artificial intelligence (AI) scientists and engineers, especially those engaged in research on autonomy, computational context, and human-machine shared contexts; advanced robotics scientists and engineers; scientists working with or interested in data issues for autonomous systems such as with the use of scarce data for training and operations with and without user interventions; social psychologists, scientists and physical research scientists pursuing models of shared context; modelers of the internet of things (IOT); systems of systems scientists and engineers and economists; scientists and engineers working with agent-based models (ABMs); policy specialists concerned with the impact of AI and ML on society and civilization; network scientists and engineers; applied mathematicians (e.g., holon theory, information theory); computational linguists; and blockchain scientists and engineers.

「人機共享情境」探討了人機系統的基礎、評估指標和應用。編者和作者就機器、人類和系統是否應只彼此交流、只與人類交流還是兩者皆可交流以及如何交流進行辯論。本書確立了人與機器之間「共享情境」的意義和運作方式；同時探討了人機系統如何影響目標受眾（研究人員、機器、機器人、使用者）和社會，以及由人類和機器組成的未來生態系統。

本書探討了使用者介入如何改善在陌生環境中運作的自主機器的情境，或在遇到意外事件時如何解釋情境；自主機器如何透過推理、推論或因果關係以及依賴直覺向人類解釋情境；以及在共享情境中，這些機器如何相互影響人類的意識、團隊和社會，以及這些機器如何受到影響。簡而言之，機器和人類之間能否共同建構和分享情境？編者們對於當機器開始思考，或者像人類一樣發展出主觀狀態以便監測和報告對現實的解釋時，共享情境是否會出現感興趣，這迫使科學家重新思考人類社會行為的一般模型。如果對機器學習的依賴持續增加，公眾也會對在這些機器發生故障時，使用者、人機團隊或社會共享的情境會發生什麼變化感興趣。隨著科學家和工程師以人類的術語思考這種變化，最終目標是讓人工智慧在機器與人類或其他機器互動的任何地方，提升自主機器和人機團隊的性能，造福社會。

本書將成為專業、工業和軍事計算機科學家和工程師、機器學習（ML）和人工智慧（AI）科學家和工程師的必讀之書，尤其是那些從事自主性、計算情境和人機共享情境研究的人；高級機器人科學家和工程師；與使用稀缺數據進行訓練和操作的自主系統的數據問題相關的科學家；追求共享情境模型的社會心理學家、科學家和物理研究科學家；物聯網（IOT）模型的建模者；系統科學家和工程師以及經濟學家；與基於代理模型（ABMs）工作的科學家和工程師；關注AI和ML對社會和文明影響的政策專家；網絡科學家和工程師；應用數學家（例如，整體論理論、信息理論）；計算語言學家；以及區塊鏈科學家和工程師。