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Register a new userApple Core-dination: Linguistic Feedback and Learning in a Speech-to-Action Shared World Game
التفاح النواة التخلي عن: ردود الفعل اللغوية والتعلم في لعبة العالم المشتركة بين الكلام
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نحن نبحث عن مسألة كيف تؤثر ردود الفعل التكيفية من عامل الظاهري على المدخلات اللغوية للمستخدم في بيئة ألعاب عالمية مشتركة. للقيام بذلك، نقوم بإجراء دراسة تجريبية استكشافية لمراقبة كيفية تأثير ردود الفعل اللغوية الفردية على إدخال خطاب المستخدم. نقدم لعبة تسيطر على الكلام، وإخلاء Apple الأساسية، حيث يتعلم الوكيل المهام المعقدة باستخدام معرفة قاعدة بأعمال بسيطة. تم تجهيز الوكيل بآلية تعليمية لرسم الأوامر الجديدة بتسلسل الإجراءات البسيطة، وكذلك القدرة على دمج إدخال المستخدم في ردود مكتوبة. يشارك الوكيل مرارا وتكرارا حالته المعرفة الداخلية من خلال الاستجابة لما يعرفه ولا يعرفه عن معنى اللغة والبيئة المشتركة. تركز ورقتنا على حلقة الملاحظات اللغوية من أجل تحليل طبيعة إدخال المستخدم. يتم توفير ردود الفعل من الوكيل في شكل حركة مرئية وردود لغوية مكتوبة. يتم إيلاء اهتمام خاص لإدماج مدخلات المستخدم في استجابات الوكيل وتحديث تعيينات الكلام إلى العمل بناء على الأوامر التي يقدمها المستخدم. من خلال دراستنا التجريبية، نقوم بتحليل نجاح المهمة ومقارنة الميزات المعجمية لإدخال المستخدم. تظهر النتائج الاختلاف في طول المدخلات والتنوع المعجمي عبر المستخدمين، مما يشير إلى ارتباط بين الاثنين يمكن دراستهما كذلك.
We investigate the question of how adaptive feedback from a virtual agent impacts the linguistic input of the user in a shared world game environment. To do so, we carry out an exploratory pilot study to observe how individualized linguistic feedback affects the user's speech input. We introduce a speech-controlled game, Apple Core-dination, in which an agent learns complex tasks using a base knowledge of simple actions. The agent is equipped with a learning mechanism for mapping new commands to sequences of simple actions, as well as the ability to incorporate user input into written responses. The agent repeatedly shares its internal knowledge state by responding to what it knows and does not know about language meaning and the shared environment. Our paper focuses on the linguistic feedback loop in order to analyze the nature of user input. Feedback from the agent is provided in the form of visual movement and written linguistic responses. Particular attention is given to incorporating user input into agent responses and updating the speech-to-action mappings based on commands provided by the user. Through our pilot study, we analyze task success and compare the lexical features of user input. Results show variation in input length and lexical variety across users, suggesting a correlation between the two that can be studied further.
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