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Register a new userHow to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds
كيفية تحفيز التنين الخاص بك: تدريس وكلاء يحركهم الأهداف للتحدث والتصرف في عوالم الخيال
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نسعى إلى إنشاء وكلاء يتصرفون والتواصل مع الوكلاء الآخرين في السعي لتحقيق هدف.تحقيقا لهذه الغاية، نقوم بتمديد الضوء (Urbanek et al. 2019) --- لعبة نصية خيال من الحشد على نطاق واسع - مع مجموعة بيانات من المهام.هذه تحتوي على دوافع لغوية طبيعية مقترنة بأهداف في اللعبة والمظاهرات البشرية؛قد يتطلب إكمال السعي حوار أو إجراءات (أو كليهما).نقدم نظام لتعليم التعزيز (1) يشتمل على التدريب المستندة إلى النمذجة على النمذجة القائمة على النمذجة على النمذجة على نطاق واسع ومقرها مسبقا لإشراف الوكيل مع البثور ذات الصلة؛و (2) يرفع مساحة عمل عوامل من أوامر العمل والحوار، موازنة بين الاثنين.نقوم بإجراء تقييمات طلقة صفرية باستخدام مظاهرات الخبراء البشرية المحتفظ بها، والتي تبين أن عملائنا قادرون على التصرف باستمرار والتحدث بشكل طبيعي فيما يتعلق بدوافعهم.
We seek to create agents that both act and communicate with other agents in pursuit of a goal. Towards this end, we extend LIGHT (Urbanek et al. 2019)---a large-scale crowd-sourced fantasy text-game---with a dataset of quests. These contain natural language motivations paired with in-game goals and human demonstrations; completing a quest might require dialogue or actions (or both). We introduce a reinforcement learning system that (1) incorporates large-scale language modeling-based and commonsense reasoning-based pre-training to imbue the agent with relevant priors; and (2) leverages a factorized action space of action commands and dialogue, balancing between the two. We conduct zero-shot evaluations using held-out human expert demonstrations, showing that our agents are able to act consistently and talk naturally with respect to their motivations.
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Text-based games simulate worlds and interact with players using natural language. Recent work has used them as a testbed for autonomous language-understanding agents, with the motivation being that understanding the meanings of words or semantics is
a key component of how humans understand, reason, and act in these worlds. However, it remains unclear to what extent artificial agents utilize semantic understanding of the text. To this end, we perform experiments to systematically reduce the amount of semantic information available to a learning agent. Surprisingly, we find that an agent is capable of achieving high scores even in the complete absence of language semantics, indicating that the currently popular experimental setup and models may be poorly designed to understand and leverage game texts. To remedy this deficiency, we propose an inverse dynamics decoder to regularize the representation space and encourage exploration, which shows improved performance on several games including Zork I. We discuss the implications of our findings for designing future agents with stronger semantic understanding.
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menu item) and oreo cookies'' (a topping) from an input utterance Can I order a cheese pizza with oreo cookies on top?'' exemplifies such invalid combinations according to the menu of a restaurant business. Traditional dialogue systems allow execution of validation rules as a post-processing step after slots have been filled which can lead to error accumulation. In this paper, we formalize knowledge-driven slot constraints and present a new task of constraint violation detection accompanied with benchmarking data. Then, we propose methods to integrate the external knowledge into the system and model constraint violation detection as an end-to-end classification task and compare it to the traditional rule-based pipeline approach. Experiments on two domains of the MultiDoGO dataset reveal challenges of constraint violation detection and sets the stage for future work and improvements.
To be good conversational partners, natural language processing (NLP) systems should be trained to produce contextually useful utterances. Prior work has investigated training NLP systems with communication-based objectives, where a neural listener s
tands in as a communication partner. However, these systems commonly suffer from semantic drift where the learned language diverges radically from natural language. We propose a method that uses a population of neural listeners to regularize speaker training. We first show that language drift originates from the poor uncertainty calibration of a neural listener, which makes high-certainty predictions on novel sentences. We explore ensemble- and dropout-based populations of listeners and find that the former results in better uncertainty quantification. We evaluate both population-based objectives on reference games, and show that the ensemble method with better calibration enables the speaker to generate pragmatic utterances while scaling to a large vocabulary and generalizing to new games and listeners.
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