Automatic Exploration of Textual Environments with Language-Conditioned Autotelic Agents

• URL: http://arxiv.org/abs/2207.04118v1
• Date: Fri, 8 Jul 2022 20:31:01 GMT
• Title: Automatic Exploration of Textual Environments with Language-Conditioned Autotelic Agents
• Authors: Laetitia Teodorescu and Eric Yuan and Marc-Alexandre C\^ot\'e and Pierre-Yves Oudeyer
• Abstract summary: We identify key properties of text worlds that make them suitable for exploration by autonmous agents. We discuss the opportunities of using autonomous agents to make progress on text environment benchmarks.
• Score: 21.29303927728839
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this extended abstract we discuss the opportunities and challenges of studying intrinsically-motivated agents for exploration in textual environments. We argue that there is important synergy between text environments and autonomous agents. We identify key properties of text worlds that make them suitable for exploration by autonmous agents, namely, depth, breadth, progress niches and the ease of use of language goals; we identify drivers of exploration for such agents that are implementable in text worlds. We discuss the opportunities of using autonomous agents to make progress on text environment benchmarks. Finally we list some specific challenges that need to be overcome in this area.

Related papers

• TextQuests: How Good are LLMs at Text-Based Video Games? [36.024745739590216]
  TextQuests is a benchmark based on the Infocom suite of interactive fiction games.<n>It is designed to assess an agent's capacity for self-contained problem-solving by precluding the use of external tools.
  arXiv  Detail & Related papers  (2025-07-31T16:22:55Z)
• A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions [51.96890647837277]
  Large Language Models (LLMs) have propelled conversational AI from traditional dialogue systems into sophisticated agents capable of autonomous actions, contextual awareness, and multi-turn interactions with users. This survey paper presents a desideratum for next-generation Conversational Agents - what has been achieved, what challenges persist, and what must be done for more scalable systems that approach human-level intelligence.
  arXiv  Detail & Related papers  (2025-04-07T21:01:25Z)
• A Survey on Complex Tasks for Goal-Directed Interactive Agents [60.53915548970061]
  This survey compiles relevant tasks and environments for evaluating goal-directed interactive agents. An up-to-date compilation of relevant resources can be found on our project website.
  arXiv  Detail & Related papers  (2024-09-27T08:17:53Z)
• tagE: Enabling an Embodied Agent to Understand Human Instructions [3.943519623674811]
  We introduce a novel system known as task and argument grounding for Embodied agents (tagE) At its core, our system employs an inventive neural network model designed to extract a series of tasks from complex task instructions expressed in natural language. Our proposed model adopts an encoder-decoder framework enriched with nested decoding to effectively extract tasks and their corresponding arguments from these intricate instructions.
  arXiv  Detail & Related papers  (2023-10-24T08:17:48Z)
• Towards Possibilities & Impossibilities of AI-generated Text Detection: A Survey [97.33926242130732]
  Large Language Models (LLMs) have revolutionized the domain of natural language processing (NLP) with remarkable capabilities of generating human-like text responses. Despite these advancements, several works in the existing literature have raised serious concerns about the potential misuse of LLMs. To address these concerns, a consensus among the research community is to develop algorithmic solutions to detect AI-generated text.
  arXiv  Detail & Related papers  (2023-10-23T18:11:32Z)
• Interactive Natural Language Processing [67.87925315773924]
  Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP. This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
  arXiv  Detail & Related papers  (2023-05-22T17:18:29Z)
• Embodied Agents for Efficient Exploration and Smart Scene Description [47.82947878753809]
  We tackle a setting for visual navigation in which an autonomous agent needs to explore and map an unseen indoor environment. We propose and evaluate an approach that combines recent advances in visual robotic exploration and image captioning on images. Our approach can generate smart scene descriptions that maximize semantic knowledge of the environment and avoid repetitions.
  arXiv  Detail & Related papers  (2023-01-17T19:28:01Z)
• A Systematic Survey of Text Worlds as Embodied Natural Language Environments [0.76146285961466]
  Text Worlds are virtual environments for embodied agents that, unlike 2D or 3D environments, are rendered exclusively using textual descriptions. These environments offer an alternative to higher-fidelity 3D environments due to their low barrier to entry.
  arXiv  Detail & Related papers  (2021-07-08T22:15:16Z)
• Mobile App Tasks with Iterative Feedback (MoTIF): Addressing Task Feasibility in Interactive Visual Environments [54.405920619915655]
  We introduce Mobile app Tasks with Iterative Feedback (MoTIF), a dataset with natural language commands for the greatest number of interactive environments to date. MoTIF is the first to contain natural language requests for interactive environments that are not satisfiable. We perform initial feasibility classification experiments and only reach an F1 score of 37.3, verifying the need for richer vision-language representations.
  arXiv  Detail & Related papers  (2021-04-17T14:48:02Z)
• Situated Language Learning via Interactive Narratives [16.67845396797253]
  This paper explores the question of how to imbue learning agents with the ability to understand and generate contextually relevant natural language. Two key components in creating such agents are interactivity and environment grounding. We discuss the unique challenges a text games' puzzle-like structure combined with natural language state-and-action spaces provides.
  arXiv  Detail & Related papers  (2021-03-18T01:55:16Z)
• Positioning yourself in the maze of Neural Text Generation: A Task-Agnostic Survey [54.34370423151014]
  This paper surveys the components of modeling approaches relaying task impacts across various generation tasks such as storytelling, summarization, translation etc. We present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges outstanding in the field in each of them.
  arXiv  Detail & Related papers  (2020-10-14T17:54:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.