15 research outputs found
Learning to Speak and Act in a Fantasy Text Adventure Game
We introduce a large scale crowdsourced text adventure game as a research
platform for studying grounded dialogue. In it, agents can perceive, emote, and
act whilst conducting dialogue with other agents. Models and humans can both
act as characters within the game. We describe the results of training
state-of-the-art generative and retrieval models in this setting. We show that
in addition to using past dialogue, these models are able to effectively use
the state of the underlying world to condition their predictions. In
particular, we show that grounding on the details of the local environment,
including location descriptions, and the objects (and their affordances) and
characters (and their previous actions) present within it allows better
predictions of agent behavior and dialogue. We analyze the ingredients
necessary for successful grounding in this setting, and how each of these
factors relate to agents that can talk and act successfully
Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models
Current dialogue research primarily studies pairwise (two-party)
conversations, and does not address the everyday setting where more than two
speakers converse together. In this work, we both collect and evaluate
multi-party conversations to study this more general case. We use the LIGHT
environment to construct grounded conversations, where each participant has an
assigned character to role-play. We thus evaluate the ability of language
models to act as one or more characters in such conversations. Models require
two skills that pairwise-trained models appear to lack: (1) being able to
decide when to talk; (2) producing coherent utterances grounded on multiple
characters. We compare models trained on our new dataset to existing
pairwise-trained dialogue models, as well as large language models with
few-shot prompting. We find that our new dataset, MultiLIGHT, which we will
publicly release, can help bring significant improvements in the group setting
How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds
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
Generating Interactive Worlds with Text
Procedurally generating cohesive and interesting game environments is
challenging and time-consuming. In order for the relationships between the game
elements to be natural, common-sense has to be encoded into arrangement of the
elements. In this work, we investigate a machine learning approach for world
creation using content from the multi-player text adventure game environment
LIGHT. We introduce neural network based models to compositionally arrange
locations, characters, and objects into a coherent whole. In addition to
creating worlds based on existing elements, our models can generate new game
content. Humans can also leverage our models to interactively aid in
worldbuilding. We show that the game environments created with our approach are
cohesive, diverse, and preferred by human evaluators compared to other machine
learning based world construction algorithms