Towards collaborative dialogue in Minecraft
type: CreativeWork
description: This dissertation describes our work in building interactive agents that can communicate with humans to collaboratively solve tasks in grounded scenarios. To investigate the challenges of building such agents, we define a novel instantiation of a situated, Minecraft-based, Collaborative Building Task in which one player (A, the Architect) is shown a target structure, denoted Target, and needs to instruct the other player (B, the Builder) to build a copy of this structure, denoted Built, in a predefined build region. While both players can interact asynchronously via a chat interface, we define the roles to be asymmetric: A can observe B and Target, but is invisible and cannot place blocks; meanwhile, B can freely place and remove blocks, but has no explicit knowledge of the target structure. Each agent requires a different set of abilities in order to be successful at this task: specifically, A's main challenges arise in the task of generating situated instructions by comparing Built and Target, while B's responsibilities focus mainly on comprehending A's situated instructions using both dialogue and world context. Both agents must be able to interact asynchronously in an evolving dialogue context and a dynamic world state within which they are embodied. In this work, we specifically examine how well end-to-end neural models can learn to be instruction givers (i.e., Architects) from a limited amount of real human-human data. In order to examine how humans complete the Collaborative Building Task, as well as use human-human data as a gold standard for training and evaluating models, we present the Minecraft Dialogue Corpus, a collection of 509 conversations and game logs. We then introduce baseline models for the challenging subtask of Architect utterance generation, and evaluate them offline, using both automated metrics and human evaluation. We show that while conditioning our model on a simple representation of the world gives our model improved ability to generat…
abstract: This dissertation describes our work in building interactive agents that can communicate with humans to collaboratively solve tasks in grounded scenarios. To investigate the challenges of building such agents, we define a novel instantiation of a situated, Minecraft-based, Collaborative Building Task in which one player (A, the Architect) is shown a target structure, denoted Target, and needs to instruct the other player (B, the Builder) to build a copy of this structure, denoted Built, in a predefined build region. While both players can interact asynchronously via a chat interface, we define the roles to be asymmetric: A can observe B and Target, but is invisible and cannot place blocks; meanwhile, B can freely place and remove blocks, but has no explicit knowledge of the target structure. Each agent requires a different set of abilities in order to be successful at this task: specifically, A's main challenges arise in the task of generating situated instructions by comparing Built and Target, while B's responsibilities focus mainly on comprehending A's situated instructions using both dialogue and world context. Both agents must be able to interact asynchronously in an evolving dialogue context and a dynamic world state within which they are embodied. In this work, we specifically examine how well end-to-end neural models can learn to be instruction givers (i.e., Architects) from a limited amount of real human-human data. In order to examine how humans complete the Collaborative Building Task, as well as use human-human data as a gold standard for training and evaluating models, we present the Minecraft Dialogue Corpus, a collection of 509 conversations and game logs. We then introduce baseline models for the challenging subtask of Architect utterance generation, and evaluate them offline, using both automated metrics and human evaluation. We show that while conditioning our model on a simple representation of the world gives our model improved ability to generat…
author: Narayan-Chen, Anjali Yuan
language: en
keywords: artificial intelligence, natural language processing, machine learning, deep learning, conversational AI, situated dialogue, natural language generation
date published: 2021-03-05T21:38:12Z
date modified: 2026-07-22T22:24:50Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois at Urbana-Champaign

PropertyValue
property ID: Handle
value: http://hdl.handle.net/2142/109404