Knowledge-Enhanced Agents for Interactive Text Games
Introduction:
Communication through natural language is crucial to machine intelligence [9]. The recent progress in computational language models (LMs) has enabled strong performance on tasks with limited interaction, like question-answering and procedural text understanding [10]. Recognizing that interactivity is an essential aspect of communication, the community has turned its attention towards training and evaluating agents in interactive fiction (IF) environments, like text-based games, which provide a unique testing ground for investigating the reasoning abilities of LMs and the potential for Artificial Intelligence (AI) agents to perform multi-step real-world tasks in a constrained environment. For instance, in Figure 1, an agent must pick a fruit in the living room and place it in a blue box in the kitchen. In these games, agents navigate complex environments using text-based inputs, which demands a sophisticated understanding of natural language and strategic decision-making from AI agents. To succeed in these games, agents must manage their knowledge, reason, and generate language-based actions that produce desired and predictable changes in the game world.
Background and Motivation:
Prior work has shown that Reinforcement Learning- and Language Model-based agents struggle to reason about or to explain science concepts in IF environments [1], which raises questions about these models' ability to generalize to unseen situations beyond what has been observed during training [2]. For example, while tasks such as ‘retrieving a known substance's melting (or boiling) point‘ may be relatively simple, ‘determining an unknown substance's melting (or boiling) point in a specific environment‘ can be challenging for these models. To improve generalization, it may be effective to incorporate world knowledge, e.g., about object affordances, yet no prior work has investigated this direction. In addition, existing models struggle to learn effectively from environmental feedback. For instance, when examining the conductivity of a specific substance, the agent must understand that it has already obtained the necessary wires and the particular substance so that it then proceeds to locate a power source. Therefore, there is a need for a framework that can analyze and evaluate the effectiveness of different types of knowledge and knowledge-injection methods for text-based game agents.
Our paper, "Knowledge-enhanced Agents for Interactive Text Games," introduces a novel framework to enhance AI agents' performance in these IF environments.
Published Version: https://dl.acm.org/doi/10.1145/3587259.3627561
We are proud to announce that our paper has been awarded the Best Student Paper at the KCAP 2023 Conference, a testament to our team's innovative research and dedication.
