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Related Experiment Video

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Automated Interactive Video Playback for Studies of Animal Communication

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

Situated language learning via interactive narratives.

Prithviraj Ammanabrolu¹, Mark O Riedl¹

¹School of Interactive Computing, Georgia Institute of Technology, 756 W Peachtree St NW, Atlanta, GA, USA.

Patterns (New York, N.Y.)

|September 23, 2021

Summary

This research explores training AI agents to understand and generate language for goal achievement. Interactive narratives are proposed as key environments for grounding language and improving AI reasoning and collaboration.

Keywords:

artificial intelligence interactive narratives 2021 MSC: 00-01 99-00 natural language processing reinforcement learning

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Area of Science:

Artificial Intelligence
Natural Language Processing
Cognitive Science

Background:

Learning agents require contextual language understanding for goal achievement.
Human language acquisition relies on interactivity and environment grounding.

Purpose of the Study:

To provide a roadmap for imbuing learning agents with contextual language abilities.
To explore interactive narratives as optimal training environments for AI agents.
To address challenges in knowledge representation, common-sense reasoning, and exploration within text-based environments.

Main Methods:

Utilizing interactive narratives as simulated environments for agent training.
Developing agents that perceive, act, and communicate using natural language.
Analyzing the intersection of natural language processing, storytelling, and sequential decision-making.

Main Results:

Interactive narratives present unique challenges including knowledge representation and common-sense reasoning.
Progress in interactive narratives has applications in AI evaluation, multi-modal adaptation, and human-AI collaboration.

Conclusions:

Interactive narratives are crucial for developing sophisticated language-understanding AI agents.
Further research can extend these methods to evaluate AI common-sense, integrate vision, and foster multi-agent collaboration.