Controlling Behavior with Shared Knowledge
Author(s)
Peng, Xiangyu
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Abstract
Controlling agent behavior is a fundamental challenge across diverse domains within
artificial intelligence and robotics. The central idea of this dissertation is that shared
knowledge can be used as a powerful tool to control AI agents’ behavior. This dissertation
explores the utilization of shared knowledge in constructing coherent narratives and
enhancing the expression of shared knowledge in Reinforcement Learning agents.
In this dissertation, I first investigate the utilization of shared knowledge for constructing
narratives by developing a story-generation agent that emulates the cognitive processes of
how human readers create detailed mental models, referred to as the “reader model”,
which they use to understand and interpret stories with shared knowledge. Employing the
reader model has resulted in the generation of significantly more coherent and goal-directed
stories. I also explore how to input unique constraints into the story generator
allowing for the modification of the shared knowledge.
Subsequently, I delve into the application of shared knowledge in controlling reinforcement
learning agents through the introduction of a technique called “Story Shaping.” This
technique involves the agent inferring tacit knowledge from an exemplar story and
rewarding itself for actions that align with the inferred reader model. Following proposing
this agent, I propose the Thespian agent to leverage the knowledge learned in this technique
to adapt to the new environment under a few-shot setting. Additionally, I investigate the
potential of using shared knowledge to explain behavior by examining the impact of
symbolic knowledge graph-based state representation and Hierarchical Graph Attention
mechanism on the decision-making process of a reinforcement learning agent. The goal of
this dissertation is to create AI-driven systems that are more coherent, controllable, and
aligned with human expectations and preferences, thereby fostering trust and safety in
human-AI interactions.
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Date
2024-01-10
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Dissertation