state-representation

An essay exploring knowledge representation in artificial intelligence, differentiating between model and state knowledge, and discussing the challenges of enabling computers to understand nuanced contexts like those in address books.

This paper investigates how action information can be incorporated into recurrent neural network architectures for reinforcement learning, examining design choices and empirically evaluating them across illustrative domains.

This paper proposes an epistemic state graph representation and an order-gap termination criterion for recursive reasoning systems, addressing how to manage evolving reasoning states and when to stop iteration.