The article discusses advancements in text-based reinforcement learning (TBGs) where agents operate in environments using only natural language for observations. This creates a complex framework akin to Partially Observable Markov Decision Processes (POMDPs). The paper emphasizes the role of Inductive Logic Programming (ILP) in forming comprehensible rules that allow for easy adaptation and understanding. The use of the s(CASP) solver in combination with ILP techniques demonstrates an innovative approach in automating decision-making processes while maintaining human interpretability in learned models.
In text-based reinforcement learning, agents perceive game states solely through natural language, challenging conventional methods of action representation and interaction.
Inductive Logic Programming facilitates the creation of human-readable logic rules, enabling incremental knowledge adaptation and user comprehension of learned models.
#text-based-reinforcement-learning #inductive-logic-programming #natural-language-processing #symbolic-ai #decision-making
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