Incorporating richer human inputs including qualitative constraints such as monotonic and synergistic influences has long been adapted inside AI. Inspired by this, we consider the problem of using such influence statements in the successful gradient-boosting framework. We develop a unified framework for both classification and regression settings that can both effectively and efficiently incorporate such constraints to accelerate learning to a better model. Our results in a large number of standard domains and two particularly novel real-world domains demonstrate the superiority of using domain knowledge rather than treating the human as a mere labeler.

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While AI planning and Reinforcement Learning (RL) solve sequential decision-making problems, they are based on different formalisms, which leads to a significant difference in their action spaces. When solving planning problems using RL algorithms, we have observed that a naive translation of the planning action space incurs severe degradation in sample complexity. In practice, those action spaces are often engineered manually in a domain-specific manner. In this abstract, we present a method that reduces the parameters of operators in AI planning domains by introducing a parameter seed set problem and casting it as a classical planning task. Our experiment shows that our proposed method significantly reduces the number of actions in the RL environments originating from AI planning domains.

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