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Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience
To address this problem, we bridge the literature on AI design and AI transparency to explore whether and how frameworks for transparent model reporting can support design ideation with pre-trained models.
RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents
We define precise syntax and grounding semantics for RLang, and provide a parser that grounds RLang programs to an algorithm-agnostic \textit{partial} world model and policy that can be exploited by an RL agent.
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