Learning with Interactive Knowledge Representations

Multiple Interactive Representations for Fractions Learning

Multiple External Representations (MERs) have been used successfully in instructional activities, including fractions. However, students often have difficulties making the connections between the MERs spontaneously. We argue that interactive fraction representations may help students in discovering relevant features and relating the MERs to one another. Support for guiding student interaction i...

Learning Visual Representations for Interactive Systems

We describe two quite different methods for associating action parameters to visual percepts. Our RLVC algorithm performs reinforcement learning directly on the visual input space. To make this very large space manageable, RLVC interleaves the reinforcement learner with a supervised classification algorithm that seeks to split perceptual states so as to reduce perceptual aliasing. This results ...

Coevolving di erent knowledge representations with ne - grained parallel Learning

Learning Word Representations with Regularization from Prior Knowledge

Conventional word embeddings are trained with specific criteria (e.g., based on language modeling or co-occurrence) inside a single information source, disregarding the opportunity for further calibration using external knowledge. This paper presents a unified framework that leverages pre-learned or external priors, in the form of a regularizer, for enhancing conventional language model-based e...

Operationalizing Operational Logics: Semiotic Knowledge Representations for Interactive Systems

All projects in AI begin by selecting or devising knowledge representations suitable for the project’s functional requirements. Interactive systems (including games) have semiotic considerations on top of their functional requirements: they must be legible to users, players, and their own designers. AI working within or around interactive systems must acknowledge and support the concerns of hum...