  Estimating the internal state of a robotic system is complex: this is
performed from multiple heterogeneous sensor inputs and knowledge sources.
Discretization of such inputs is done to capture saliences, represented as
symbolic information, which often presents structure and recurrence. As these
sequences are used to reason over complex scenarios, a more compact
representation would aid exactness of technical cognitive reasoning
capabilities, which are today constrained by computational complexity issues
and fallback to representational heuristics or human intervention. Such
problems need to be addressed to ensure timely and meaningful human-robot
interaction. Our work is towards understanding the variability of learning
informativeness when training on subsets of a given input dataset. This is in
view of reducing the training size while retaining the majority of the symbolic
learning potential. We prove the concept on human-written texts, and conjecture
this work will reduce training data size of sequential instructions, while
preserving semantic relations, when gathering information from large remote
sources.
