Automated case generation using a genetic algorithm

Borck, Hayley; Boddy, Mark S.

doi:10.1145/3067695.3075603

Cited by 2 publications

(1 citation statement)

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“…Retention happens after classification, adding the newest observed problem and its recognized solution to the CB. To manage the size of the CB as it grows, we consider the novelty and benefit of adding the case to the CB (Borck and Boddy 2017). This will trim the CB when initialized and regulate which new cases are added, reducing bias that stems from too many similar cases.…”

Section: Case-based Reasoningmentioning

confidence: 99%

A Symbolic Representation of Human Posture for Interpretable Learning and Reasoning

Freedman¹,

Mueller²,

Ladwig³

et al. 2022

Preprint

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Robots that interact with humans in a physical space or application need to think about the person's posture, which typically comes from visual sensors like cameras and infra-red. Artificial intelligence and machine learning algorithms use information from these sensors either directly or after some level of symbolic abstraction, and the latter usually partitions the range of observed values to discretize the continuous signal data. Although these representations have been effective in a variety of algorithms with respect to accuracy and task completion, the underlying models are rarely interpretable, which also makes their outputs more difficult to explain to people who request them. Instead of focusing on the possible sensor values that are familiar to a machine, we introduce a qualitative spatial reasoning approach that describes the human posture in terms that are more familiar to people. This paper explores the derivation of our symbolic representation at two levels of detail and its preliminary use as features for interpretable activity recognition.

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Section: Case-based Reasoningmentioning

confidence: 99%