Reliable agnostic learning

A multivariate time-series forecasting has great potentials in various domains. However, it is challenging to find dependency structure among the time-series variables and appropriate time-lags for each variable, which change dynamically over time. In this study, I suggest partial correlation-based attention mechanism which overcomes the shortcomings of existing pair-wise comparisons-based attention mechanisms. Moreover, I propose data-driven series-wise multi-resolution convolutional layers to represent the input time-series data for domain agnostic learning.

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Humanoid infers Archimedes' principle: understanding physical relations and object affordances through cumulative learning experiences

Journal of The Royal Society Interface ◽

10.1098/rsif.2016.0310 ◽

2016 ◽

Vol 13 (120) ◽

pp. 20160310 ◽

Cited By ~ 6

Author(s):

Ajaz Ahmad Bhat ◽

Vishwanathan Mohan ◽

Giulio Sandini ◽

Pietro Morasso

Keyword(s):

Status Quo ◽

Causal Learning ◽

Long Term Memory ◽

Natural Agents ◽

Agnostic Learning ◽

Learning Rules ◽

Novel Objects ◽

Cumulative Learning ◽

Past Experiences

Emerging studies indicate that several species such as corvids, apes and children solve ‘The Crow and the Pitcher’ task (from Aesop's Fables) in diverse conditions. Hidden beneath this fascinating paradigm is a fundamental question: by cumulatively interacting with different objects, how can an agent abstract the underlying cause–effect relations to predict and creatively exploit potential affordances of novel objects in the context of sought goals? Re-enacting this Aesop's Fable task on a humanoid within an open-ended ‘learning–prediction–abstraction’ loop, we address this problem and (i) present a brain-guided neural framework that emulates rapid one-shot encoding of ongoing experiences into a long-term memory and (ii) propose four task-agnostic learning rules (elimination, growth, uncertainty and status quo) that correlate predictions from remembered past experiences with the unfolding present situation to gradually abstract the underlying causal relations. Driven by the proposed architecture, the ensuing robot behaviours illustrated causal learning and anticipation similar to natural agents. Results further demonstrate that by cumulatively interacting with few objects, the predictions of the robot in case of novel objects converge close to the physical law, i.e. the Archimedes principle: this being independent of both the objects explored during learning and the order of their cumulative exploration.

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