Towards reasoning based representations: Deep Consistence Seeking Machine

A. Lőrincz, M. Csákvári, Fóthi, Z. Milacski, A. Sárkány, Z. Tősér

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Machine learning is making substantial progress in diverse applications. The success is mostly due to advances in deep learning. However, deep learning can make mistakes and its generalization abilities to new tasks are questionable. We ask when and how one can combine network outputs, when (i) details of the observations are evaluated by learned deep components and (ii) facts and rules are available. The Deep Consistence Seeking (DCS) machine seeks for consistent and deterministic event descriptions and improves the representation accordingly. The machine has an anomaly detection component that may trigger coherence seeking. Coherence seeking resolves conflicts between computational modules by preferring components with higher scores. We illustrate that context can help in correcting recognitions and in deriving training samples for self-training. We put these concepts into a general framework of cognition, by distinguishing creativity, rule extraction, verification, and symbol grounding. We demonstrate our approach in a driving scenario.

Original languageEnglish
Pages (from-to)92-108
Number of pages17
JournalCognitive Systems Research
Volume47
DOIs
Publication statusPublished - Jan 1 2018

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Keywords

  • Communication
  • Complexity
  • Deep learning
  • Episodic description
  • Recognition by components
  • Rule-based system

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Cognitive Neuroscience
  • Artificial Intelligence

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