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Explanation-Based Learning for Planning

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Encyclopedia of Machine Learning and Data Mining

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Synonyms

Explanation-based generalization for planning; Speedup learning for planning

Definition

Explanation-based learning (EBL) involves using prior knowledge to explain (“prove”) why the training example has the label it is given and using this explanation to guide the learning. Since the explanations are often able to pinpoint the features of the example that justify its label, EBL techniques are able to get by with much fewer number of training examples. On the flip side, unlike general classification learners, EBL requires prior knowledge (aka “domain theory/model”) in addition to labeled training examples – a requirement that is not easily met in some scenarios. Since many planning and problem-solving agents do start with declarative domain theories (consisting at least of descriptions of actions along with their preconditions and effects), EBL has been a popular learning technique for planning.

Dimensions of Variation

The application of EBL in planning varies along several...

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Author information

Authors and Affiliations

  1. Arizona State University, Tempe, AZ, USA

    Subbarao Kambhampati

  2. MapR, San Jose, CA, USA

    Sungwook Yoon

Authors
  1. Subbarao Kambhampati
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  2. Sungwook Yoon
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Corresponding author

Correspondence to Subbarao Kambhampati .

Editor information

Editors and Affiliations

  1. The University of New South Wales, Sydney, NSW, Australia

    Claude Sammut

  2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

    Geoffrey I. Webb

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Kambhampati, S., Yoon, S. (2017). Explanation-Based Learning for Planning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_97

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