Intelligent agents solving problems in the real world require domain models containing widespread knowledge of the world. Domain models can be encoded by human experts or automatically learned through the observation of some existing plans (behaviours). Encoding a domain model manually from experience and intuition is a very complex and time-consuming task, even for domain experts. This chapter investigates various classical and state-of-the-art methods proposed by the researchers to attain the ability of automatic learning of domain models from training data. This concerns with the learning and representation of knowledge about the operator schema, discrete or continuous resources, processes and events involved in the planning domain model. The taxonomy and order of these methods we followed are based on their standing and frequency of usage in the past research. Our intended contribution in this chapter is to provide a broader perspective on the range of techniques in the domain model learning area which underpin the developmental decisions of the learning tools.
|Title of host publication||Knowledge Engineering Tools and Techniques for AI Planning|
|Editors||Mauro Vallati, Diane Kitchin|
|Place of Publication||Cham|
|Number of pages||26|
|ISBN (Print)||9783030385606, 9783030385637|
|Publication status||Published - 26 Mar 2021|