  Commonsense reasoning has long been considered as one of the holy grails of
artificial intelligence. Most of the recent progress in the field has been
achieved by novel machine learning algorithms for natural language processing.
However, without incorporating logical reasoning, these algorithms remain
arguably shallow. With some notable exceptions, developers of practical
automated logic-based reasoners have mostly avoided focusing on the problem.
The paper argues that the methods and algorithms used by existing automated
reasoners for classical first-order logic can be extended towards commonsense
reasoning. Instead of devising new specialized logics we propose a framework of
extensions to the mainstream resolution-based search methods to make these
capable of performing search tasks for practical commonsense reasoning with
reasonable efficiency. The proposed extensions mostly rely on operating on
ordinary proof trees and are devised to handle commonsense knowledge bases
containing inconsistencies, default rules, taxonomies, topics, relevance,
confidence and similarity measures. We claim that machine learning is best
suited for the construction of commonsense knowledge bases while the extended
logic-based methods would be well-suited for actually answering queries from
these knowledge bases.
