  Controlled natural languages (CNLs) are effective languages for knowledge
representation and reasoning. They are designed based on certain natural
languages with restricted lexicon and grammar. CNLs are unambiguous and simple
as opposed to their base languages. They preserve the expressiveness and
coherence of natural languages. In this report, we focus on a class of CNLs,
called machine-oriented CNLs, which have well-defined semantics that can be
deterministically translated into formal languages, such as Prolog, to do
logical reasoning. Over the past 20 years, a number of machine-oriented CNLs
emerged and have been used in many application domains for problem solving and
question answering. However, few of them support non-monotonic inference. In
our work, we propose non-monotonic extensions of CNL to support defeasible
reasoning.
  In the first part of this report, we survey CNLs and compare three
influential systems: Attempto Controlled English (ACE), Processable English
(PENG), and Computer-processable English (CPL). We compare their language
design, semantic interpretations, and reasoning services. In the second part of
this report, we first identify typical non-monotonicity in natural languages,
such as defaults, exceptions and conversational implicatures. Then, we propose
their representation in CNL and the corresponding formalizations in a form of
defeasible reasoning known as Logic Programming with Defaults and Argumentation
Theory (LPDA).
