In a previous paper, we have proposed a set of concepts, axiom schemata and
algorithms that can be used by agents to learn to describe their behaviour,
goals, capabilities, and environment. The current paper proposes a new set of
concepts, axiom schemata and algorithms that allow the agent to learn new
descriptions of an observed behaviour (e.g., perplexing actions), of its actor
(e.g., undesired propositions or actions), and of its environment (e.g.,
incompatible propositions). Each learned description (e.g., a certain action
prevents another action from being performed in the future) is represented by a
relationship between entities (either propositions or actions) and is learned
by the agent, just by observation, using domain-independent axiom schemata and
or learning algorithms. The relations used by agents to represent the
descriptions they learn were inspired on the Theory of Rhetorical Structure
(RST). The main contribution of the paper is the relation family Although,
inspired on the RST relation Concession. The accurate definition of the
relations of the family Although involves a set of deontic concepts whose
definition and corresponding algorithms are presented. The relations of the
family Although, once extracted from the agent's observations, express surprise
at the observed behaviour and, in certain circumstances, present a
justification for it.
The paper shows results of the presented proposals in a demonstration
scenario, using implemented software.

该研究提出了一种新的概念、公理模式和算法，使智能体能够学习描述其行为、目标、能力和环境，并通过观察学习新的行为描述，代之以感性的关系，这些关系被称为 Although 关系，并利用修辞结构理论，结果证明了该算法在采用实施软件的演示场景中是有效的。