内涵人工智能：从符号涌现到可解释和移情AI
Intensional Artificial Intelligence: From Symbol Emergence to Explainable and Empathetic AI
摘要：
我们认为，可解释的人工智能必须为其决策提供理由，能够推断观察到的行为的目的，并能够在受众理解和意图的背景下解释其决策。为了解决这些问题，我们提出了四个新的贡献。首先，我们定义了一个任意任务的感知状态，并讨论了两个极端的域的可能的解决方案。其次，我们定义了内涵解。根据某些智能定义，它描述了任务的目的。拥有它的智能体有一个根据该目的做出决定的理由，用基于硬件的感知符号系统来表达。第三，为了传达这种基本原理，需要自然语言，一种编码和解码感知状态的手段。我们提出了一个理论的意义，在其中，获得语言，代理人应该模拟世界的语言描述，而不是语言本身。如果人类的话语对智能体的目标具有预测价值，那么智能体将根据自己的目标和感知状态赋予这些话语以意义。在皮尔士符号学的背景下，一个共同体的代理人必须共享粗略的近似符号，所指对象和解释，以便沟通。意义只存在于意图的语境中，因此要与人类交流，代理人必须具有可比的经验和目标。一个学习内涵解决方案的智能体，在某种程度上类似于人类动机（如饥饿和痛苦）的目标函数的驱使下，可能不仅能够根据自己的意图，而且能够根据受众的理解和意图来解释其基本原理。它形成了人类感知状态的某种近似。
英文摘要：
We argue that an explainable artificial intelligence must possess a rationale for its decisions, be able to infer the purpose of observed behaviour, and be able to explain its decisions in the context of what its audience understands and intends. To address these issues we present four novel contributions. Firstly, we define an arbitrary task in terms of perceptual states, and discuss two extremes of a domain of possible solutions. Secondly, we define the intensional solution. Optimal by some definitions of intelligence, it describes the purpose of a task. An agent possessed of it has a rationale for its decisions in terms of that purpose, expressed in a perceptual symbol system grounded in hardware. Thirdly, to communicate that rationale requires natural language, a means of encoding and decoding perceptual states. We propose a theory of meaning in which, to acquire language, an agent should model the world a language describes rather than the language itself. If the utterances of humans are of predictive value to the agent's goals, then the agent will imbue those utterances with meaning in terms of its own goals and perceptual states. In the context of Peircean semiotics, a community of agents must share rough approximations of signs, referents and interpretants in order to communicate. Meaning exists only in the context of intent, so to communicate with humans an agent must have comparable experiences and goals. An agent that learns intensional solutions, compelled by objective functions somewhat analogous to human motivators such as hunger and pain, may be capable of explaining its rationale not just in terms of its own intent, but in terms of what its audience understands and intends. It forms some approximation of the perceptual states of humans.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
