一种智能系统评估体系结构

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摘要翻译：
人工智能的主要研究领域之一是智能体（程序）的编码，这些智能体（程序）能够在任何情况下自我学习。这意味着代理必须用于它们被创建的目的以外的目的，例如，下棋。通过这种方式，我们试图更接近人工智能的原始目标。判断一个智能体是否真正智能的一个问题是其智能性的度量，因为目前还没有一种可靠的方法来度量它。这个项目的目的是创建一个解释器，允许执行几个环境，包括那些随机生成的环境，以便代理（一个人或一个程序）可以与它们交互。一旦agent与环境的交互结束，解释器将根据agent在测试过程中在环境中所经历的动作、状态和回报来衡量agent的智能。因此，我们将能够在任何可能的环境中测量Agent的智能，并在几个Agent之间进行比较，以确定其中哪一个是最智能的。为了执行测试，解释器必须能够随机生成对测量代理智能真正有用的环境，因为没有任何随机生成的环境可以用于此目的。
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英文标题：
《An architecture for the evaluation of intelligent systems》
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作者：
Javier Insa-Cabrera, Jose Hernandez-Orallo
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  One of the main research areas in Artificial Intelligence is the coding of agents (programs) which are able to learn by themselves in any situation. This means that agents must be useful for purposes other than those they were created for, as, for example, playing chess. In this way we try to get closer to the pristine goal of Artificial Intelligence. One of the problems to decide whether an agent is really intelligent or not is the measurement of its intelligence, since there is currently no way to measure it in a reliable way. The purpose of this project is to create an interpreter that allows for the execution of several environments, including those which are generated randomly, so that an agent (a person or a program) can interact with them. Once the interaction between the agent and the environment is over, the interpreter will measure the intelligence of the agent according to the actions, states and rewards the agent has undergone inside the environment during the test. As a result we will be able to measure agents' intelligence in any possible environment, and to make comparisons between several agents, in order to determine which of them is the most intelligent. In order to perform the tests, the interpreter must be able to randomly generate environments that are really useful to measure agents' intelligence, since not any randomly generated environment will serve that purpose.
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PDF链接：
https://arxiv.org/pdf/1102.0714

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关键词：Intelligence Environments Architecture Presentation environment 进行 will 环境 代理 intelligent