Agent-Based Model Characterization Using Natural Language Processing

J. Padilla, David Shuttleworth, Kevin O'Brien
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引用次数: 7

Abstract

This paper reports on Natural Language Processing (NLP) as a technique to analyze phenomena towards specifying agent-based models (ABM). The objective of the ABM NLP Analyzer is to facilitate nonsimulationists to actively engage in the learning and collaborative designing of ABMs. The NLP model identifies candidate agents, candidate agent attributes, and candidate rules all of which non-simulationists can later evaluate for feasibility. IBM’s Watson Natural Language Understanding (NLU) and Knowledge Studio were used in order to annotate, evaluate, extract agents, agent attributes, and agent rules from unstructured descriptions of phenomena. The software, and related agent-attribute-rule characterization, provides insight into a simple but useful means of conceptualizing and specifying baseline ABMs. Further, it emphasizes on how to approach the design of ABMs without the use of NLP by focusing on the identification of agent, attributes and rules.
使用自然语言处理的基于agent的模型表征
本文报道了自然语言处理(NLP)作为一种分析现象的技术,用于指定基于智能体的模型(ABM)。ABM NLP分析器的目标是促进非仿真者积极参与ABM的学习和协作设计。NLP模型识别候选代理、候选代理属性和候选规则,所有这些非仿真者都可以稍后评估其可行性。使用IBM的沃森自然语言理解(NLU)和Knowledge Studio来从现象的非结构化描述中注释、评估、提取代理、代理属性和代理规则。该软件以及相关的代理-属性-规则特征,提供了一种简单但有用的概念化和指定基线abm的方法。此外,它强调了如何在不使用NLP的情况下通过关注代理,属性和规则的识别来接近abm的设计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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