Jérémy Lemée, Danai Vachtsevanou, Simon Mayer, Andrei Ciortea
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引用次数: 0
摘要
生态心理学家詹姆斯-吉布森(James J. Gibson)定义了 "可负担性"(affordances)这一概念,指的是环境为动物提供的行动可能性。在本文中,我们将展示(人工)代理如何在多代理系统(MAS)环境中发现并利用可负担性来实现其目标。为了向代理指明其所处环境中存在哪些可负担性,以及这些可负担性是否有可能帮助代理实现其目标,环境可以在考虑到环境和代理当前情况的情况下暴露出标志物。在此基础上,我们定义了一种标识符暴露机制,由环境来计算哪些标识符应暴露给代理,以便让代理只感知可能与其相关的负担能力信息,从而提高其交互效率。如果这样做成功的话,代理就能更有效地与部分可观测环境进行交互,因为标识符指明了他们可以利用的能力,以达到特定目的。因此,标识符有助于探索和利用 MAS 环境。在超媒体多代理系统的背景下,介绍了标识符和标识符暴露机制的实施,并通过一个场景的开发介绍了这种方法的实用性。
Signifiers for conveying and exploiting affordances: from human-computer interaction to multi-agent systems
The ecological psychologist James J. Gibson defined the notion of affordances to refer to what action possibilities environments offer to animals. In this paper, we show how (artificial) agents can discover and exploit affordances in a Multi-Agent System (MAS) environment to achieve their goals. To indicate to agents what affordances are present in their environment and whether it is likely that these may help the agents to achieve their objectives, the environment may expose signifiers while taking into account the current situation of the environment and of the agent. On this basis, we define a Signifier Exposure Mechanism that is used by the environment to compute which signifiers should be exposed to agents in order to permit agents to only perceive information about affordances that are likely to be relevant to them, and thereby increase their interaction efficiency. If this is successful, agents can interact with partially observable environments more efficiently because the signifiers indicate the affordances they can exploit towards given purposes. Signifiers thereby facilitate the exploration and the exploitation of MAS environments. Implementations of signifiers and of the Signifier Exposure Mechanism are presented within the context of a Hypermedia Multi-Agent System, and the utility of this approach is presented through the development of a scenario.
期刊介绍:
Annals of Mathematics and Artificial Intelligence presents a range of topics of concern to scholars applying quantitative, combinatorial, logical, algebraic and algorithmic methods to diverse areas of Artificial Intelligence, from decision support, automated deduction, and reasoning, to knowledge-based systems, machine learning, computer vision, robotics and planning.
The journal features collections of papers appearing either in volumes (400 pages) or in separate issues (100-300 pages), which focus on one topic and have one or more guest editors.
Annals of Mathematics and Artificial Intelligence hopes to influence the spawning of new areas of applied mathematics and strengthen the scientific underpinnings of Artificial Intelligence.