Planning Agent for Geriatric Residences

J. Bajo, D. I. Tapia, S. Rodríguez, J. Corchado
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引用次数: 3

Abstract

Agents and Multi-Agent Systems (MAS) have become increasingly relevant for developing distributed and dynamic intelligent environments. The ability of software agents to act somewhat autonomously links them with living animals and humans, so they seem appropriate for discussion under nature-inspired computing (Marrow, 2000). This paper presents AGALZ (Autonomous aGent for monitoring ALZheimer patients), and explains how this deliberative planning agent has been designed and implemented. A case study is then presented, with AGALZ working with complementary agents into a prototype environment-aware multi-agent system (ALZ-MAS: ALZheimer Multi-Agent System) (Bajo, Tapia, De Luis, Rodriguez & Corchado, 2007). The elderly health care problem is studied, and the possibilities of Radio Frequency Identification (RFID) (Sokymat, 2006) as a technology for constructing an intelligent environment and ascertaining patient location to generate plans and maximize safety are examined. This paper focuses in the development of natureinspired deliberative agents using a Case-Based Reasoning (CBR) (Aamodt & Plaza, 1994) architecture, as a way to implement sensitive and adaptive systems to improve assistance and health care support for elderly and people with disabilities, in particular with Alzheimer. Agents in this context must be able to respond to events, take the initiative according to their goals, communicate with other agents, interact with users, and make use of past experiences to find the best plans to achieve goals, so we propose the development of an autonomous deliberative agent that incorporates a Case-Based Planning (CBP) mechanism, derivative from Case-Based Reasoning (CBR) (Bajo, Corchado & Castillo, 2006), specially designed for planning construction. CBP-BDI facilitates learning and adaptation, and therefore a greater degree of autonomy than that found in pure BDI (Believe, Desire, Intention) architecture (Bratman, 1987). BDI agents can be implemented by using different tools, such as Jadex (Pokahr, Braubach & Lamersdorf, 2003), dealing with the concepts of beliefs, goals and plans, as java objects that can be created and handled within the agent at execution time.
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长者住宅规划代理
智能体和多智能体系统(MAS)在开发分布式和动态智能环境中变得越来越重要。软件代理在某种程度上自主行动的能力将它们与活着的动物和人类联系起来,因此它们似乎适合在自然启发的计算下进行讨论(骨髓,2000)。本文提出了用于监测阿尔茨海默病患者的自主代理(AGALZ),并解释了这种审慎规划代理的设计和实现方法。然后介绍了一个案例研究,AGALZ与互补代理一起工作,形成一个环境感知的原型多代理系统(ALZ-MAS:阿尔茨海默病多代理系统)(Bajo, Tapia, De Luis, Rodriguez & Corchado, 2007)。研究了老年保健问题,并研究了射频识别(RFID) (Sokymat, 2006)作为构建智能环境和确定患者位置以生成计划和最大化安全性的技术的可能性。本文关注的是使用基于案例的推理(CBR) (Aamodt & Plaza, 1994)架构开发受自然启发的审议代理,作为实现敏感和自适应系统的一种方式,以改善对老年人和残疾人(特别是阿尔茨海默病患者)的援助和医疗支持。在这种情况下,智能体必须能够响应事件,根据自己的目标采取主动,与其他智能体进行沟通,与用户进行交互,并利用过去的经验找到实现目标的最佳计划,因此我们提出开发一种自主协商智能体,该智能体融合了基于案例推理(CBR)的基于案例的规划(CBP)机制(Bajo, Corchado & Castillo, 2006),专为规划构建而设计。CBP-BDI促进了学习和适应,因此比纯粹的BDI(信念、欲望、意图)架构有更大程度的自主性(Bratman, 1987)。BDI代理可以通过使用不同的工具来实现,例如Jadex (Pokahr, Braubach & Lamersdorf, 2003),将信念、目标和计划的概念作为java对象处理,这些对象可以在执行时在代理内部创建和处理。
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