Planning Agent for Geriatric Residences

Planning Agent for Geriatric Residences

Javier Bajo, Dante I. Tapia, Sara Rodríguez, Juan M. Corchado
Copyright: © 2009 |Pages: 7
DOI: 10.4018/978-1-59904-849-9.ch193
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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, Rodríguez & 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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During the last three decades the number of Europeans over 60 years old has risen by about 50%. Today they represent more than 25% of the population and it is estimated that in 20 years this percentage will rise to one third of the population, meaning 100 millions of citizens (Camarinha-Matos & Afsarmanesh, 2002). This situation is not exclusive to Europe, since studies in other parts of the world show similar tendencies (Camarinha-Matos & Afsarmanesh, 2002). The importance of developing new and more reliable ways to provide care and support to the elderly is underlined by this trend (Camarinha-Matos & Afsarmanesh, 2002), and the creation of secure, unobtrusive and adaptable environments for monitoring and optimizing health care will become vital. Some authors (Nealon & Moreno, 2003) consider that tomorrow’s health care institutions will be equipped with intelligent systems capable of interacting with humans. Multi-agent systems and architectures based on intelligent devices have recently been explored as supervision systems for medical care for the elderly or Alzheimer patients, aimed to support them in all aspects of daily life, predicting potential hazardous situations and delivering physical and cognitive support.

RFID technology is a wireless technology used to identify and receive information on the move. An RFID system contains basically four components: tags, readers, antennas and software (Sokymat, 2006). The configuration used in the system presented in this paper consists of 125KHZ transponders mounted on bracelets worn on the patient’s wrist or ankle, several readers installed over protected zones, with up to 2 meters capture range, and a central computer where all the ID numbers sent by the readers is processed.

Key Terms in this Chapter

Ambient Intelligence (AmI): Refers to electronic environments that are sensitive and responsive to context and people needs and characteristics. It is characterized by systems and technologies that are embedded, context-aware, ubiquitous, non intrusive, personalized, adaptive and anticipatory.

Case-Based Reasoning: A type of reasoning based on the use of past experiences. The purpose of CBR systems is to solve new problems by adapting solutions that have been used to solve similar problems in the past. The main concept when working with CBR is the concept of case, which can be defined as a past experience.

CBP-BDI: A deliberative BDI agent specialized in generating plans. It incorporates a Case-Based Planning mechanism.

CBR-BDI: A deliberative BDI agent that incorporates a CBR motor as reasoning mechanism.

Radio Frequency Identification: A wireless technology used to identify and receive information on the move. An RFID system contains basically four components: tags, readers, antennas and software.

Multi-Agent System: A system composed of several intelligent autonomous agents, collectively capable of reaching goals solving problems in a distributed way.

Case-Based Planning: A specialization of Case-Based Reasoning in which the solution proposed by the system is a plan (a sequence of actions).

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