The continuous technological advances have gradually surrounded people with a wide range of electronic devices and information technology. In this regard, it is necessary to develop intuitive interfaces and systems with some degree of intelligence, with the ability to recognize and respond to the needs of individuals in a discrete and often invisible way, considering people in the center of the development to create technologically complex and intelligent environments.
This paper describes ALZ-MAS; an Ambient Intelligence based multi-agent system aimed at enhancing the assistance and health care for Alzheimer patients in geriatric residences. Furthermore, this paper includes new changes made to this system regarding previous publications (Tapia & Corchado, 2007), including the integration of new wireless sensor devices to improve the system with new and better locating techniques.
Ambient Intelligence (AmI) is an emerging multidisciplinary area based on ubiquitous computing, which influences the design of protocols, communications, systems, devices, etc., proposing new ways of interaction between people and technology, adapting them to the needs of individuals and their environment (Weber, et al. 2005). AmI offers a great potential to improve quality of life and simplify the use of technology by offering a wider range of personalized services and providing users with easier and more efficient ways to communicate and interact with other people and systems (Weber, et al., 2005; Corchado, et al., 2008b).
However, the development of systems that clearly fulfill the needs of AmI is difficult and not always satisfactory. It requires a joint development of models, techniques and technologies based on services. An AmI-based system consists of a set of human actors and adaptive mechanisms which work together in a distributed way. Those mechanisms provide on demand personalized services and stimulate users through their environment according to specific situation characteristics (Weber, et al., 2005).
AI problems tend to be large. They are computationally complex and cannot be solved by straightforward algorithms. AI problems and their domains tend to embody a large amount of human expertise, especially if tackled by strong AI methods. Some types of problems are better solved using AI, whereas others are more suitable for traditional computer science approaches involving simple decision-making or exact computations to produce solutions.
Let us consider a few examples:
- Medical diagnosis
- Shopping using a cash register with bar code scanning
- ATMs As we become more familiar with AI and learn how it is distinct from traditional computer science, we must answer the question: "What makes a problem suitable for AI?" There are three characteristics that are common to most AI problems: Two person games such as chess and checkers