Ambient intelligence (AmI) is intrinsically and thoroughly connected with artificial intelligence (AI). Some even say that it is, in essence, AI in the environment. AI, on the other hand, owes its success to the phenomenal development of the information and communication technologies (ICTs), based on principles such as Moore's law. In this paper we give an overview of the progress in AI and AmI interconnected with ICT through information-society laws, superintelligence, and several related disciplines, such as multi-agent systems and the Semantic Web, ambient assisted living and e-healthcare, AmI for assisting medical diagnosis, ambient intelligence for e-learning and ambient intelligence for smart cities. Besides a short history and a description of the current state, the frontiers and the future of AmI and AI are also considered in the paper.

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Abstract-Wireless sensor networks are widely adopted in many location-sensitive applications including disaster management, environmental monitoring, military applications where the precise estimation of each node position is inevitably important when the absolute positions of a relatively small portion as anchor nodes of the underlying network were predetermined. Intrinsically, localization is an unconstrained optimization problem based on various distance/path measures. Most of the existing localization methods focus on using different heuristic-based or mathematical techniques to increase the precision in position estimation. However, there were recent studies showing that nature-inspired algorithms like the ant-based or genetic algorithms can effectively solve many complex optimization problems. In this paper, we propose to adapt an evolutionary approach, namely a micro-genetic algorithm, as a post-optimizer into some existing localization methods such as the Ad-hoc Positioning System (APS) to further improve the accuracy of their position estimation. Obviously, our proposed MGA is highly adaptable and easily integrated into other localization methods. Furthermore, the remarkable improvements attained by our proposed MGA on both isotropic and anisotropic topologies of our simulation tests prompt for several interesting directions for further investigation.

Most existing e-learning systems strictly require the course instructors to explicitly input the pre-requisite requirements and/or some relationship measures between the involved concepts/modules such that an optimal learning path as a sequence of the involved concepts can be determined for a class or an individual after considering the student's academic performance, educational background, learning interests, learner profile, learning styles, etc. In some cases, the learning path is determined solely by human experts. Since human can be biased, the course instructor's views on the relations of the involved concepts/modules can be imprecise or even contradictory, thus prohibiting any logical deduction of an optimal learning path. Besides, human experts may ignore or possibly be confused by contradictory requirements in the real-world applications. Therefore, we propose a new and systematic framework to develop the next-generation e-learning systems that will perform an explicit semantic analysis on the course materials to extract the individual concepts, and then grouped by a heuristic-based concept clustering algorithm to compute the relationship measures as the basis for extracting the prerequisite requirements/constraints between the involved concepts. Lastly, an evolutionary optimizer will be invoked to return the optimal learning sequence after considering multiple experts' recommended learning sequences which may contain conflicting views in different cases. It is worth noting that our proposed and structured framework with the seamless integration of concept clustering and learning path optimization uniquely represents the first attempt to facilitate the course designers/instructors in providing more personalized and 'systematic' advice (2014) 1(4): 335-352 DOI 10.1007/s40692-014-0016-8 through optimizing the learning path(s) for each individual class/learner. To demonstrate the feasibility of our prototype, we implemented a prototype of the proposed e-learning system framework, and also enhanced the original optimizer with the hill-climbing heuristic. Our empirical evaluation clearly revealed the many possible advantages of our proposal with interesting directions for future investigation.

This paper describes a project, including the design, development, and use of a mobile application (referred to as application hereafter) for learning Chinese as a second language in a bilingual primary school. The application was designed for iPod Touch Apple technology with the purpose to facilitate learning of a fundamental set of 200 Chinese characters. The project was a coordinated effort of experts, including an instructional designer, a software engineer, a Chinese language expert, and classroom teachers to develop an experimental Chinese character learning application for the primary school classroom. This paper reports how the project team explored experiences of teachers and learners in a particular context, developed understanding of teaching and learning needs for Chinese language learning, and how these inform design of the educational application. The final outcomes of the project include a Chinese character learning application and recommendations for design and use of educational applications in Chinese language teaching and other similar contexts.

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