Abstract
Nano-communications have tremendous potential for applications in the biomedical, environmental, industrial and military fields. Molecular communication is a new communication paradigm which allows nanomachines to exchange information using molecules as carrier. This is the most promising communication method within nanonetworks, since using electromagnetic waves is not very likely due to the size of the nanomachines. In molecular communication, information is encoded onto molecules at senders and the molecules propagate to receivers in a controlled manner. This control is one of the most important challenges, and this paper will describe some solutions for molecular communication. Since molecular communication offers means to transport information-encoded molecules to receivers and allows biological and artificially-created components to communicate with each other, future applications for health care are discussed in this paper. Results are obtained from computerized simulation scenarios selecting appropriate parameters for optimal performance.

Abstract
One of the research topics on multi-agent systems focuses on the development of mechanisms such as plans to empower a team of agents to cooperate in order to perform complex tasks. In many cases, the definition of these plans are based on a specific and rather complex grammar and stored in structured text files. In the context of the 2D simulated Robotic Soccer domain, a set-play language was proposed to coordinate the execution of teammates' behaviors to improve a team's overall performance. The process of manually writing set-play definitions is hazardous and can benefit from the use of a graphical tool to reach new users and allow typical users to become more productive. This work presents such a tool for which several experiments were run to measure its usability with forty two users by having them perform a set of tasks for which their execution time, number errors and satisfaction were recorded. The tool reduced the previous average time required to completely define a set-play by 90% and enabled even non-expert users to use it Moreover, users were on average satisfied with SPlanner having ranked it with a score of 77 (out of 100) using a System Usability Scale questionnaire.

Abstract
Many stochastic search algorithms require relearning if the task changes slightly to adapt the solution to the new situation or the new context. Therefore in this research, we investigate the contextual stochastic search algorithms that can learn from multiple tasks simultaneously. Here, we want to find good parameter vectors for multiple related tasks, where each task is described by a continuous context vector. Hence, the objective function might change slightly for each parameter vector evaluation.

Abstract
Decision making in complex, multi-agent and dynamic environments such as disaster spaces is a challenging problem in Artificial Intelligence. This research paper aims at developing distributed coordination and cooperation method based on reinforcement learning to enable team of homogeneous, autonomous fire fighter agents, with similar skills to accomplish complex task allocation, with emphasis on firefighting tasks in disaster space. The main contribution is applying reinforcement learning to solve the bottleneck caused by dynamicity and variety of conditions in such situations as well as improving the distributed coordination of fire fighter agent's to extinguish fires within a disaster zone. The proposed method increases the speed of learning; it has very low memory usage and has a good scalability and robustness in the case that the number of agents and complexity of task increases. The effectiveness of the proposed method is shown through simulation results. © 2013 Springer-Verlag.

Abstract
Intelligent wheelchairs (IW) are technologies that can increase the autonomy and independence of elderly people and patients suffering from some kind of disability. Nowadays the intelligent wheelchairs and the human-machine studies are very active research areas. This paper presents a methodology and a Data Analysis System (DAS) that provides an adapted command language to an user of the IW. This command language is a set of input sequences that can be created using inputs from an input device or a combination of the inputs available in a multimodal interface. The results show that there are statistical evidences to affirm that the mean of the evaluation of the DAS generated command language is higher than the mean of the evaluation of the command language recommended by the health specialist (p value = 0.002) with a sample of 11 cerebral palsy users. This work demonstrates that it is possible to adapt an intelligent wheelchair interface to the user even when the users present heterogeneous and severe physical constraints.

Abstract
In this paper we address the problem of musical genre recognition for a dancing robot with embedded microphones capable of distinguishing the genre of a musical piece while moving in a real-world scenario. For this purpose, we assess and compare two state-of-the-art musical genre recognition systems, based on Support Vector Machines and Markov Models, in the context of different real-world acoustic environments. In addition, we compare different preprocessing robot audition variants (single channel and separated signal from multiple channels) and test different acoustic models, learned a priori, to tackle multiple noise conditions of increasing complexity in the presence of noises of different natures (e.g., robot motion, speech). The results with six different musical genres suggest improved results, in the order of 43.6pp for the most complex conditions, when recurring to Sound Source Separation and acoustic models trained in similar conditions to the testing scenarios. A robot dance demonstration session confirms the applicability of the proposed integration for genre-adaptive dancing robots in real-world noisy environments.