This paper presents a work in progress about stakeholder detection for online debates. We propose an approach based on classical community detection methods applied to semantic social networks representation. We defend that new web2.0 tools should assist users to define semantic relations between users, groups and roles based on social interaction analysis. The main goal is to provide new mecanisms for moderation decreasing misunderstandings and highlighting unexpected behaviors.

Control architectures, such as the LAAS architecture, CLARATY and HARPIC, have been developped to provide autonomy to robots. To achieve a robot's task, these control architectures plan sequences of sensorimotor behaviors. Currently carried out by roboticians, the design of sensorimotor behaviors is a truly complex task that can require many hours of hard work and intensive computations. In this paper, we propose a Constraint Programming-based framework to interact with roboticians during the sensorimotor behaviors design. A constraint network acquisition platform and a CSP-Based planner are used to automatically design sensorimotor behaviors. Moreover, our architecture exploits the propagation properties of the acquired CSPs to supervise the execution of a given sensorimotor behavior. Some experimental results are presented to validate our approach.

Killer applications, such as Excel and others, may be retrospectively considered as potential excellent learning environments in different specific domains and also in meta-cognitive skills,even if there is no learning intention either in the designer or the user. We briefly present a patented methodology for knowledge acquisition and construction, which has been widely and successfully used for five years, and interpreted as a learning environment for users engaged in domain-dependent interactions.

The paper proposes a new technology for intelligent machines based on the concept of programmable hardware. To build an intelligent system, the designer has to adapt it to the problem. First we show that programmable hardware is an intermediate step for building configurations, in order to choose the best architecture. In this case, the tasks are performed in a time period that respects human cognitive capacities. Next is detailed a multi level model composed of the cognitive, software and hardware levels. An experimental platform has been built, based on programmable hardware, and used in a "Grand Challenge" problem: knowledge discovery in genetic sequence databases, to compare the relative efficiencies of programmable hardware and classical Von Neumann based architecture. Programmable hardware is shown to have a significantly faster response time, which is essential for modern day intelligent machine user interaction.