We present results and research projects about the computational aspects of classical problems in Artificial Intelligence. We are interested in the setting of agents able to describe their environment through a possibly huge number of Boolean descriptors, and to act upon this environment. The typical applications of this kind of studies are to the design of autonomous robots (for exploring unknown zones, for instance) or of software assistants (for scheduling, for instance). The ultimate goal of research in this domain is the design of agents able to learn autonomously, by learning and interacting with their environment (including human users), also able to reason for producing new pieces of knowledge, for explaining observed phenomena, and finally, able to decide on which action to take at any moment, in a rational fashion. Ideally, such agents will be fast, efficient as soon as they start to interact with their environment, they will improve their behavior as time goes by, and they will be able to communicate naturally with humans. Among the numerous research questions raised by these objectives, we are especially interested in concept and preference learning, in reinforcement learning, in planning, and in some underlying problems in complexity theory. A particular attention is paid to interaction with humans and to huge numbers of descriptors of the environment, as are necessary in real-world applications.
