## Section: New Results

### Complex Systems

Participants : Jacques Bibaï, Nicolas Bredèche, Matthias Brendel, Cyril Furtlehner, Anne Auger, Philippe Caillou, Jean-Marc Montanier, Hélène Paugam-Moisy, Marc Schoenauer, Michèle Sebag, Maxim Samsonov.

- Evolutionary AI Planning:
Divide And Evolve (DAE) [134] is an evolutionary planning algorithm: A planning problem is sequentially sliced into hopefully simpler problems that are handled by a classical planner, and evolution optimizes the slicing. In 2010, the DESCARWIN ANR project started, and Jacques Bibaï defended his PhD [1] . Main results in 2010 include the validation of the use of the sub-optimal planner YAHSP rather than the optimal CPT [45] , a thorough comparison with state-of-the-art competitors at ICAPS2010 [44] , and the improved results obtained by off-line tuning of the parameters [43] . DAE was awarded the Silver Medal at ACM-GECCO Humies Awards 2010.

- Distributed Autonomous Robotics:
Within the Symbrion project, we are concerned with a fixed-size population of autonomous robot-agents facing unknown, possibly changing, environments.

One of our goals is to design an embodied self-adaptation algorithm that can adapt to some implicit pressure from the environment. An initial step is to provide adaptation and increase of performance in the long run at the level of a single robotic agent [130] , or a population of robotic agents. The main results obtained in 2010 include parameter analysis of the (1 + 1) -on-line for single robot self-adaptation [110] and the design of a new algorithm for on-line distributed optimization of behaviors in swarms of autonomous agents [50] .

Another issue in autonomous robotics is that of designing an implicit fitness, in order to provide the robots with intrinsic motivation and perform latent learning when no task is defined. Two approaches based on Information Theory have been proposed, implementing some

*Curiosity*, i.e., incentives for the robot to explore its sensori-motor space [62] .- Statistical Physics Perspective
Basic tools from statistical physics (scaling, mean-field techniques and associated distributed algorithms, exactly-solvable models) and probability have been used to model and optimize complex systems, either standalone or combined with MABS approaches. Results are

a scaling analysis of the “affinity propagation” algorithm and a related renormalization-based method able to find the true number of clusters in a dataset under some well defined conditions [17] .

In the context of the ANR TRAVESTI project we propose methods for doing a spatial and temporal analysis of traffic states on large scale networks and how this relates to the encoding of traffic patterns into belief propagation fixed points, which we use for traffic reconstruction and prediction [68] .

a work in progress concerns the design of exactly solvable models relevant to the understanding of the fundamental diagram of traffic flow in the ANR Travesti context [124] , [106] .

the design of a message passing algorithm for sampling the Pareto Front of a bi-objective 3-SAT optimization problem has been set up in the STREP Gennetec context [121] , [104] .

- Multi-agent and games
Samuel Thiriot joined the team as Post-doc in September on the InnovNation project. Its goal is to design, analyze and simulate with a multi-agent system a serious game of innovation emergence in a social network (in collaboration with ParaSchool and BlueNove).

To understand multi-agent simulation logs, a tool to generate new simulations and automatically analyze the results and provide statistically valid test values was designed [57] , [56] .

Lastly, Multi-agent systems were used to study decentralized coalition formation and restructuration protocol in a multi-objective framework. A proof of principle of the approach, delivering Pareto optimal solutions in a small-size class-scheduling problem has been proposed in collaboration with Université Lyon-1 and Université Paris Dauphine [11] .