Section: New Results
Developmental Representations for Evolutionary Design
Developmental represenatations provide a new and promising approach to automatic design of complex structures inspired from artificial ontogeny. In this setup, a set of elements, or cells, interacts locally so as to provide global synchronization and communication toward some objectives. The key feature of such systems is to address the efficient design of large scale structures and to provide solution with some guaranties about its scalability and robustness. Proof of principle of the approach, concerning the design of bridges or multi-robot cooperation, have been given. Our main contributions in previous work involve: i) the evolutionary optimization of Echo State Network using new fitness objectives; ii) the design of a specific stopping criterion. This stopping criterion results not only in significant computational gains, but also in an unprecedented robustness of the design solution to perturbations in the environment. The approach was extended to the design of truss structure  ; the novelty is to use a single cell template all over the structure (achieving different behaviours depending on the local environment), resulting in a low-dimensional search space while featuring excellent flexibility. All those results will build Alexandre Devert's PhD (submitted, to be defended in early 2009).
TAO is an active participant in the EU funded IP Symbrion swarm-robotic project (2008-2012). Tao's role in SYMBRION is to provide the swarm with learning and evolution facilities, based on hardware developed by other partners in the consortium. Early work in this direction has been explored this year based on an ontogenic approach in order to enable multi-robot pattern formation in noisy environments  . The specificity of the approach (compared to cellular automata) is to consider a continuous and dynamic topology of the agents in the geographical field. Again, scalability and robustness were achieved through simple, local-only, communication scheme. Lastly, it should be noted that the Complex System SIG benefits from strong interactions with the Reservoir Computing SIG, as both groups are investigating the computational properties of e.g., Echo State Networks from different and complementary perspectives. A joint JST-ANR proposal has also been recently accepted, in collaboration with Suzuki Lab at Kyushu University, dealing with a coupling between symbolic and numerical approaches for swarm robotics.
Social Systems Modelling
In complement to robotic applications, we also used agents as a tool to simulate and understand complex systems, and more specifically social system. Multi-agent based simulation (MABS) is a growing approach for the study of complex domains: it allows an intuitive modelling (agent behavior, interaction rules), simple sceniarii definitions, micro and macro variables observability. In previous work, we used this methodology to study theoretical networks of financial agents. This year, we extended this field of research in three directions. First, by studying a domain with a multi-step decision process and some empirical data: the French academic labor market (maitre de conferences hiring system). Starting with a model of candidates and universities agents, we showed that the high local hiring rate observed in empirical data was more a product of the system structure itself than a consequence of the agent preferences (  ). Current work includes the use of regional empirical data to obtain a better validation of the model. A second line of research was to study a market with complex behaviors and interactions with strong trust issues: the Rungis wholesale market. Due to the importance of bilateral agreements, a fine definition and tuning of the agents was needed, which is why we used cognitive agents for this simulation (  , with University of Coimbra and AUDENCIA school of management). Current work includes the use of this simulation to explain empirical facts, such as the strong difference between the official price and the effective price on the market. Finally, a last line of research concerned the study of the agent itself, by exploring new ways to build cognitive agents, such as polychronous networks and Memory Evolutive System (P. Caillou with J. Monteiro and M. Netto (University of Sao Paolo), submitted)
A Statistical Physics Perspective
Basic tools (mean field approaches and associated distributed algorithms) from statistical physics and probability are used to complement the MABS tools developed in tao to address complex systems, both from the modelling and computing viewpoint. The data stream clustering (see section 6.1 for more details) as been identified as a well suited application of such approach, with the setting of a new version of “affinity propagation” which uses an on-line aggregation and hierarchical treatment of the data stream and for which a scaling analyses has been performed  ,  . Another line of research also based on this message-passing approach, deals with the problem of learning an approximate Markov Random Field based on generalized Bethe free energy approximations (Cyril Furtlehner, Jean-Marc Lasgouttes(projet Imara), Anne Auger, Inria report in progress). In addition, some basic properties (update rules, stability properties) of loopy belief propagation have been analyzed (Cyril Furtlehner, Jean-Marc Lasgouttes (EPI Imara)). These theoretical studies are done in the perspective of the ANR project TRAVESTI, to begin in January 2009 upon final financial acceptation decision, and that deals with traffic networks modelling. An interesting question which arises in multi-agent systems is the effect of asymmetry in some decision process (buy or sell in financial systems, accelerate or break in traffic systems...) on the emergence of instabilities (speculative bubbles, traffic jams...). We have formalized this question by introducing a new type of zero-range processes with specific Markov non-reversibility and applied it to the computation of the fundamental diagram of road traffic (Cyril Furtlehner, Jean-Marc Lasgouttes (EPI Imara), Inria report in progress). Problems like the random Ising model or the NK model offers benchmark possibilities. On such a benchmark we have compared in particular a population-based strategy (memetic algorithm) versus a UCT (Upper Confidence Bound, Auer 2001) strategy based on the same local search heuristic. Using extreme value statistics of the local search algorithm combined with simple scaling hypothesis (well verified experimentally) yields a (hopefully) new generic UCT-like approach to combinatorial optimization (Cyril Furtlehner, Olivier Teytaud, Inria report in progress).
Sequential Representation for Temporal Planning
On-going collaboration with Thalès is concerned with validating, understanding, and improving the Divide-and-Evolve (DAE) approach to Temporal Planning, and includes the supervision of Jacques Bibaï's CIFRE PhD. After promising comparative results obtained at the beginning of the PhD  , DAE entered the IPC'08 (International Planning Competition), and though the overall results were disappointing, because it failed to simply solve about 50% of the instances, DAE obtained equal or better results that the winner of the competition on all instances that it could solve. Based on the analysis of those results, several improvements of DAE have already been implemented. Two papers have been submitted, and another one is in preparation.