This paper addresses the problem of task allocation among multiple autonomous agents that must accomplish a complex global task. Solutions to the problem have real-world applications in defense, space, disaster management, etc. We solve this problem via agent coalition formation. Multiple coalition formation mechanisms were introduced in prior art, seldom accounting for interdependent tasks. We address this challenge. We introduce an anytime decentralized coalition formation mechanism that enables agents with complementary capabilities to form, autonomously and dynamically, feasible coalition structures that accomplish a global, composite task. The formed structures are incrementally improved via agent replacements to optimize a global utility. We analyze the complexity and show that, although the general problem is NP-hard, our mechanism provides a solution within acceptable time. We present extensive experimental results that illustrate the added value of our approach.

Per Instance Algorithm Configuration (PIAC) relies on features that describe problem instances. It builds an Empirical Performance Model (EPM) from a training set made of (instance, parameter configuration) pairs together with the corresponding performance of the algorithm at hand. This paper presents a case study in the continuous black-box optimization domain, using features proposed in the literature. The target algorithm is CMA-ES, and three of its hyper-parameters. Special care is taken to the computational cost of the features. The EPM is learned on the BBOB benchmark, but tested on independent test functions gathered from the optimization literature. The results demonstrate that the proposed approach can outperform the default setting of CMA-ES with as few as 30 or 50 time the problem dimension additional function evaluations for feature computation.

We investigate a method to deal with congestion of sectors and delays in the tactical phase of air traffic flow and capacity management. It relies on temporal objectives given for every point of the flight plans and shared among the controllers in order to create a collaborative environment. This would enhance the transition from the network view of the flow management to the local view of air traffic control. Uncertainty is modeled at the trajectory level with temporal information on the boundary points of the crossed sectors and then, we infer the probabilistic occupancy count. Therefore, we can model the accuracy of the trajectory prediction in the optimization process in order to fix some safety margins. On the one hand, more accurate is our prediction; more efficient will be the proposed solutions, because of the tighter safety margins. On the other hand, when uncertainty is not negligible, the proposed solutions will be more robust to disruptions. Furthermore, a multiobjective algorithm is used to find the tradeoff between the delays and congestion, which are antagonist in airspace with high traffic density. The flow management position can choose manually, or automatically with a preference-based algorithm, the adequate solution. This method is tested against two instances, one with 10 flights and 5 sectors and one with 300 flights and 16 sectors.

Multicore machines are becoming a standard way to speed up the system performance. After having instantiated the evolutionary metaheuristic DAEX with the forward search YAHSP planner, we investigate on the global parallelism approach, which exploits the intrinsic parallelism of the individual evaluation. This paper describes a parallel shared-memory version of the DAEYAHSP planning system using the OpenMP directive-based API. The parallelization scheme applies at a high level of abstraction and thus can be used by any evolutionary algorithm implemented with the Evolving Objects framework. The proof of concept is validated on a 48-core machine with two planning tasks extracted from the last international planning competition. Experiments show significant speedups with an increasing number of cores. This preliminary work opens an avenue for parallelizing any evolutionary algorithm developed with EO that would target multicore architectures.

Divide-and-Evolve (DaE) is an original "memeticization" of Evolutionary Computation and Artificial Intelligence Planning. However, like any Evolutionary Algorithm, DaE has several parameters that need to be tuned, and the already excellent experimental results demonstrated by DaE on benchmarks from the International Planning Competition, at the level of those of standard AI planners, have been obtained with parameters that had been tuned once and forall using the Racing method. This paper demonstrates that more specific parameter tuning (e.g. at the domain level or even at the instance level) can further improve DaE results, and discusses the trade-off between the gain in quality of the resulting plans and the overhead in terms of computational cost.