Intelligent autonomous agents in HLA virtual environments

Many simulations of virtual environments involve realistically behaving autonomous agents to give users as much interaction capabilities as possible. In this kind of simulations, interoperable architectures such as the High Level Architecture enable us to create complex and lively environments from simple simulations of different kinds of entities. However, making simulations collaborate requires to give existing agents the ability to interact with the newly integrated ones. Such a task generally consists in redefining existing behaviours completely. Hence, making several even interoperable simulations collaborate, implies long and demanding developments while interoperability tries to prevent them. Such drawbacks are mainly due to the fact that most behavioural models are based on nite state machines and expert systems for which designers have to describe exhaustively all agents'behaviour. Classifier systems enable designers to describe agents' behaviours through goals ("what to do") instead of transitions or set of rules ("how to do it"). Then, agents learn how to reach those objectives using evolutionary learning algorithms. Such a modeling is more suited for the development of interoperable simulations. Indeed, adding new simulated agents only requires to add new goals to the existing ones. This paper presents the distributed driving simulation we built using interoperable subsimulations. At first, it presents how classier systems make agents' behaviour easily evolutional in the context of interoperable simulations. Then, it shows how interoperability enables us to share the management of autonomous entities between several computers in a distributed way

Adaptive management of an active services network

The benefits of active services and networks cannot be realised unless the associated increase in system complexity can be efficiently managed. An adaptive management solution is required. Simulation results show that a distributed genetic algorithm, inspired by observations of bacterial communities, can offer many key management functions. The algorithm is fast and efficient, even when the demand for network services is rapidly varying

Knowledge discovery for scheduling in computational grids

International audienceScheduling in computational grids addresses the allocation of computing jobs to globally distributed compute resources. In a frequently changing resource environment, scheduling decisions have to be made rapidly. Depending on both the job properties and the current state of the resources, those decisions are different. Thus, the performance of grid scheduling systems highly depends on their adaptivity and flexibility in changing environments. Under these conditions, methods from knowledge discovery yielded significant success to augment and substitute conventional grid scheduling techniques. This paper presents a survey on approaches to extract, represent, and utilize knowledge to improve the grid scheduling performance. It aims to give researchers insight into techniques used for knowledge-supported scheduling in large-scale distributed computing environments

USING COEVOLUTION IN COMPLEX DOMAINS

Genetic Algorithms is a computational model inspired by Darwin's theory of evolution. It has a broad range of applications from function optimization to solving robotic control problems. Coevolution is an extension of Genetic Algorithms in which more than one population is evolved at the same time. Coevolution can be done in two ways: cooperatively, in which populations jointly try to solve an evolutionary problem, or competitively. Coevolution has been shown to be useful in solving many problems, yet its application in complex domains still needs to be demonstrated.Robotic soccer is a complex domain that has a dynamic and noisy environment. Many Reinforcement Learning techniques have been applied to the robotic soccer domain, since it is a great test bed for many machine learning methods. However, the success of Reinforcement Learning methods has been limited due to the huge state space of the domain. Evolutionary Algorithms have also been used to tackle this domain; nevertheless, their application has been limited to a small subset of the domain, and no attempt has been shown to be successful in acting on solving the whole problem.This thesis will try to answer the question of whether coevolution can be applied successfully to complex domains. Three techniques are introduced to tackle the robotic soccer problem. First, an incremental learning algorithm is used to achieve a desirable performance of some soccer tasks. Second, a hierarchical coevolution paradigm is introduced to allow coevolution to scale up in solving the problem. Third, an orchestration mechanism is utilized to manage the learning processes