Time management adaptability in multi-agent systems

So far, the main focus of research on adaptability in multi-agent systems (MASs) has been on the agents’ behavior, for example on developing new learning techniques and more flexible action selection mechanisms. However, we introduce a different type of adaptability in MASs, called time management adaptability. Time management adaptability focuses on adaptability in MASs with respect to execution control. First, time management adaptability allows a MAS to be adaptive with respect to its execution platform, anticipating arbitrary and varying timing delays which can violate correctness. Second, time management adaptability allows the execution policy of a MAS to be customized at will to suit the needs of a particular application. In this paper, we discuss the essential aspects of time management adaptability: (1) we introduce time models as a means to explicitly capture the execution policy derived from the application’s execution requirements, (2) we classify and evaluate time management mechanisms which can be used to enforce time models, and (3) we describe a MAS execution control platform which combines both previous aspects to offer high level execution control.

[1]  Sven A. Brueckner,et al.  RETURN FROM THE ANT SYNTHETIC ECOSYSTEMS FOR MANUFACTURING CONTROL , 2000 .

[2]  Leslie Lamport,et al.  Time, clocks, and the ordering of events in a distributed system , 1978, CACM.

[3]  Jayadev Misra,et al.  Distributed discrete-event simulation , 1986, CSUR.

[4]  MatternFriedemann,et al.  Detecting causal relationships in distributed computations , 1994 .

[5]  Danny Weyns,et al.  The Packet-World as a case to study sociality in multi-agent systems , 2002 .

[6]  Adelinde M. Uhrmacher,et al.  Distributed, parallel simulation of multiple, deliberative agents , 2000, Proceedings Fourteenth Workshop on Parallel and Distributed Simulation.

[7]  Nicholas R. Jennings,et al.  Intelligent agents: theory and practice , 1995, The Knowledge Engineering Review.

[8]  Friedemann Mattern,et al.  Detecting causal relationships in distributed computations: In search of the holy grail , 1994, Distributed Computing.

[9]  K. Mani Chandy,et al.  Asynchronous distributed simulation via a sequence of parallel computations , 1981, CACM.

[10]  Scott D. Anderson,et al.  Simulation Of Multiple Time-pressured agents , 1997, Winter Simulation Conference Proceedings,.

[11]  Barbara Dunin-Keplicz,et al.  Multi-Agent Systems and Applications III , 2003 .

[12]  Adelinde M. Uhrmacher,et al.  "Plug and test" - software agents in virtual environments , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

[13]  H. Van Dyke Parunak,et al.  Ant-like missionaries and cannibals: synthetic pheromones for distributed motion control , 2000, AGENTS '00.

[14]  Scott D. Anderson,et al.  A simulation substrate for real-time planning , 1996 .

[15]  Richard M. Fujimoto,et al.  Time Management in The High Level Architecture , 1998, Simul..

[16]  Scott E. Page.  On Incentives and Updating in Agent Based Models , 1997 .

[17]  H. Van Dyke Parunak,et al.  Evolving adaptive pheromone path planning mechanisms , 2002, AAMAS '02.

[18]  Robert L. Axtell,et al.  Effects of Interaction Topology and Activation Regime in Several Multi-Agent Systems , 2000, MABS.

[19]  Luc Steels,et al.  Cooperation between distributed agents through self-organisation , 1990, EEE International Workshop on Intelligent Robots and Systems, Towards a New Frontier of Applications.

[20]  Danny Weyns,et al.  Regional Synchronization for Simultaneous Actions in Situated Multi-agent Systems , 2003, CEEMAS.

[21]  David Jefferson,et al.  Fast Concurrent Simulation Using the Time Warp Mechanism. Part I. Local Control. , 1982 .

[22]  H. Van Dyke Parunak,et al.  Distinguishing Environmental and Agent Dynamics: A Case Study in Abstraction and Alternate Modeling Technologies , 2000, ESAW.