Yeong Rak Seong

Ordering of simultaneous events in distributed DEVS simulation

Abstract Simultaneous events are the events scheduled to occur at the same simulation time. This paper proposes a new event ordering mechanism for handling simultaneous events of DEVS models in distributed simulation. The DEVS formalism provides a formal framework for specifying discrete event models in a modular, hierarchical form. Thus, the formalism can ease the model verification and validation problems of distributed simulation. Also, the formalism separates models from underlying simulation algorithms. Hence, DEVS models can be simulated in both sequential and distributed environments without any modification. One important issue for such framework is to obtain the same results in both simulation environments. However, in distributed simulation of DEVS models, the processing order of simultaneous events may affect the simulation results. Thus, some ordering mechanism of events is required for well-defined simulation results. The proposed mechanism orders simultaneous events correctly with respect to their causal relationships in distributed DEVS simulation. Also, the mechanism guarantees the same ordering of simultaneous events in both sequential and distributed simulation environments.

Mapping Modular, Hierarchical Discrete Event Models in a Hypercube Multicomputer

Zeiglers DEVS (Discrete Event Systems Specification) formalism specifies discrete event systems in a modular, hierarchical manner. This paper devises a mapping algorithm which exploits a maximum degree of parallelism in DEVS simulation. Transformation of task graphs is employed for parallelization of DEVS external events; clustering of hypercube nodes is employed for parallelization of DEVS internal events. Simulation experimentations with a simple benchmark model show that the potential parallelism of DEVS simulation can be exploited by using the proposed algorithm.

Mapping hierarchical, modular discrete event models in a hypercube multicomputer

Abstract The Discrete Event Systems Specification (DEVS) formalism specifies discrete event systems in a modular, hierarchical form. Timed state transitions within the formalism are specified by an external transition function for randomly arriving external events. The internal transition function schedules internal events. This paper devises a mapping algorithm which exploits a maximum degree of parallelism in DEVS simulation. For parallel DEVS simulation, some hierarchical simulation algorithms were already developed. However, our mapping algorithm employs different approaches for parallelizing the two types of events. For external events parallelization, a task graph representing a hierarchical DEVS model is transformed into a binomial tree which can be easily embedded into a hypercube network. The transformation markedly reduces the synchronization overhead for parallel computation. For internal events parallelization, a hypercube is partitioned into a set of subcubes such that an internal event, and the external events caused by the internal event are processed in the same subcube. Simulation experimentations with a simple benchmark model show that the potential parallelism of DEVS simulation can be exploited by using the proposed algorithm.

Performance evaluation of mobile RFID under multiple BoMR environments

Form factor and demand for low power consumption limit the transmission power of UHF mobile RFID readers. BoMR [1], a kind of booster for mobile RFID readers to extend the read range, was introduced and its performance was analyzed. But some practical issues such as collision and interference should be estimated for its widespread usage. In this paper, the collision, interference and performance of BoMRs are evaluated under multiple BoMR environments. Simulation model with DEVS formalism is presented and the results are summarized. Finally, the performances of LBT schemes are studied by simulation.

Reinforcement learning of intelligent characters in fighting action games

In this paper, we investigate reinforcement learning (RL) of intelligent characters, based on neural network technology, for fighting action games. RL can be either on-policy or off-policy. We apply both schemes to tabula rasa learning and adaptation. The experimental results show that (1) in tabula rasa leaning, off-policy RL outperforms on-policy RL, but (2) in adaptation, on-policy RL outperforms off-policy RL.

Adaptation of intelligent characters to changes of game environments

This paper addresses how intelligent characters, having learning capability based on the neural network technology, automatically adapt to environmental changes in computer games. Our adaptation solution includes an autonomous adaptation scheme and a cooperative adaptation scheme. With the autonomous adaptation scheme, each intelligent character steadily assesses changes of its game environment while taking into consideration recently earned scores, and initiates a new learning process when a change is detected. Intelligent characters may confront various opponents in many computer games. When each intelligent character has fought with just part of the opponents, the cooperative adaptation scheme, based on a genetic algorithm, creates new intelligent characters by composing their partial knowledge of the existing intelligent characters. The experimental results show that intelligent characters can properly accommodate to the changes with the proposed schemes.

Extending the Read Range of UHF Mobile RFID Readers: Arbitration Methods Based on Interference Estimation

The read range of UHF mobile readers can be extended by a booster for mobile RFID readers (BoMR). But in an environment where multiple BoMRs are installed, the read success rate may be decreased due to signal interference. This paper proposes three arbitration methods based on interference estimation with the purpose of enhancing the read success rate. A central arbitration server manages global information in centralized arbitration method (CAM) without broadcast/multicast communication facility. In fully distributed arbitration method (FDAM), all the arbitration messages are broadcasted from a BoMR to every BoMR, and each BoMR decides with broadcasted global information. Events in FDAM are serialized naturally with broadcasted messages. Cluster Distributed Arbitration Method (CDAM) forms clusters with multicasted BoMRs and a selected BoMR acts as an arbiter in the cluster. Such effects as lengthened read range, improved the read success rates of readers can be obtained by the proposed methods without any hardware modification. In order to evaluate the arbitration methods, the RFID system is modeled by using the DEVS formalism and simulated by using the DEVSim++.

Design of a Dual Processor Structure Sensor Node for Energy Harvesting Environments

In this paper, a dual processor structure sensor node for dual mode operation is presented for ultra-low power energy harvesting environments. Energy harvesting environments have the two disadvantages: low amounts of usable energy and large deviations. To overcome the disadvantages, two solutions are applied to the sensor node. First, the sensor node is designed for dual mode operation where it operates differently depending on the amount of usable energy. The sensor node executes only the most fundamental functions and defers the remains while it has insufficient energy, and executes the deferred functions after it obtains sufficient energy. Second, an event handling co-processor (EHCP) is added to the sensor node. Because it would be too excessive to use a heavy-weight general-purpose CPU for such simple operations when there is little usable energy. So, the EHCP is designed with the minimum basic instruction sets so that only the most basic functions are processed. Through this study, a new technique of operating a sensor node is proposed for ultra-low power energy harvesting environments with low amounts of usable energy and large deviations.

Implementation of a distributed problem solving framework based on discrete event system specification

Distributed problem solving(DPS) is a field of distributed artificial intelligence. In a DPS system, a problem is solved by a team of cooperating intelligent agents, each intelligent agent having partial data and knowledge of the problem and the problem solving environment. A DPS system is a discrete event system. In the problem solving process, an intelligent agent randomly receives and processes messages from the outside and other intelligent agent; it also unpredictably activates knowledge sources in itself. This paper proposes a DPS framework based on DEVS formalism specifying discrete event systems. The framework consists of two components, AGENT and WHITEBOARD. The AGENT maintains several problem-solving plans. It is able to solve many problems concurrently. The WHITEBOARD keeps capability and loading factors of the AGENT and distributes messages during the solving process. For verifying the proposed framework, an example system with three intelligent agents was examined. The results show that the DPS framework concurrently solves problems, and each AGENT maintains an appropriate load.