Modeling and Verification of Agent based Adaptive Traffic Signal using Symbolic Model Verifier

This paper addresses the issue of modeling and verification of a Multi Agent System (MAS) scenario. We have considered an agent based adaptive traffic signal system. The system monitors the smooth flow of traffic at intersection of two road segment. After describing how the adaptive traffic signal system can efficiently be used and showing its advantages over traffic signals with predetermined periods, we have shown how we can transform this scenario into Finite State Machine (FSM). Once the system is transformed into a FSM, we have verified the specifications specified in Computational Tree Logic(CTL) using NuSMV as a model checking tool. Simulation results obtained from NuSMV showed us whether the system satisfied the specifications or not. It has also showed us the state where the system specification does not hold. Using which we traced back our system to find the source, leading to the specification violation. Finally, we again verified the modified system with NuSMV for its specifications.Comment: 13 pages, 6 figures, Submitted to International Journal of Computer Application (IJCA

Distributed agent-based building evacuation simulator

The optimisation of the evacuation of a building plays a fundamental role in emergency situations. The behaviour of individuals, the directions that civilians receive, and the actions of the emergency personnel, will affect the success of the operation. We describe a simulation system that represents the individual, intelligent, and interacting agents that cooperate and compete while evacuating the building. The system also takes into account detailed information about the building and the sensory capabilities that it may contain. Since the level of detail represented in such a simulation can lead to computational needs that grow at least as a polynomial function of the number of the simulated agents, we propose an agent-oriented Distributed Building Evacuation Simulator (DBES). The DBES is integrated with a wireless sensor network which offers a closed loop representation of the evacuation procedure, including the sensed data and the emergency decision making

KInNeSS: A Modular Framework for Computational Neuroscience

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biological-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalabiltiy, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multu-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions of ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further developement of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effecitively collaborate using a modern neural simulation platform.Center for Excellence for Learning Education, Science, and Technology (SBE-0354378); Air Force Office of Scientific Research (F49620-01-1-0397); Office of Naval Research (N00014-01-1-0624