3 research outputs found
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Evolving scout agents for military simulations
textSimulations play an increasingly significant role in training and preparing the military, particularly in environments with constrained budgets. Unfortunately, in most cases a small number of people must control a large number of simulated vehicles and soldiers. This often leads to micromanagement of computer-controlled forces in order to get them to exhibit the human-like characteristics of an enemy force. This thesis uses Neuroevolution of Augmenting Topologies (NEAT) to train neural networks to perform the role of scouts which analyze the terrain and decide where to place themselves to best observe the enemy forces. The main attribute that the scout agents consider is a vapor flow rate from the enemy starting location to their intended objective, which according to previous studies indicates likely chokepoints along the enemy route. This thesis experiments with different configurations of sensors and fitness functions in order to maximize how much of the enemy team is spotted over the course of the scenario. The results show that these agents perform better than randomly placed scouts and better than scouts deployed using heuristics in many situations, although not consistently so. Evolutionary optimization of scout agents using vapor flow is thus a promising approach for developing autonomous scout agents in military simulations.Computer Science
Performance Analysis of AntNet-LA Protocol for Ad-hoc Networks based on Disaster Area Mobility Model
Availability of cheap positioning instruments like GPS receivers makes it possible for routing algorithms to use the position of nodes in an ad hoc mobile network. Regular position based routing algorithms fail to find a route from a source to a destination in some cases when the network contains nodes with irregular transmission ranges or they find a route that is much longer than the shortest path. On the other hand routing algorithms based on Ant Colony Optimization (ACO) find routing paths that are close to the shortest paths even if the nodes in the network have different transmission ranges. The drawback of these algorithms is the large number of messages that needs to be sent or the long delay before the routes are established. In this paper, we propose a novel protocol AntNet-LA which combines the idea of ACO with information about position of all nodes. In this technique the distance between the nodes is considered to transmit the packets, hence overcomes the drawbacks of AntNet algorithm which considers only cumulative probability for packet transmission. We compare performance of AntNet-LA with AntNet, Ad-hoc On Demand Distance Vector (AODV), Ad-hoc On Demand Multipath Distance Vector (AOMDV), Dynamic Source Routing (DSR) and Destination-Sequenced Distance-Vector Routing (DSDV) protocols. We also compare performance of AntNet-LA with distance-aware protocols such as Location Aided Routing (LAR), Geographical AODV GeoAODV and Position Based ANT colony optimization (PBANT)