Qualitative simulation of temporal concurrent processes using Time Interval Petri Nets

AbstractThis paper presents a formalism called Time Interval Petri Nets (TIPNs), which are designed to support a qualitative simulation of temporal concurrent processes. One of the key features of TIPNs is a uniform use of time intervals throughout the model. This enables a natural and efficient representation of temporal uncertainty in inputs, outputs, and intermediate states of the qualitative simulation. This is required because the exact time of key events, such as the start time of a fire crisis, is typically not known with certainty. Likewise, output conclusions of the qualitative simulation include earliest time and guaranteed time of key events that can be used by a decision maker to select the most appropriate action.Results are described of a TIPN-based qualitative simulator constructed in the domain of ship damage control. The simulator was created to replace an existing quantitative simulator which was too slow to support envisionment-based real-time decision making in this domain. The experimental results showed a speedup of four to five orders of magnitude which enables hyper-real time qualitative prediction of consequences of multiple competing actions. An automated shipboard damage control decision-making system incorporating a TIPN-based qualitative simulator achieved a 318% improvement over human subject matter experts in a large-scale simulated exercise of over 500 scenarios

Land-Cover and Land-Use Study Using Genetic Algorithms, Petri Nets, and Cellular Automata

Recent research techniques, such as genetic algorithm (GA), Petri net (PN), and cellular automata (CA) have been applied in a number of studies. However, their capability and performance in land-cover land-use (LCLU) classification, change detection, and predictive modeling have not been well understood. This study seeks to address the following questions: 1) How do genetic parameters impact the accuracy of GA-based LCLU classification; 2) How do image parameters impact the accuracy of GA-based LCLU classification; 3) Is GA-based LCLU classification more accurate than the maximum likelihood classifier (MLC), iterative self-organizing data analysis technique (ISODATA), and the hybrid approach; 4) How do genetic parameters impact Petri Net-based LCLU change detection; and 5) How do cellular automata components impact the accuracy of LCLU predictive modeling. The study area, namely the Tickfaw River watershed (711mi²), is located in southeast Louisiana and southwest Mississippi. The major datasets include time-series Landsat TM / ETM images and Digital Orthophoto Quarter Quadrangles (DOQQ’s). LCLU classification was conducted by using the GA, MLC, ISODATA, and Hybrid approach. The LCLU change was modeled by using genetic PN-based process mining technique. The process models were interpreted and input to a CA for predicting future LCLU. The major findings include: 1) GA-based LCLU classification is more accurate than the traditional approaches; 2) When genetic parameters, image parameters, or CA components are configured improperly, the accuracy of LCLU classification, the coverage of LCLU change process model, and/or the accuracy of LCLU predictive modeling will be low; 3) For GA-based LCLU classification, the recommended configuration of genetic / image parameters is generation 2000-5000, population 1000, crossover rate 69%-99%, mutation rate 0.1%-0.5%, generation gap 25%-50%, data layers 16-20, training / testing data size 10000-20000 / 5000-10000, and spatial resolution 30m-60m; 4) For genetic Petri nets-based LCLU change detection, the recommended configuration of genetic parameters is generation 500, population 300, crossover rate 59%, mutation rate 5%, and elitism rate 4%; and 5) For CA-based LCLU predictive modeling, the recommended configuration of CA components is space 6025 * 12993, state 2, von Neumann neighborhood 3 * 3, time step 2-3 years, and optimized transition rules