In nearly all videogames, creating smart and complex artificial agents helps ensure an enjoyable and challenging player experience. Using a dodgeball-inspired simulation, we attempt to train a population of robots to develop effective individual strategies against hard-coded opponents. Every evolving robot is controlled by a feedforward artificial neural network, and has a fitness function based on its hits and deaths. We evolved the robots using both standard and real-time NEAT against several teams. We hypothesized that interesting strategies would develop using both evolutionary algorithms, and fitness would increase in each trial. Initial experiments using rtNEAT did not increase fitness substantially, and after several thousand time steps the robots still exhibited mostly random movement. One exception was a defensive strategy against randomly moving enemies where individuals would specifically avoid the area near the center line. Subsequent experiments using the NEAT algorithm were more successful both visually and quantitatively: average fitness improved, and complex tactics appeared to develop in some trials, such as hiding behind the obstacle. Further research could improve our rtNEAT algorithm to match the relative effectiveness of NEAT, or use competitive coevolution to remove the need for hard-coded opponents.
Tests conducted comparing the effect exposure of Paracetamol to a laser with oven heating a sample to a temperature above its melting point, 169 degrees Celsius showed similarities. This implies that the primary cause of sample damage during Raman spectroscopy is heating. It was found that inserting a piece of glass between the sample and the microscope lens dramatically reduced the ability of the laser to damage samples. Computer models indicate that rotating a sample at 8 rotations per minute could be an effective method of limiting sample damage, and is a potential alternative to active cooling if this becomes financially and logistically viable in industry. Active sample cooling was investigated, but results proved inconclusive. This is a key area for any future research on the topic.
Pakiet dialogue definiuje polecenia przydatne przy składaniu dialogów. Można go wykorzystać do przygotowania zapisu sztuki lub do łatwego sformatowania dialogów na inne potrzeby (np. artykułów z zakresu językoznawstwa).
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