Στη παρούσα διπλωματική εργασία παρουσιάζεται η ανάπτυξη κώδικα σε γλώσσα προγραμματισμού Python με σκοπό να παίζει το παιχνίδι Bluff. Αναλυτικότερα το Bluff ανήκει στη κατηγορία των παιχνιδιών μερικούς πληροφόρησης και εκδοχές του περιλαμβάνουν το στοιχείο της τύχης άρα είναι και στοχαστικό. Στην ίδια κατηγορία παιχνιδιών εντάσεται και το πόκερ στο οποίο διεξάγεται εντονη ερευνητική δραστηριότητα αυτή τη περίοδο. Οι δυσκολίες που παρουσιάζει το εγχείρημα της κατασκευης ενός τέτοιου αλγόριθμου εγκειται στο μεγάλο χώρο καταστασης του παιχνιδιού και στην αδυναμια εφαρμογης της τεχνικής min max λόγω της δομής του παιχνιδιού. Επίσης ενας επιτυχημένος παίχτης bluff θα πρέπει να αναγνωρίζει ποτε ο αντίπαλος μπλοφάρει καθώς και να μπλοφάρει ο ίδιος. Τέλος όπως και στο ποκερ για να γίνεις μετρ στο παιχνίδι θα πρέπει να μεταβάλεις τη στρατηγική σου ανάλογα με τον αντίπαλο, θα πρέπει να εκμεταλεύεσαι τα λάθη του και ταυτόχρονα να μη γίνεσαι προβλέψιμος. Ο κώδικας μας εχει 3 versions. Στη πρώτη version ενας απλος μηχανισμός που στηρίζεται στους κανονες του παιχνιδίου υλοποιείται και εξετάζεται η επιτυχια του. Στη δευτερη εκδοση εισαγουμε το στοιχειο της μπλόφας ενώ στη τρίτη αφου μοντελοποιήσουμε τον αντίπαλο, λαμβάνουμε αποφάσεις με βάση αυτη τη μοντελοποίηση. / This diploma thesis deals with the problem of developing an algorithm that can play the game of Bluff. The programming language that is used is Python. Concretely the game of bluff belongs into the category of partial information games and some variations involve luck which makes it also stochastic. Intense research is conducted in poker which belongs to the same family of games. The main difficulty is the huge state space of these games due to uncertainty and the deficit of the min-max method. In addition a succesfull bluff player must recognize when the opponent is bluffing and must make bluffs on his own. One last thing that this game requires is to have dynamic strategies which means being able to change your strategy according to the opponent in order to maximize your wining by exploiting his errors. The algorithm builded has 3 versions. The first one simulated a beginner that sticks to the rules, makes no bluffs and raises according to probabilities. The second version introduces bluffing. The final version includes opponent modeling and making decision based on that.

Doctor of Philosophy / Department of Physics / Talat S. Rahman / This dissertation is an extensive study of several issues related to post deposition morphological evolution of fcc metal surfaces. These studies were carried out by probing the energetics and the dynamics of underlying atomistic mechanisms responsible for surface diffusion. An important aspect is the determination of relative probability of competing atomistic mechanisms and their contribution to controlling shapes and step edge patterns of nano structures on surfaces. In this scenario, the descent of adatoms from Ag islands on Ag(111) surface is examined. It shows an exchange mechanism to dominate over hopping and the process to favor the formation of (100)-microfacetted steps (A-type) over the (111)-microfacetted ones (B-type). Molecular dynamics simulations support these results at low temperature while at high temperature B-type step formation dominates. This change in the trend could happen if these processes leading to the formation of the A and B type steps have different values of their diffusion prefactors. This difference is confirmed on the basis of our calculations of the diffusion coefficients. Further, to understand the macroscopic properties of a system on the basis of its atomic scale information, spatial and temporal fluctuations of step edges on vicinal Cu(1 1 13) and Cu(1 1 19) surfaces is studied using kinetic Monte Carlo (KMC) simulations. These results show excellent agreement with experimental data, highlighting the role of mass transport along step edges, and also showing the validity of tools like KMC which aims at bridging the gap in length and time scales at which a range of interesting phenomena take place. To facilitate unbiased modeling of material properties, a novel way of performing KMC simulations is presented. In this approach the lists of diffusion processes are automatically collected during the simulation using a saddle-point search method in the potential energy landscape. The speed of the simulations is thus enhanced along with a substantial gain in reliability. Using this method the diffusion and coalescence of two-dimensional Cu and Ag adatom-island on Cu(111) and Ag(111) is studied. Together with input from molecular dynamics simulations, new processes involving the concerted motion of smaller islands are revealed. A significant difference in the scaling of the effective diffusion barriers with island size is observed for the sets of smaller (less than 10 atoms) and larger islands. In particular, the presence of concerted island motion leads to an almost linear increase in the effective diffusion barrier with size, while its absence accounts for strong size-dependent oscillations and anomalous behavior for trimers and heptamers. A crossover from diffusion due to the collective motion of the smaller island to a regime in which the island diffuses through the periphery dominated mass transport (large islands, 19 to 100 atoms) is predicted. For islands containing 19 to 100 atoms the scaling exponent is found to be in good agreement with that found in previous studies.