The phenomenon of Decision Oscillation: a new consequence of pathology in Game Trees

Random minimaxing studies the consequences of using a random number for scoring the leaf nodes of a full width game tree and then computing the best move using the standard minimax procedure. Experiments in Chess showed that the strength of play increases as the depth of the lookahead is increased. Previous research by the authors provided a partial explanation of why random minimaxing can strengthen play by showing that, when one move dominates another move, then the dominating move is more likely to be chosen by minimax. This paper examines a special case of determining the move probability when domination does not occur. Specifically, we show that, under a uniform branching game tree model, whether the probability that one move is chosen rather than another depends not only on the branching factors of the moves involved, but also on whether the number of ply searched is odd or even. This is a new type of game tree pathology, where the minimax procedure will change its mind as to which move is best, independently of the true value of the game, and oscillate between moves as the depth of lookahead alternates between odd and even

Stochastic Reasoning with Action Probabilistic Logic Programs

In the real world, there is a constant need to reason about the behavior of various entities. A soccer goalie could benefit from information available about past penalty kicks by the same player facing him now. National security experts could benefit from the ability to reason about behaviors of terror groups. By applying behavioral models, an organization may get a better understanding about how best to target their efforts and achieve their goals. In this thesis, we propose action probabilistic logic (or ap-) programs, a formalism designed for reasoning about the probability of events whose inter-dependencies are unknown. We investigate how to use ap-programs to reason in the kinds of scenarios described above. Our approach is based on probabilistic logic programming, a well known formalism for reasoning under uncertainty, which has been shown to be highly flexible since it allows imprecise probabilities to be specified in the form of intervals that convey the inherent uncertainty in the knowledge. Furthermore, no independence assumptions are made, in contrast to many of the probabilistic reasoning formalisms that have been proposed. Up to now, all work in probabilistic logic programming has focused on the problem of entailment, i.e., verifying if a given formula follows from the available knowledge. In this thesis, we argue that other problems also need to be solved for this kind of reasoning. The three main problems we address are: Computing most probable worlds: what is the most likely set of actions given the current state of affairs?; answering abductive queries: how can we effect changes in the environment in order to evoke certain desired actions?; and Reasoning about promises: given the importance of promises and how they are fulfilled, how can we incorporate quantitative knowledge about promise fulfillment in ap-programs? We address different variants of these problems, propose exact and heuristic algorithms to scalably solve them, present empirical evaluations of their performance, and discuss their application in real world scenarios

Review of Kalah Game research and the proposition of a novel heuristic-deterministic algorithm compared to tree-search solutions and human decision-making

The Kalah game represents the most popular version of probably the oldest board game ever-the Mancala game. From this viewpoint, the art of playing Kalah can contribute to cultural heritage. This paper primarily focuses on a review of Kalah history and on a survey of research made so far for solving and analyzing the Kalah game (and some other related Mancala games). This review concludes that even if strong in-depth tree-search solutions for some types of the game were already published, it is still reasonable to develop less time-consumptive and computationally-demanding playing algorithms and their strategies Therefore, the paper also presents an original heuristic algorithm based on particular deterministic strategies arising from the analysis of the game rules. Standard and modified mini-max tree-search algorithms are introduced as well. A simple C++ application with Qt framework is developed to perform the algorithm verification and comparative experiments. Two sets of benchmark tests are made; namely, a tournament where a mid-experienced amateur human player competes with the three algorithms is introduced first. Then, a round-robin tournament of all the algorithms is presented. It can be deduced that the proposed heuristic algorithm has comparable success to the human player and to low-depth tree-search solutions. Moreover, multiple-case experiments proved that the opening move has a decisive impact on winning or losing. Namely, if the computer plays first, the human opponent cannot beat it. Contrariwise, if it starts to play second, using the heuristic algorithm, it nearly always loses. © 2020 by the authors.European Regional Development FundEuropean Union (EU); Ministry of Education, Youth and SportsMinistry of Education, Youth & Sports - Czech Republic [LO1303 (MSMT-7778/2014)]; internal grant agency of VSB Technical University of Ostrava, Faculty of Electrical Engineering and Computer Science, Czech Republic [SP2020/46

Investigating evolutionary checkers by incorporating individual and social learning, N-tuple systems and a round robin tournament

In recent years, much research attention has been paid to evolving self-learning game players. Fogel's Blondie24 is just one demonstration of a real success in this field and it has inspired many other scientists. In this thesis, artificial neural networks are employed to evolve game playing strategies for the game of checkers by introducing a league structure into the learning phase of a system based on Blondie24. We believe that this helps eliminate some of the randomness in the evolution. The best player obtained is tested against an evolutionary checkers program based on Blondie24. The results obtained are promising. In addition, we introduce an individual and social learning mechanism into the learning phase of the evolutionary checkers system. The best player obtained is tested against an implementation of an evolutionary checkers program, and also against a player, which utilises a round robin tournament. The results are promising. N-tuple systems are also investigated and are used as position value functions for the game of checkers. The architecture of the n-tuple is utilises temporal difference learning. The best player obtained is compared with an implementation of evolutionary checkers program based on Blondie24, and also against a Blondie24 inspired player, which utilises a round robin tournament. The results are promising. We also address the question of whether piece difference and the look-ahead depth are important factors in the Blondie24 architecture. Our experiments show that piece difference and the look-ahead depth have a significant effect on learning abilities

Runtime Quantitative Verification of Self-Adaptive Systems

Software systems used in mission- and business-critical applications in domains including defence, healthcare, and finance must comply with strict dependability, performance, and other Quality-of-Service (QoS) requirements. Self-adaptive systems achieve this compliance under changing environmental conditions, evolving requirements and system failures by using closed-loop control to modify their behaviour and structure in response to these events. Runtime quantitative verification (RQV) is a mathematically-based approach that implements the closed-loop control of self-adaptive systems. Using runtime observations of a system and its environment, RQV updates stochastic models whose formal analysis underpins the adaptation decisions made within the control loop. The approach can identify and, under certain conditions, predict violation of QoS requirements, and can drive self-adaptation in ways guaranteed to restore or maintain compliance with these requirements. Despite its merits, RQV has significant computation and memory overheads, which restrict its applicability to small systems and to adaptations affecting only the configuration parameters of the system. In this thesis, we introduce RQV variants that improve the efficiency and scalability of the approach and extend its applicability to larger and more complex self-adaptive software systems, and to adaptations that modify the structure of a system. First, we integrate RQV with established efficiency improvement techniques from other software engineering areas. We use caching of recent analysis results, limited lookahead to precompute suitable adaptations for potential future changes, and nearly-optimal reconfiguration to eliminate the need for an exhaustive analysis of the entire reconfiguration space. Second, we introduce an RQV variant that incorporates evolutionary algorithms into the RQV process facilitating the efficient search through large reconfiguration spaces and enabling adaptations that include structural changes. Third, we propose an RQV-driven approach that decentralises the control loops in distributed self-adaptive systems. Finally, we devise an RQV-based methodology for the engineering of trustworthy self-adaptive systems. We evaluate the proposed RQV variants using prototype self-adaptive systems from several application domains, including an embedded system for unmanned underwater vehicles and a foreign exchange service-based system. Our results, subject to the adaptation scenarios used in the evaluation, demonstrate the effectiveness and generality of the new RQV variants

Investigating evolutionary checkers by incorporating individual and social learning, N-tuple systems and a round robin tournament

In recent years, much research attention has been paid to evolving self-learning game players. Fogel's Blondie24 is just one demonstration of a real success in this field and it has inspired many other scientists. In this thesis, artificial neural networks are employed to evolve game playing strategies for the game of checkers by introducing a league structure into the learning phase of a system based on Blondie24. We believe that this helps eliminate some of the randomness in the evolution. The best player obtained is tested against an evolutionary checkers program based on Blondie24. The results obtained are promising. In addition, we introduce an individual and social learning mechanism into the learning phase of the evolutionary checkers system. The best player obtained is tested against an implementation of an evolutionary checkers program, and also against a player, which utilises a round robin tournament. The results are promising. N-tuple systems are also investigated and are used as position value functions for the game of checkers. The architecture of the n-tuple is utilises temporal difference learning. The best player obtained is compared with an implementation of evolutionary checkers program based on Blondie24, and also against a Blondie24 inspired player, which utilises a round robin tournament. The results are promising. We also address the question of whether piece difference and the look-ahead depth are important factors in the Blondie24 architecture. Our experiments show that piece difference and the look-ahead depth have a significant effect on learning abilities