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Node Based Parallel Game Tree Search Algorithm Using Accelerated GPU

Node Based Parallel Game Tree Search Algorithm Using Accelerated GPU

PVS, EPVS, DTS algorithms are implemented using tree based approach consist of drawbacks like searching is done using DFS manner, it requires more time for computation. Game is splitted into number of possible choices that are considered as possible moves which is next best move for player. Using a tree-based approach many choices of games are computed serially by processor in DFS manner. Due to the SIMD feature of GPU, the tree based approach cannot be easily adopted in GPU. Different from tree-based approach, node-based approach is advantageous in which CPU generates number of possible trees contains the nodes as well as leaf. Number of possible moves in form of tree is created on CPU. CPU is responsible for execution control as well as it is responsible for maintaining the game tree structure. Representation of the game is shown in figure 3. Shows Connect4 is a two-player connection game in which the players first choose a “X” pieces and then take turns dropping “X” discs from the top into a seven column, eight rows vertically suspended grid. The pieces fall straight down, occupying the next available space within the column. The objective of the game is to connect four of one's own discs of the same piece next to each other vertically, horizontally, or diagonally before your opponent. Connect Four is a strongly solved game.
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Parallelization of Node Based Game Tree Search Algorithm on GPU

Parallelization of Node Based Game Tree Search Algorithm on GPU

Representation of the game is shown as tree in figure 2. The tree starts at the initial position of the game as its root. Moves of the players are represented by “X” and “O”. The player represented by the “X”, plays game first and each node in this tree is a possible situation of the game. Possible moves by another player for “O” are generated on the CPU which is tree structure. Evaluation of all nodes, leafs are done by number of threads. Calculation of many tree nodes is done in the same depth in the current game tree, which is the BFS search. Further each cycle in the search process will take in the deepest nodes of the current game tree, which is the DFS search. That means on DFS approach CPU works to calculate nodes, since CPU will execute faster than GPU in this situation and on BFS approach GPU used for calculating the branch and the leaf nodes in parallel. By this hybrid manner, our algorithm is fully utilizes both architectures.
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Alpha Beta Pruning in Mini Max Algorithm –An Optimized Approach for a Connect 4 Game

Alpha Beta Pruning in Mini Max Algorithm –An Optimized Approach for a Connect 4 Game

Fig-1: Initial Game Tree in case of Mini-Max It belongs to the category of backtracking algorithm therefore the decision is made by backtracking after trying all the possible moves. Initially maximizer has the option to go left or right. Assuming it goes left after which it is the minimizer’s turn. The minimizer has a choice to make between 2 and 4. The aim of minimizer is to minimize its value therefore it chooses the least value among both, which is equal to 2. Next maximizer goes right after which it is the minimizer’s turn. The minimizer has to choose a value between 1 and 8. It will definitely choose 1 over 8. Now it is maximizer’s turn to choose between 2 and 1. Maximizer’s aim is to get the maximum value possible therefore it chooses the larger value of the both that is 2. So finally the maximizer gets the optimal value of 2 and its best interest lies in going left. The game tree used in the example is very small and is just used for the purpose of describing the concepts clearly and easily but in reality Connect-4 game results in a game tree with a large number of game states. To find and compute each of the game state is virtually impossible. The depth of the tree may be six with a branching factor of seven. Therefore a large number of game states are computed.
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Game Theory Explorer - Software for the Applied Game Theorist

Game Theory Explorer - Software for the Applied Game Theorist

Computing equilibria is a main research topic of the authors, and in later sections we will explain some of our results on finding equilibria of two-player games in strategic form (Avis et al., 2010) and extensive form based on the “sequence form” (von Stengel, 1996). Scientific algorithms are often implemented as prototopes to show that they work, as done, for example, by Audet et al. (2001), or von Stengel, van den Elzen, and Talman (2002). However, providing a robust user interface to create games is much more involved, and necessary to make such algorithms useful for a wider research community. In particular, the drawing of game trees should be done with a friendly graphical user interface (GUI) where the game tree can be created and seen on the screen, and be stored, retrieved, and changed in an intuitive manner. This is one of the purposes of the GTE software presented in this article.
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Ensemble Determinization in Monte Carlo Tree Search for the Imperfect Information Card Game Magic: The Gathering

Ensemble Determinization in Monte Carlo Tree Search for the Imperfect Information Card Game Magic: The Gathering

Abstract —In this paper, we examine the use of Monte Carlo Tree Search (MCTS) for a variant of one of the most popular and profitable games in the world: the card game Magic: The Gath- ering (M:TG). The game tree for M:TG has a range of distinctive features, which we discuss here, and has incomplete information through the opponent’s hidden cards, and randomness through card drawing from a shuffled deck. We investigate a wide range of approaches that use determinization, where all hidden and random information is assumed known to all players, alongside Monte Carlo Tree Search. We consider a number of variations to the rollout strategy using a range of levels of sophistication and expert knowledge, and decaying reward to encourage play urgency. We examine the effect of utilising various pruning strategies in order to increase the information gained from each determinization, alongside methods that increase the relevance of random choices. Additionally we deconstruct the move generation procedure into a binary yes/no decision tree and apply MCTS to this finer grained decision process. We compare our modifications to a basic MCTS approach for Magic: The Gathering using fixed decks, and show that significant improvements in playing strength can be obtained.
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The Application of Monte Carlo Sampling to Sequential Auction Games with Incomplete Information: An Empirical Study

The Application of Monte Carlo Sampling to Sequential Auction Games with Incomplete Information: An Empirical Study

come to a decision node that we need to make a decision for, we first compute a hash value according to the sub-game tree structure and the opponents’ bid history behavior and then use this value to lookup the policy decision. If a policy has been recorded for this node, we can use it. Otherwise, it means we did not record an equilibrium strategy path through this node in the sampling steps. In such case, we will try to find a similar decision node and adopt its strategy. When opponents have identical distributions for valuations, we consider a node similar if it has a d ifferent sub-game structure but with the same opponents’ history behavior. For example, node A with a sub-game tree of players 1, 2 and 4 and node B with a sub-game tree of players 1, 3 and 4 have different sub-game structures. If their opponents’ history behaviors are isomorphic , (Assuming player 1 is our agent), then we can say node A and node B are similar. In our simulations, we were able to find a strategy for most of the decision nodes in this way, if the similarity lookup failed, we randomly picked one choice or assigned equal probability to each choice.
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AINN ICT4101 FS

AINN ICT4101 FS

Idea: – Do depth first search to generate partial game tree – Cutoff test : • Depth limit • Iterative deepening • Cutoff when no big changes quiescent search – When cutoff, apply static [r]

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A Functional Imaging Study of Cooperation in Two Person reciprocal Exchange

A Functional Imaging Study of Cooperation in Two Person reciprocal Exchange

Behavioral Protocol. Subjects responded to cash-payoff salient features of a visually presented two-person binary game tree by pressing response buttons with their right (move right) or left hand (move left). The subjects played the role of either first decision maker or second decision maker in each game. Second decision makers saw the first decision makers’ choice before making their decision. Subjects were matched with either a human or computer counterpart and were visually informed of their counterpart’s type before seeing the game tree. When the subject in the scanner played the computer they were told that it would play a fixed probabilistic strategy of 75% left and 25% right as DM2 and that the computer plays 100% right as DM1. We provided this information to the subjects to reduce the chances that the subjects would try to predict the experimenters’ intentions. Similarly, when the computer moved, it did so immediately to make it less likely that subjects would anthro- pomorphize the computer responses. The task was administered in six scanning runs. Each run consisted of 12 randomly pre- sented games with different payoffs with counterbalanced roles and counterparts. Behavioral and functional MRI data were recorded simultaneously from 12 right-handed subjects who were trained before entering the scanner. The subjects provided written informed consent.
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Monte Carlo Tree Search and Its Applications

Monte Carlo Tree Search and Its Applications

In order to address problems with larger search spaces, we must turn to alternative methods. Monte Carlo tree search (MCTS) has had a lot of success in Go and in other appli- cations [2] [1]. MCTS eschews the typical brute force tree searching methods, and utilizes statistical sampling instead. This makes MCTS a probabilistic algorithm. As such, it will not always choose the best action, but it still performs rea- sonably well given sufficient time and memory. MCTS per- forms lightweight simulations that randomly select actions. These simulations are used to selectively grow a game tree over a large number of iterations. Since these simulations do not take long to perform, it allows MCTS to explore search spaces quickly. This is what gives MCTS the advantage over deterministic methods in large search spaces.
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PP 2009 07: 
  Decisions, Actions, and Games: a logical perspective

PP 2009 07: Decisions, Actions, and Games: a logical perspective

Soft update But belief dynamics is often driven by events of soft information, which do not eliminate worlds, but merely rearrange their plausibility ordering (van Benthem 2007A), as happens in the familiar model-theoretic ‘Grove sphere semantics’ of belief revision theory. In the above, we already cast Backward Induction in this manner, as a way of creating plausibility relations in a game tree – but beyond such an ‘off-line’ preprocessing phase of a given game, there can also be dynamic ‘on-line’ events that might change players’ beliefs and expectations in the course of an actual play of the game. With doxastic-temporal models adapted to this setting, we get representation theorems (van Benthem & Dégrémont 2008) that say which doxastic-temporal models are produced by plausibility update in the style of Baltag & Smets 2006. Also, Baltag, Smets & Zvesper 2008 provide a striking new dynamic alternative to Aumann-style characterization theorems for Backward Induction.
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Game of the Cursed Prince based on Android

Game of the Cursed Prince based on Android

Author collecting data and information on the issues discussed, the authors read and study the results of emerging technologies such as the internet, e-books, films, folklore, and others related to the study of the Cursed Prince's story for reference. System development method used in completing this Final Project using Waterfall model consisting of: (a) Stages of planning conditions at this stage high-level users decide what functions should be featured by the game using the Unity3d. (b) Stages of user design, at this stage done the design process and interface design of the game using Unity3d then convert to Android. (c) Construction Phase, at this stage coding of designs has been defined. (d) Phase of Implementation, at this stage testing and testing analysis of games created using Unity3d
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A characterization of tree-like resolution size

A characterization of tree-like resolution size

We explain an asymmetric Prover-Delayer game which precisely characterizes proof size in tree-like Resolu- tion. This game was previously described in a parameterized complexity context to show lower bounds for parameterized formulas [BGL13] and for the classical pigeonhole principle [BGL10]. The main point of this note is to show that the asymmetric game in fact characterizes tree-like Resolution proof size, i. e. in princi- ple our proof method allows to always achieve the optimal lower bounds. This is in contrast with previous techniques described in the literature. We also provide a very intuitive information-theoretic interpretation of the game.
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The game of hospitality

The game of hospitality

In other studies, efforts to turn parents into responsible citizens are seen as a part of a governmentality regime. Partnerships are not only a matter of supporting the child’s learning processes but also a matter of governing the family (Franklin et al., 1998; Newman, 2001: 154-160). Nikolas Rose writes about responsibilisation of the family (Rose, 1999: 74), and as he points out, ‘such a moralising ethico-politics tends to incite a “will to govern” which imposes no limits upon itself’ (Rose, 1999: 192). With regard to the reading pledge included in home-school contracts, he has this to say: ‘In what other politics would elected politicians seek to use the apparatus of the law to require parents to read to their children for a fixed period each day?’ (Rose, 1999: 193). My point here differs from Rose’s in several ways: What I analyse is not ‘the apparatus of law’ but interactions framed as a game. And the expectations of the parents do not take the form of requirements but of an invitation to the parents to see the family in an educational gaze. And what is being governed is not only the self and the self’s relation to itself – but the self’s relation to the community, ‘the mutual’ of home and school, and the creation of the mutual responsibility.
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Game Developer   Game Career Guide Fall 2005 pdf

Game Developer Game Career Guide Fall 2005 pdf

Although we received 3,913 unique responses worldwide, not all who participated in this survey provided sufficient compensation information to be included in the findings. We also excluded cases in which the compensation was given at less than $10,000 or greater than $300,000 or if there was text entered that did not readily correspond to a compensation figure. We further excluded records missing key demographic and classification information. Finally, this report is of the U.S. compensation only, excluding approximately 1,389 otherwise valid respondents from outside the U.S. So the total sample reflected in the compensation data presented in the following pages is 2,091, smaller than the original number of respondents, but still very comprehensive. The sample represented in our salary survey can be projected to the overall game developer community with a margin of error of plus or minus 2.1 percent at the 95 percent confidence level. The margin of error increases for specific subgroups reported within this community.
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Computer Gaming World   Mar 2006 pdf

Computer Gaming World Mar 2006 pdf

It’s not so little anymore. Crytek’s staff now tops 100 and includes artists, design- ers, producers, programmers, and sound engineers from 23 countries (up from 16). “We’ve always tried to be different as a fun- damental principle,” explains Crytek CEO and president Cevat Yerli. “We decided early on that no matter where we were located, we should be international. Whether it’s the organization, the way we work, our projects, the technology we use, or our toolsets, we try to approach everything we do uniquely.” Most fans of critical darling Far Cry would agree. Scrubbing trigger-play stereotypes from its limber CryEngine, Crytek stole much thun- der from powerhouses like id Software and Valve. Still, Far Cry has its share of foibles. The game’s first half sees you unceremoniously chucked into an island sandbox swarming with brutally cunning mercenaries, but later game- play devolves into sophomoric run-and-gun hokum. And the cliché-ridden story is often plain batty, careening from serious to silly with- out comfortably inhabiting either medium.
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Elsevier Creating Games with Unity and Maya pdf

Elsevier Creating Games with Unity and Maya pdf

Back in 3D world, the problem is that all this bouncing light is a nontrivial calculation. In fact for a long time, most rendering solutions did not calculate any bounced light at all; when a light ray hit a surface, it stopped. This meant that the area behind the surface received no light (from this light source) and thus rendered black, as a shadow. At first blush, this sounds fine. The problem is that real shadows in most situations are never black. There is enough bounced light shooting around the environment to still illuminate the objects in shadow, and thus the grass under a tree on a sunny day is simply less lit, not black. As rendering technologies matured, radiosity-based solutions emerged that simulated bounced light. In Maya, MentalRay is typically the rendering engine to enable this. Through tools like Final Gather and Global Illumination, nice diffuse light appears to be bounced across surface and the light even picks up the color of the surface it has just bounced off of (actually the lit surface is really just sampling surfaces around it through stochastic samples, but let's not split hairs). The problem is that all this beautiful bounced light simulation is processer intensive. Rendering a complex scene with great sampling rates was a very time-intensive task. It just is not that unusual to have 12- or 15-hour renders for a single frame, which of course won't work in game situations. Hence the need to bake the lighting. Previous to Unity 3, baking in Maya could yield some very nice results that could then be brought into Unity (with the time-intensive reconstruction of materials). Now though, via Beast (the technology for which Unity Technologies has a license), a built-in radiosity-like renderer will calculate a render that includes bounced light that yields much more believable shadows and even color bleed.
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Game Developer   Game Career Guide Fall 2008 pdf

Game Developer Game Career Guide Fall 2008 pdf

Meanwhile, an Advisory Board of industry leaders, including luminaries from Activision, BioWare, Namco, and Ubisoft, helps to shape and adapt the curriculum on an ongoing basis to meet the needs of an evolving industry. Reflecting the collaborative nature of the game industry, at various stages of the development process, students gain valuable experience by working with other production-oriented programs at VFS and drawing on the talent and expertise of their peers in disciplines like Sound Design, Acting, and Animation & Visual Effects. Students at VFS have the advantage of living and creating in Vancouver, B.C., Canada. Along with strong film, TV, and animation industries, Vancouver is a world center of game development, with dozens of developers based in this beautiful and cosmopolitan city, from the smallest independents to industry giants. That means that VFS can remain industry-current, host regular guest speakers, and provide all students with the chance to present their final playable games to an audience of industry representatives and recruiters: a unique opportunity to demonstrate their skills and make vital professional contacts. The proof is in the results. Since the Game Design program’s inception in 2004, graduates have gone on to work for dozens of companies in Vancouver and beyond. Recent graduates have been hired in Designer and Producer roles at companies like Backbone Entertainment, BioWare, EA Black Box, EA Mythic, Pipeworks Software, Propaganda Games, Radical, and Relic, and recent and upcoming credits include Mass Effect, skate, Prototype, and Warhammer Online. To learn more about Game Design at VFS, visit vfs.com/gamedesign.
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Teaching games through the A.G.E. framework

Teaching games through the A.G.E. framework

For example, in a platform game like "Super Mario Bros." (Nintendo, 1985) the rule "the player can kill enemies by landing on top of them" links the jumping and falling actions to the "fighting enemies" gameplay while the rule "player will progress to the next stage by reaching an end-level flagpole" links the running and jumping actions to the race-to-an-end gameplay. Similarly, the ultimate goal of saving the princess serves as a motivation to link the fighting and race-to-an-end gameplay to the emotional experience of players, namely excitement and curiosity for the mission and for being the sole protector of the beautiful and elusive damsel in distress.
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A game characterisation of tree-like Q-resolution size

A game characterisation of tree-like Q-resolution size

would suffice to get the lower bound. Our second example are the well-known KBKF(t)-formulas of Kleine B¨ uning, Karpinski and Fl¨ ogel [20]. In the same work [20], where Q-Resolution was introduced, these formulas were suggested as hard formulas for the system. Very recently, the formulas KBKF(t) were even shown to be hard for IR-calc, a system stronger than Q-Resolution [8]. In fact, a number of further separations of QBF proof systems builds on the hardness of KBKF(t) [3, 16] (cf. also [8]). Here we use our new technique to show that these formulas require exponential-size proofs in tree-like Q-Resolution. In terms of the lower bound, this result is weaker than the result obtained in [8]. However, it provides an interesting example for our new game technique. In contrast to the first example, both the Delayer strategy as well as the scoring analysis is technically involved. Here we need the refined asymmetric game. The formulas KBKF(t) have very unbalanced proofs, so we cannot use a symmetric Delayer, as symmetric games only yield a lower bound according to the largest full binary tree embeddable into the proof tree (cf. [10]).
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Predicting Outcomes of Chess Endgames Using Machine Learning Algorithms

Predicting Outcomes of Chess Endgames Using Machine Learning Algorithms

By using data mining techniques predicting the outcome of the chess endgames is a time consuming task. So in the proposed system we will use different supervised classification models to find the accuracy given by the each model and finally selects the best model which gives the highest accuracy. Some of the classification models which used are Logistic Regression, Random Forest, Support Vector Machine and Decision Tree. Task flow involved during developing of this application is

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