Viewpoint Of Player Research Articles

An applied multi-agent system within the framework of a player-centered probabilistic computer game

The probabilistic model to represent the behavior of an applied multi-agent system that introduces the interaction between a set of agents and Player has been developed within the framework of player-centered probabilistic computer games. The approach features are given with the aid of a game developed for testing cognitive abilities. The agent’s behavior is nondeterministic and therefore unpredictable from Player viewpoint. The system allows both coordinated and autonomous agent’s behavior that depends on availability of information about the presence and position of workable agents for each other. Agent’s behavior is determined with the aid of the algorithm that includes identification of the probabilistic model parameters using maximized objective functions representing individual and group probabilities for Player defeating. Both the model and algorithm ensure the behavior control for relevant applied multi-agent systems.

Measuring the level of difficulty in single player video games

In this paper, we discuss the interest and the need to evaluate the difficulty of single player video games. We first show the importance of difficulty, drawing from semiotics to explain the link between tension-resolution cycles and challenge with the player’s enjoyment. Then, we report related work on automatic gameplay analysis. We show through a simple experimentation that automatic video game analysis is both practicable and can lead to interesting results. We argue that automatic analysis tools are limited if they do not consider difficulty from the player point of view. The last two sections provide a player and Game Design oriented definition of the challenge and difficulty notions in games. As a consequence we derive the property that must fulfil a measurable definition of difficulty.

Structuring a Competitive Analysis: Decision Trees, Decision Forests, and Payoff Matrices

Most business decisions involve significant uncertainty and risk. Decision trees can be helpful in analyzing the key drivers of profitability and in designing risk-mitigation strategies. When analyzing competitive situations, each player's viewpoint can be represented by a separate tree, creating a decision forest. In some situations, it is also useful to summarize two players' trees in a single payoff matrix. This note introduces those concepts step by step.

The construction of subjective games by motive distributions in n -person non-cooperative game

The purpose of this paper is to propose the model considering the subjective viewpoint of player in a n -person non-cooperative game. It is realized by taking in player's subjective feeling for values as his motive distribution. Subjective games and subjective Nash equilibrium strategies are defined, and some examples of motive are shown. By using the subjective game, we can rationally explain various possibilities in selecting a strategy by a player.

多視点テニス画像からのプレーヤ視点画像の生成

We propose a new method for synthesizing player viewpoint images from multiple view tennis videos.Our method uses two key techniques : virtual-view synthesis and player's viewpoint estimation. In the former, we divide the object tennis scene into sub-regions, which are the background, tennis court ground, players, and ball. For each sub-region, a virtualviewpoint image is synthesized by considering the geometrical conditions of each region. Then the virtual viewpoint images of the sub-regions are merged into a virtual viewpoint image of the whole scene. In virtual viewpoint image generation, view interpolation, which is a way of synthesizing images at an intermediate viewpoint to two views of the real object, restricts the viewpoint to a position between the two views. To avoid this restriction, we propose our first key technique, whereby we are able to flexibly position the viewpoint. In our second technique, the viewpoint is computed using epipolar geometry from the center of gravity of a player. By applying the computed player's viewpoint to the former technique, we can synthesize player viewpoint images. Experimental results demonstrate that the proposed method can successfully provide a video of the tennis player's viewpoint from multiple-view video images.

Asymptotically Efficient Adaptive Strategies in Repeated Games Part I: Certainty Equivalence Strategies

This paper addresses the problem of dynamic decision making in an uncertain and competitive environment. A decision maker (player 1) faces a system about which he has some (parametric) uncertainty, and which is affected also by the actions of other agents. We focus on a worst-case analysts from the viewpoint of player 1, using the simplified model of a repeated matrix game with lack of information on one side, where single-stage rewards are random but announced, and perfect observations are assumed. Certain ideas from the field of stochastic adaptive control are used to formulate performance criteria in a non-Bayesian setting, and to devise appropriate control strategies. The basic performance measure is the total reward accumulated by player 1 over all stages played; the purpose of player 1 is to guarantee that his expected total reward will be “close” to what he could guarantee under complete information. The present paper considers adaptive decision strategies of the Certainty Equivalence type, based on a (modified) Maximum Likelihood estimator, and studies their asymptotic (long-term) performance. A sequel paper will be devoted to “asymptotically optimal” strategies.