Mohammad Shaker

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In this paper we present a method for the automatic generation of content for the physics-based puzzle game Cut The Rope. An evolutionary game generator is implemented which evolves the design of levels based on a context-free grammar. We present various measures for analyzing the expressivity of the generator and visualizing the space of content covered.(More)
In order to automatically generate high-quality game levels, one needs to be able to automatically verify that the levels are playable. The simulation-based approach to playability testing uses an artificial agent to play through the level, but building such an agent is not always an easy task and such an agent is not always readily available. We discuss(More)
Harrison (1918: J. Exp. Zool. 25: 413-461) described a developmental field as an "equipotential self-differentiating system." The present study was undertaken to address the question: To what extent can be pre-limb territory of a chick embryo be considered a developmental field? To what extent is the chick pre-limb territory an equipotential(More)
We present a demonstration of Ropossum, an authoring tool for the generation and testing of levels of the physics-based game, Cut the Rope. Ropossum integrates many features: (1) automatic design of complete solvable content, (2) incorporation of designer’s input through the creation of complete or partial designs, (3) automatic check for playability and(More)
PCG approaches are commonly categorised as constructive, generateand-test or search-based. Each of these approaches has its distinctive advantages and drawbacks. In this paper, we propose an approach to Content Generation (CG)– in particular level generation – that combines the advantages of constructive and search-based approaches thus providing a fast,(More)
In this paper, we describe a methodology for capturing player experience while interacting with a game and we present a data-driven approach for modeling this interaction. We believe the best way to adapt games to a specific player is to use quantitative models of player experience derived from the in-game interaction. Therefore, we rely on crowd-sourced(More)
This paper introduces an authoring tool for physics-based puzzle games that supports game designers through providing visual feedback about the space of interactions. The underlying algorithm accounts for the type and physical properties of the different game components. An area of influence, which identifies the possible space of interaction, is identified(More)
In this paper, we present an approach for predicting users’ level of engagement from nonverbal cues within a game environment. We use a data corpus collected from 28 participants (152 minutes of video recording) playing the popular platform game Super Mario Bros. The richness of the corpus allows extraction of several visual and facial expression features(More)