Automatic generation and analysis of physics-based puzzle games

Shaker, Mohammad; Sarhan, Mhd Hasan; Naameh, Ola Al; Shaker, Noor; Togelius, Julian

doi:10.1109/cig.2013.6633633

Cited by 30 publications

(34 citation statements)

References 16 publications

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“…There is no need for any additional playability check afterwards. This is a main advantage over other CG methods proposed in the literature where two separated steps are usually employed to (1) generate complete designs and (2) performs a playability check [9,10] which results in a significantly slower performance than the one obtained by the proposed approach.…”

Section: Notes On the Progressive Approachmentioning

confidence: 95%

“…all the components presented are necessary, and should be used, to complete the level, unlike previous attempts to generate content where this is not guaranteed [9][10][11]. This is an important issue in game design since the number of paths that could be followed to finish the game provides an estimate of difficulty.…”

Section: Notes On the Progressive Approachmentioning

confidence: 99%

“…The possible actions that could be performed thereafter are: a rope cut, an air-cushion press, a bubble pop, a rocket press or a void action (not taking any action, i.e, waiting for the candy to reach a certain position). More details about the different game components and the possible interactions can be found in [9].…”

Section: Customising the Framework For Cut The Ropementioning

confidence: 99%

“…Colour maps is one of the methods used for this purpose [9]. This is done by generating a large amount of game content, converting each instance into a pixelated image and projecting all instances on a single image.…”

Section: Generator Expressivity Analysismentioning

confidence: 99%

“…While it takes the simulation-based and the random agents 470 and 82 sec to evolve a complete playable level [9,10], the proposed methodology is able to evolve timelines with more than one associated design in 9.79 sec while ensuring that all generated entities are used in all successful playthroughs.…”

Section: Comparison With Previous Attemptsmentioning

confidence: 99%

See 4 more Smart Citations

A Progressive Approach to Content Generation

Shaker

Togelius

et al. 2015

Applications of Evolutionary Computation

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Abstract. 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, flexible and reliable way of generating diverse content of high quality. In our framework, CG is seen from a new perspective which differentiates between two main aspects of the gameplay experience, namely the order of the in-game interactions and the associated level design. The framework first generates timelines following the search-based paradigm. Timelines are game-independent and they reflect the rhythmic feel of the levels. A progressive, constructive-based approach is then implemented to evaluate timelines by mapping them into level designs. The framework is applied for the generation of puzzles for the Cut the Rope game and the results in terms of performance, expressivity and controllability are characterised and discussed.

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Section: Notes On the Progressive Approachmentioning

confidence: 95%

Section: Notes On the Progressive Approachmentioning

confidence: 99%

Section: Customising the Framework For Cut The Ropementioning

confidence: 99%

Section: Generator Expressivity Analysismentioning

confidence: 99%

Section: Comparison With Previous Attemptsmentioning

confidence: 99%

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A Progressive Approach to Content Generation

Shaker

Togelius

et al. 2015

Applications of Evolutionary Computation

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Adaptive Puzzle Generation for Computational Thinking

Scirea

2020

Lecture Notes in Computer Science

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This paper describes a system to generate puzzles with a difficulty degree that adapts to the player. The puzzle is designed with the objective of being used by young pupils, and it is mainly a planning/sequencing task, which is considered one of the aspects of computational thinking. The system is powered by a constrained multi-objective algorithm (NSFI-2Pop) -which evolves the sequences of actions necessary to solve the puzzle -combined with a stochastic algorithm that translates the sequences in playable levels. We also present a pilot evaluation of the system, which seems to indicate that the levels presented to the player are perceived as having an increasing difficulty.

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A Projection-Based Approach for Real-Time Assessment and Playability Check for Physics-Based Games

Shaker

Abou-Zleikha

et al. 2015

Applications of Evolutionary Computation

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Abstract. 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 for each component. The influence areas of all components in a given design are then merged considering the components' type and the context information. The tool can be used offline where complete designs are analyzed and the final interactive space is projected, and online where edits in the interactive space are projected on the canvas in realtime permitting continuous assistance for game designers and providing informative feedback about playability.

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Automatic generation and analysis of physics-based puzzle games

Cited by 30 publications

References 16 publications

A Progressive Approach to Content Generation

A Progressive Approach to Content Generation

Adaptive Puzzle Generation for Computational Thinking

A Projection-Based Approach for Real-Time Assessment and Playability Check for Physics-Based Games

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