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Design and Interaction: The Development of Video Games

Description

The objective of this project concentrates on the game Defense of the Ancients 2 (Dota 2). In this game, players are constantly striving to improve their skills, which are fueled by the competitive nature of the game. The design influences

The objective of this project concentrates on the game Defense of the Ancients 2 (Dota 2). In this game, players are constantly striving to improve their skills, which are fueled by the competitive nature of the game. The design influences the community to engage in this interaction as they play the game cooperatively. This thesis illustrates the importance of player interaction in influencing design as well as how imperative design is in affecting player interaction. These two concepts are not separate, but are deeply entwined. Every action performed within a game has to interact with some element of design. Both determine how games become defined as competitive, casual, or creative. Game designers can benefit from this study as it reinforces the basics of developing a game for players to interact with. However, it is impossible to predict exactly how players will react to a designed element. Designers should remember to tailor the game towards their audience, but also react and change the game depending on how players are using the elements of design. In addition, players should continue to push the boundaries of games to help designers adapt their product to their audience. If there is not constant communication between players and designers, games will not be tailored appropriately. Pushing the limits of a game benefits the players as well as the designers to make a more complete game. Designers do not solely create a game for the players. Rather, players design the game for themselves. Keywords: game design, player interaction, affinity space, emergent behavior, Dota 2

Contributors

Agent

Created

Date Created
2015-05

Collaborating in Motion: Distributed and Stochastic Algorithms for Emergent Behavior in Programmable Matter

Description

The world is filled with systems of entities that collaborate in motion, both natural and engineered. These cooperative distributed systems are capable of sophisticated emergent behavior arising from the comparatively simple interactions of their members. A model system for emergent

The world is filled with systems of entities that collaborate in motion, both natural and engineered. These cooperative distributed systems are capable of sophisticated emergent behavior arising from the comparatively simple interactions of their members. A model system for emergent collective behavior is programmable matter, a physical substance capable of autonomously changing its properties in response to user input or environmental stimuli. This dissertation studies distributed and stochastic algorithms that control the local behaviors of individual modules of programmable matter to induce complex collective behavior at the macroscale. It consists of four parts. In the first, the canonical amoebot model of programmable matter is proposed. A key goal of this model is to bring algorithmic theory closer to the physical realities of programmable matter hardware, especially with respect to concurrency and energy distribution. Two protocols are presented that together extend sequential, energy-agnostic algorithms to the more realistic concurrent, energy-constrained setting without sacrificing correctness, assuming the original algorithms satisfy certain conventions. In the second part, stateful distributed algorithms using amoebot memory and communication are presented for leader election, object coating, convex hull formation, and hexagon formation. The first three algorithms are proven to have linear runtimes when assuming a simplified sequential setting. The final algorithm for hexagon formation is instead proven to be correct under unfair asynchronous adversarial activation, the most general of all adversarial activation models. In the third part, distributed algorithms are combined with ideas from statistical physics and Markov chain design to replace algorithm reliance on memory and communication with biased random decisions, gaining inherent self-stabilizing and fault-tolerant properties. Using this stochastic approach, algorithms for compression, shortcut bridging, and separation are designed and analyzed. Finally, a two-pronged approach to "programming" physical ensembles is presented. This approach leverages the physics of local interactions to pair theoretical abstractions of self-organizing particle systems with experimental robot systems of active granular matter that intentionally lack digital computation and communication. By physically embodying the salient features of an algorithm in robot design, the algorithm's theoretical analysis can predict the robot ensemble's behavior. This approach is applied to phototaxing, aggregation, dispersion, and object transport.

Contributors

Agent

Created

Date Created
2021