If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Artificial Intelligence is using data to change the world. Have you considered how AI might be changing architecture? Architects are not sure what to think about Artificial Intelligence. You are probably very familiar with how AI will change industries, like cybersecurity, medicine, and manufacturing. The core issue centers around the idea that creatives will be replaced by super-intelligent robots to design buildings, create art, or design vehicles.
Starting up in her 50s, Swati Padmaraj is bringing style gurus to one's home and increase a retailer's engagement with customers with machine learning platform'Style-At-Iz' by Atiz Fashion House. After 25 years of being a housewife and a mother, anyone would want to retire after the kids are off to college. But, Swati Padmaraj actually went back to college in 2011 to study a degree in designing and sourcing apparel at Seattle University to fulfil her childhood dream of being a fashion designer. Funnily enough, the master's degree in chemistry she got 33 years ago played a vital role in her becoming a fashion designer. "I realised how to use different materials and create my own brand," says Swati, founder of Atiz Fashion House, which owns startup Style-At-Iz.
As enterprise end-users and customers alike have embraced the digital life, the need for well-designed user experience (UX) has intensified. With so many applications, platforms and services that continue to change day by day, or even hour by hour, UX has become a major force in its own right. Now, the world is moving to artificial intelligence (AI), which promises to greatly enhance UX, working behind the scenes to deliver automatic and intuitive responses to user requests. The benefits of AI go even deeper. A recent survey of design professionals by Adobe finds more than half, 62%, expressed interest in AI and machine learning and what they add to the creative process.
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Artificial intelligence already touches our lives both directly and indirectly; it works in the open and it works behind filters, apps, APIs, and other processes. AI promises immense change but this evolution is also a cause for concern. When decisions are driven by black box algorithms, the ripples of AI's influence are often difficult to measure. AI's new technologies and novel effects are spurring new methods of design and development. This marks the beginning of the age of relationship design.
The process of play testing a game is subjective, expensive and incomplete. In this paper, we present a play-testing approach that explores the game space with automated agents and collects data to answer questions posed by the designers. Rather than have agents interacting with an actual game client, this approach recreates the bare bone mechanics of the game as a separate system. Our agent is able to play in minutes what would take testers days of organic gameplay. The analysis of thousands of game simulations exposed imbalances in game actions, identified inconsequential rewards and evaluated the effectiveness of optional strategic choices. Our test case game, The Sims Mobile, was recently released and the findings shown here influenced design changes that resulted in improved player experience.
Mixed-initiative PCG systems provide a way to leverage the expressive power of algorithmic techniques for content generation in a manner that lowers the technical barrier for content creators. While these tools are a proof of concept of how PCG systems can aide aspiring designers reach their vision, there are issues pertaining capturing designer intent, and interface complexity. In this paper we introduce CADI (Conversational Assistive Design Interface) a mixed initiative PCG system for creating variations of the game Pong that utilizes natural language input through a natural language interface to explore the design space of Pong variations. We provide a motivation for the creation of CADI and discuss the implementation and design decisions taken to address the issues of designer intent and interface complexity in mixed-initiative PCG systems.
Aytemiz, Batu (University of California, Santa Cruz) | Karth, Isaac (University of California, Santa Cruz) | Harder, Jesse (University of California, Santa Cruz) | Smith, Adam M. (University of California, Santa Cruz) | Whitehead, Jim (University of California, Santa Cruz)
Most tutorials in video games do not consider the skill level of the player when deciding what information to present. This makes many tutorials either tedious for experienced players or not informative enough for players who are new to the given genre. With Talin, implemented as an asset in the Unity game engine, we make it possible to create a mastery model of an individual player's skill levels by operationalizing Dan Cook's skill atom theory. We propose that using this mastery model opens up a new design space when it comes to designing tutorials. We show an example tutorial implementation with Talin assembled using only graphical components provided by our framework, without the need of writing any code. The dynamic tutorial implementation results in the player receiving information only when they need it, whenever they need it. While the novice player is given all the information they need to learn the system, the expert player is not bogged down by tooltip pop-ups regarding mechanics they have already mastered.
In order to create well-crafted learning progressions, designers guide players as they present game skills and give ample time for the player to master those skills. However, analyzing the quality of learning progressions is challenging, especially during the design phase, as content is ever-changing. This research presents the application of Stratabots — automated player simulations based on models of players with varying sets of skills — to the human computation game Foldit. Stratabot performance analysis coupled with player data reveals a relatively smooth learning progression within tutorial levels, yet still shows evidence for improvement. Leveraging existing general gameplaying algorithms such as Monte Carlo Evaluation can reduce the development time of this approach to automated playtesting without losing predicitive power of the player model.
In this paper we present an approach to using sequence analysis to model player behavior. This approach is designed to work in game development contexts, integrating production teams and delivering profiles that inform game design. We demonstrate the method via a case study of the game T om Clancy’s The Division, which with its 20 million players represents a major current commercial title. The approach presented provides a mixed-methods framework, combining qualitative knowledge elicitation and workshops with large-scale telemetry analysis, using sequence mining and clustering to develop detailed player profiles showing the core game-play loops of The Division’s players.