English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present the last of a series of three academic essays which deal with thequestion of how and why to build a generalized player model. We propose that ageneral player model needs parameters for subjective experience of play,including: player psychology, game structure, and actions of play. Based onthis proposition, we pose three linked research questions: RQ1 what is anecessary and sufficient foundation to a general player model?; RQ2 can such afoundation improve performance of a computational intelligence- based playermodel?; and RQ3 can such a player model improve efficacy of adaptive artificialintelligence in games?  We set out the arguments behind these research questions in each of the threeessays, presented as three preprints. The third essay, in this preprint,presents the argument that adaptive game artificial intelligence will beenhanced by a generalised player model. This is because games are inherentlyhuman artefacts which therefore, require some encoding of the human perspectivein order to effectively autonomously respond to the individual player. Theplayer model informs the necessary constraints on the adaptive artificialintelligence. A generalised player model is not only more efficient than aper-game solution, but also allows comparison between games which makes it auseful tool for studying play in general. We describe the concept and meaningof an adaptive game. We propose requirements for functional adaptive AI,arguing from first principles drawn from the games research literature. Wepropose solutions to these requirements, based on a formal model approach toour existing 'Behavlets' method for psychologically-derived player modelling:  Cowley, B., & Charles, D. (2016). Behavlets: a Method for Practical PlayerModelling using Psychology-Based Player Traits and Domain Specific Features.User Modeling and User-Adapted Interaction, 26(2), 257-306.

Adaptive Artificial Intelligence in Games: Issues, Requirements, and a Solution through Behavlets-based General Player Modelling