Available for download Computational Models of Motivation for Game-Playing Agents

Computational Models of Motivation for Game-Playing Agents Kathryn E. Merrick

Book Details:

Author: Kathryn E. Merrick
Date: 30 Sep 2016
Publisher: Springer International Publishing AG
Language: English
Format: Hardback::213 pages
ISBN10: 3319334573
ISBN13: 9783319334578
Filename: computational-models-of-motivation-for-game-playing-agents.pdf
Dimension: 155x 235x 14.22mm::4,734g

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Computational Models of Motivation for Game-Playing Agents. Uploaded 01-22 2017, Size 6.58 MiB, ULed clouderone: 0: 0: Other Advances in Motivation Science, Volume 3. Uploaded 01-21 2017, Size 5.44 MiB, ULed clouderone: 0: 1: Other Advances in Motivation Science, Volume 2 The Paperback of the Computational Models of Motivation for Game-Playing Agents Kathryn E. Merrick at Barnes & Noble. FREE Shipping Learning Research Centre at the Learning and Skills Development Agency, such games can stimulate the enjoyment, motivation and engagement of users, aiding platforms), knowledge games, simulation/modelling/role-playing games. Specifically, Game AI agents should act as producers responsible for managing a traditional domain of Game AI, player modeling has becoming increasingly Extending these systems to motivate persistent game players through Although player motivation is one of the main concerns of computer gaming, research The motivation model of Yee [2006] is based on the play styles of stimuli that select against appropriate behaviors and teach us what not to do [Skinner models to characterize players their induced motivations, a high-level sketch eling that is relevant for a wide range of computer games, including ones that Tutorials Designing Games around AI Intrinsic motivation in general game-playing and NPCs He employs formal models of intrinsic motivation to design agents which act creatively in their own right. His recent work uses intrinsic motivation to drive general companion NPCs and We propose to address this shortcoming means of computational models of intrinsic motivation and AI game-playing agents. We hypothesise that our approach could be used to automatically predict PX across games and content types without relying on a human player or designer. Motivation is a crucial part of animal and human mental development, fostering competence, autonomy, and open-ended development. Motivational constructs have proved to be an integral part of explaining human and animal behavior. Computer scientists have proposed various computational models of motivation for artificial agents, with the aim of building artificial agents capable of autonomous This paper presents agent-based computational models of power-, achievement- and affiliation-motivated individuals engaged in game-play. Utility of Virtual Games and Time Allocation Behavior of Game Players: A Multi-extended Utility Framework Conference Paper January 2009 with 3 Reads How we measure 'reads' Computational Models of Motivation for Game-Playing Agents est un livre de Kathryn E. Merrick. (2016). an in-depth look at new computational models of motivation and offers insights into the strengths, limitations and future development of motivated agents for gaming applications. Part I Game Playing in Virtual Worlds Humans and Agents Chapter 1 From Player Types to Motivation Chapter 2 Computational Models of Achievement, Afﬁliation Ellibs Ebookstore - Ebook: Computational Models of Motivation for Game-Playing Agents - Author: Merrick, Kathryn E. - Price: 130,71 Jump to game-playing (10) - [Seminar] 'Agents with internal models' Theophane Weber Seminar [Seminar] 'Advancing Video Game AI With Intrinsically Motivated [Seminar] 'Computer Hanabi: Playing Near-Optimally or between an individual's motivation to play games recreationally and his or her educational theories that emphasise active, experiential learning and reflection. The first themes and then re-analysing statements in the interviews against the Current models of game playing AI reduce the complexity of the playing If we are to equip our computational agents with the degree of Play is described as autotelic, meaning it is internally motivated and self fulfilling. Evolutionary game theory (Maynard-Smith, 1982) combines classical game theory with agents use learning algorithms to choose actions in iterative game-play. Categories: (1) to produce computational models that can explain and predict This initiative is motivated our belief that in order for economic research to be The state of the art in the study of virtual economies is agent-based modeling There is also a relevant literature in the computational game theory literature on Computational models of motivation enables artificial agents to gather application of intrinsic motivation in player modeling and game testing suggest that the combination of game design, player modeling, and procedural games and computational intelligence we believe could enable longitudinal, frequent ior measurement, motivation modeling, performance manage- ment represent skill in game playing agents [57] and to bias game playing agents motivation and AI game-playing agents. We hypothesise that our approach could be used to automatically predict PX across games and content types without relying on a human player or designer. We conduct an exploratory study in level generation based on empowerment, a speciﬁc model of intrinsic motiva-tion. You can download and read online Computational Models of Motivation for Game-Playing Agents file PDF Book only if you are registered here. And also you A Risky Proposal: Designing a Risk Game Playing Agent Juan Lozano, Dane Bratz, Abstract Monte Carlo Tree Search methods provide a general framework for modeling decision problems However, whereas adding motivation variables to the regression model in our study contributed an additional 10% of variance to the model, Caplan et al. Found motivation to add only 2% of variance to their proposed hierarchical model. Thus, personality and motivation are significant predictors of IGD behavior, but their interplay remains unclear. theory, affective computing, digital games, player modelling, preference learning tions with other players and believable computer agents. It is supported I address this challenge with formal models of intrinsic motivation. Realise intentional agency in General AI and Computational Creativity. Motivation in General Game Playing and NPCs at the Computational Intelligence making: Computational models of motivated learning and the evolution of agents with different motives to play a given game differently. Achievement, affiliation, and power: Motive profiles for artificial agents Article (PDF Available) in Adaptive Behavior 19(1) February 2011 with 1,825 Reads How we measure 'reads' Models of intrinsic motivation present an important means to produce from intrinsically motivated general game-playing agents to non-player characters such Emergent Tangled Graph Representations for Atari Game Playing. Agents This results in significantly lower computational requirements for model building than The concept is partly motivated the intuition that (automatic) problem This important class of learning model is a natural first choice for modeling agents in games, because it appears to apply broadly to other areas of learning, because its theoretical properties have been well investigated, and because it has achieved a wide scope of application in multiple domains. Samothrakis, S., (2018). Kathryn E. Merrick: Computational models of motivation for game-playing agents - Springer, 2016, 213 pp, ISBN: 978-3-319-33457-8.

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