Personality Reinforced Search for Mobile Strategy Games

Game AI Pro 2 ◽  
2015 ◽  
pp. 243-254
Author(s):  
Miguel Nieves
Keyword(s):  
Strategy Games

Profile, Gaming Usage and Purposes of Gaming of Internet Café Users in Manila

2013 ◽  
Vol 5 (4) ◽  
pp. 43-58
Author(s):  
Ana Clariza Natanauan ◽  
Jenmart Bonifacio ◽  
Mikael Manuel ◽  
Rex Bringula ◽  
John Benedic Enriquez
Keyword(s):  
Real Time ◽  
Exploratory Study ◽  
Roman Catholic ◽  
Descriptive Statistics ◽  
Future Research ◽  
Chi Square ◽  
Middle Income ◽  
Income Class ◽  
College Degrees ◽  
Strategy Games

This descriptive-exploratory study attempted to give the readers a portrait of cyber café gamers in Manila. It determined the profile of gamers, their gaming usage, and their purposes of cyber café gaming. Descriptive statistics revealed that most of the respondents were Manila settlers, students, pursuing or had obtained college degrees, male, young, Roman Catholic, single, belonged to middle-income class, and played games in cyber cafés in the afternoon once to twice a week. One-way chi-square showed that frequency of gaming was not equally distributed in a week and gamers showed tendency to play games in a cyber in a particular time of the day. Real-time strategy games were the most frequently played games in cyber cafés. To recreate, to relieve boredom, and to have fun were the top three reasons in playing games in cyber cafés. Conclusions and directions for future research were also presented.

scholarly journals Considerations for comparing video-game AI agents with humans

2020 ◽  
Author(s):  
Christopher R Madan
Keyword(s):  
Video Games ◽  
Video Game ◽  
Reaction Times ◽  
Game Play ◽  
Complex Action ◽  
Potential Approach ◽  
Video Game Play ◽  
Game Ai ◽  
Strategy Games ◽  
Action Sequences

Video games are sometimes used as environments to evaluate AI agents' ability to develop and execute complex action sequences to maximize a defined reward. However, humans cannot match the fine precision of timed actions of AI agents--in games such as StarCraft, build orders take the place of chess opening gambits. However, unlike strategy games, such as chess and go, video games also rely heavily on sensorimotor precision. If the `finding' was merely that AI agents have superhuman reaction times and precision, none would be surprised. The goal is rather to look at adaptive reasoning and strategies produced by AI agents that may replicate human approaches or even result in strategies not previously produced by humans.Here I will provide: (1) an overview of observations where AI agents are perhaps not being fairly evaluated relative to humans, (2) a potential approach for making this comparison more appropriate, and (3) highlight some important recent advances in video-game play provided by AI agents.

scholarly journals Learning to Reach Agreement in a Continuous Ultimatum Game

2008 ◽  
Vol 33 ◽  
pp. 551-574 ◽  
Author(s):  
S. De Jong ◽  
S. Uyttendaele ◽  
K. Tuyls
Keyword(s):  
Ultimatum Game ◽  
Social Dilemmas ◽  
Interaction Network ◽  
Multi Agent Systems ◽  
Scale Free ◽  
Agent Systems ◽  
Strategy Games ◽  
Common Strategy ◽  
Multi Agent ◽  
Free Interaction

It is well-known that acting in an individually rational manner, according to the principles of classical game theory, may lead to sub-optimal solutions in a class of problems named social dilemmas. In contrast, humans generally do not have much difficulty with social dilemmas, as they are able to balance personal benefit and group benefit. As agents in multi-agent systems are regularly confronted with social dilemmas, for instance in tasks such as resource allocation, these agents may benefit from the inclusion of mechanisms thought to facilitate human fairness. Although many of such mechanisms have already been implemented in a multi-agent systems context, their application is usually limited to rather abstract social dilemmas with a discrete set of available strategies (usually two). Given that many real-world examples of social dilemmas are actually continuous in nature, we extend this previous work to more general dilemmas, in which agents operate in a continuous strategy space. The social dilemma under study here is the well-known Ultimatum Game, in which an optimal solution is achieved if agents agree on a common strategy. We investigate whether a scale-free interaction network facilitates agents to reach agreement, especially in the presence of fixed-strategy agents that represent a desired (e.g. human) outcome. Moreover, we study the influence of rewiring in the interaction network. The agents are equipped with continuous-action learning automata and play a large number of random pairwise games in order to establish a common strategy. From our experiments, we may conclude that results obtained in discrete-strategy games can be generalized to continuous-strategy games to a certain extent: a scale-free interaction network structure allows agents to achieve agreement on a common strategy, and rewiring in the interaction network greatly enhances the agents' ability to reach agreement. However, it also becomes clear that some alternative mechanisms, such as reputation and volunteering, have many subtleties involved and do not have convincing beneficial effects in the continuous case.

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