scholarly journals Enhancing Artificial Intelligence on a Real Mobile Game

2009 ◽  
Vol 2009 ◽  
pp. 1-9 ◽  
Author(s):  
Fabio Aiolli ◽  
Claudio E. Palazzi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mobile Phones ◽  
Imperfect Information ◽  
Learning Algorithm ◽  
Predictive Performance ◽  
Human Opponent ◽  
Imperfect Information Games ◽  
Game State ◽  
Information Games

Mobile games represent a killer application that is attracting millions of subscribers worldwide. One of the aspects crucial to the commercial success of a game is ensuring an appropriately challenging artificial intelligence (AI) algorithm against which to play. However, creating this component is particularly complex as classic search AI algorithms cannot be employed by limited devices such as mobile phones or, even on more powerful computers, when considering imperfect information games (i.e., games in which participants do not a complete knowledge of the game state at any moment). In this paper, we propose to solve this issue by resorting to a machine learning algorithm which uses profiling functionalities in order to infer the missing information, thus making the AI able to efficiently adapt its strategies to the human opponent. We studied a simple and computationally light machine learning method that can be employed with success, enabling AI improvements for imperfect information games even on mobile phones. We created a mobile phone-based version of a game calledGhostsand present results which clearly show the ability of our algorithm to quickly improve its own predictive performance as far as the number of games against the same human opponent increases.

A Decision Making Method Based on Society of Mind Theory in Multi-Player Imperfect Information Games

2020 ◽  
pp. 317-329
Author(s):  
Mitsuo Wakatsuki ◽  
Mari Fujimura ◽  
Tetsuro Nishino
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Imperfect Information ◽  
Card Game ◽  
Mind Theory ◽  
Imperfect Information Games ◽  
Game Players ◽  
The University ◽  
Society Of Mind ◽  
Information Games

The authors are concerned with a card game called Daihinmin (Extreme Needy), which is a multi-player imperfect information game. Using Marvin Minsky's “Society of Mind” theory, they attempt to model the workings of the minds of game players. The UEC Computer Daihinmin Competitions (UECda) have been held at the University of Electro-Communications since 2006, to bring together competitive client programs that correspond to players of Daihinmin, and contest their strengths. In this paper, the authors extract the behavior of client programs from actual competition records of the computer Daihinmin, and propose a method of building a system that determines the parameters of Daihinmin agencies by machine learning.

A Decision Making Method Based on Society of Mind Theory in Multi-Player Imperfect Information Games

2016 ◽  
Vol 4 (2) ◽  
pp. 58-70 ◽  
Author(s):  
Mitsuo Wakatsuki ◽  
Mari Fujimura ◽  
Tetsuro Nishino
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Imperfect Information ◽  
Card Game ◽  
Mind Theory ◽  
Imperfect Information Games ◽  
Game Players ◽  
The University ◽  
Society Of Mind ◽  
Information Games

The authors are concerned with a card game called Daihinmin (Extreme Needy), which is a multi-player imperfect information game. Using Marvin Minsky's “Society of Mind” theory, they attempt to model the workings of the minds of game players. The UEC Computer Daihinmin Competitions (UECda) have been held at the University of Electro-Communications since 2006, to bring together competitive client programs that correspond to players of Daihinmin, and contest their strengths. In this paper, the authors extract the behavior of client programs from actual competition records of the computer Daihinmin, and propose a method of building a system that determines the parameters of Daihinmin agencies by machine learning.

scholarly journals Research on machine learning Algorithm optimization based on 0-1 coding

2021 ◽  
Vol 2083 (4) ◽  
pp. 042086
Author(s):  
Yuqi Qin
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Manufacturing Industry ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Algorithm Optimization ◽  
The Core ◽  
Coding Method ◽  
Discrete Manufacturing

Abstract Machine learning algorithm is the core of artificial intelligence, is the fundamental way to make computer intelligent, its application in all fields of artificial intelligence. Aiming at the problems of the existing algorithms in the discrete manufacturing industry, this paper proposes a new 0-1 coding method to optimize the learning algorithm, and finally proposes a learning algorithm of “IG type learning only from the best”.

Introduction to Machine Learning and Its Application

2020 ◽  
pp. 262-290
Author(s):  
Ladly Patel ◽  
Kumar Abhishek Gaurav
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Human Interaction ◽  
Machine Learning Algorithm ◽  
Past Data ◽  
Applications Of Machine Learning ◽  
Learning Frameworks ◽  
Learning Data

In today's world, a huge amount of data is available. So, all the available data are analyzed to get information, and later this data is used to train the machine learning algorithm. Machine learning is a subpart of artificial intelligence where machines are given training with data and the machine predicts the results. Machine learning is being used in healthcare, image processing, marketing, etc. The aim of machine learning is to reduce the work of the programmer by doing complex coding and decreasing human interaction with systems. The machine learns itself from past data and then predict the desired output. This chapter describes machine learning in brief with different machine learning algorithms with examples and about machine learning frameworks such as tensor flow and Keras. The limitations of machine learning and various applications of machine learning are discussed. This chapter also describes how to identify features in machine learning data.

scholarly journals Limited lookahead in imperfect-information games

2020 ◽  
Vol 283 ◽  
pp. 103218 ◽  
Author(s):  
Christian Kroer ◽  
Tuomas Sandholm
Keyword(s):  
Imperfect Information ◽  
Imperfect Information Games ◽  
Information Games

scholarly journals Implementation of modified SARSA learning technique in EMCAP

2017 ◽  
Vol 7 (1.5) ◽  
pp. 274
Author(s):  
D. Ganesha ◽  
Vijayakumar Maragal Venkatamuni
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Process ◽  
Learning Algorithm ◽  
Research Work ◽  
Learning System ◽  
State Action ◽  
Learning Technique ◽  
Markov Decision ◽  
Experiment Analysis

This research work presents analysis of Modified Sarsa learning algorithm. Modified Sarsa algorithm.  State-Action-Reward-State-Action (SARSA) is an technique for learning a Markov decision process (MDP) strategy, used in for reinforcement learning int the field of artificial intelligence (AI) and machine learning (ML). The Modified SARSA Algorithm makes better actions to get better rewards.  Experiment are conducted to evaluate the performace for each agent individually. For result comparison among different agent, the same statistics were collected. This work considered varied kind of agents in different level of architecture for experiment analysis. The Fungus world testbed has been considered for experiment which is has been implemented using SwI-Prolog 5.4.6. The fixed obstructs tend to be more versatile, to make a location that is specific to Fungus world testbed environment. The various parameters are introduced in an environment to test a agent’s performance. This modified   SARSA learning algorithm can   be more suitable in EMCAP architecture.  The experiments are conducted the modified   SARSA Learning system gets   more rewards compare to existing  SARSA algorithm.

scholarly journals Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone–based survey when cities and towns are under quarantine

2020 ◽  
Vol 41 (7) ◽  
pp. 826-830 ◽  
Author(s):  
Arni S. R. Srinivasa Rao ◽  
Jose A. Vazquez
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mobile Phone ◽  
Learning Algorithm ◽  
Web Survey ◽  
Machine Learning Algorithm ◽  
Case Identification ◽  
Susceptible Populations

AbstractWe propose the use of a machine learning algorithm to improve possible COVID-19 case identification more quickly using a mobile phone–based web survey. This method could reduce the spread of the virus in susceptible populations under quarantine.

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