scholarly journals Study on the Strategy of Playing Doudizhu Game Based on Multirole Modeling

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Shuqin Li ◽  
Saisai Li ◽  
Hengyang Cao ◽  
Kun Meng ◽  
Meng Ding
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Making ◽  
Incomplete Information ◽  
Computer Game ◽  
Game Problem ◽  
Role Modeling ◽  
Human Beings ◽  
National Competition ◽  
Evaluation Algorithm

Doudizhu poker is a very popular and interesting national poker game in China, and now it has become a national competition in China. As this game is a typical example of incomplete information game problem, it has received more and more attention from artificial intelligence experts. This paper proposes a multirole modeling-based card-playing framework. This framework includes three parts: role modeling, cards carrying, and decision-making strategies. Role modeling learns different roles and behaviors by using a convolutional neural network. Cards carrying can calculate reasonable rules especially for “triplet” by using an evaluation algorithm. Decision making is for implementing different card strategies for different player roles. Experimental results showed that this card-playing framework makes playing decisions like human beings, and it can to some extent learn, collaborate, and reason when facing an incomplete information game problem. This framework won the runner-up in the 2018 China Computer Game Competition.

scholarly journals Penerapan Algoritma Minimax Pada Game Macan-macanan

2016 ◽  
Vol 6 (1) ◽  
pp. 21
Author(s):  
Kartika Imam Santoso ◽  
Farida Yunita ◽  
Nafi Projo Kusumo
Keyword(s):  
Artificial Intelligence ◽  
Computer Game ◽  
Terminal State ◽  
Technology Products ◽  
Object Based ◽  
Minimax Algorithms ◽  
Evaluation Algorithm ◽  
End Node ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

Lots of traditional games, but now the game is becoming obsolete. Many games were replaced with the modern game technology products. Modern games are becoming more practical because it did not require the terrain and many friends. Quite alone in front of the screen was a person may engage in an exciting game. One of the efforts to preserve and disseminate traditional games one of which is macan-macanan is to adapt the game into a computer game. This study aims to apply artificial intelligence using minimax algorithms and programming language ActionScript 3 in the game with a macan-macanan research methods are prototyping. The design used in this study is an artificial intelligence approach for representing the state, science, human computer interaction for designing the user experience, as well as the UML for object-based design. Results from this study is that in order to determine the value of the evaluation algorithm minimax for the end node / terminal state in the game macan-macanan, required the calculation of the total step is valid for each piece, as well as to pawn macan, necessary calculations springboard to a higher value and the weight difference the appropriate type of pawns.

Artificial Intelligence in Aviation Industries

2020 ◽  
pp. 1-35
Author(s):  
Tetiana Shmelova ◽  
Arnold Sterenharz ◽  
Serge Dolgikh
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Making ◽  
Unmanned Aircraft ◽  
Innovative Technology ◽  
Sociotechnical System ◽  
Air Traffic Controllers ◽  
High Degree ◽  
Difficult Situations

This chapter presents opportunities to use Artificial Intelligence (AI) in aviation and aerospace industries. The AI used an innovative technology for improving the effectiveness of building aviation systems in each stage of the lifecycle for enhancing the security of aviation systems and the characteristic ability to learn, improve, and predict difficult situations. The AI is presented in Air Navigation Sociotechnical system (ANSTS) because the activity of ANSTS, is accompanied by a high degree of risk of causing catastrophic outcomes. The operator's models of decision making in AI systems are presented such as Expert Systems, Decision Support Systems for pilots of manned and unmanned aircraft, air traffic controllers, engineers, etc. The quality of operator's decisions depends on the development and use of innovative technology of AI and related fields (Big Data, Data Mining, Multicriteria Decision Analysis, Collaboration Decision Making, Blockchain, Artificial Neural Network, etc.).

scholarly journals Whose morality? Which rationality? Challenging artificial intelligence as a remedy for the lack of moral enhancement

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Silviya Serafimova
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Intelligent Agents ◽  
Machine Ethics ◽  
Decision Making Process ◽  
Human Beings ◽  
First Order ◽  
Moral Agents ◽  
Act Utilitarianism ◽  
Implicit And Explicit

Abstract Moral implications of the decision-making process based on algorithms require special attention within the field of machine ethics. Specifically, research focuses on clarifying why even if one assumes the existence of well-working ethical intelligent agents in epistemic terms, it does not necessarily mean that they meet the requirements of autonomous moral agents, such as human beings. For the purposes of exemplifying some of the difficulties in arguing for implicit and explicit ethical agents in Moor’s sense, three first-order normative theories in the field of machine ethics are put to test. Those are Powers’ prospect for a Kantian machine, Anderson and Anderson’s reinterpretation of act utilitarianism and Howard and Muntean’s prospect for a moral machine based on a virtue ethical approach. By comparing and contrasting the three first-order normative theories, and by clarifying the gist of the differences between the processes of calculation and moral estimation, the possibility for building what—one might call strong “moral” AI scenarios—is questioned. The possibility of weak “moral” AI scenarios is likewise discussed critically.

Artificial Intelligence in International Arbitration

2020 ◽  
pp. 590-616
Author(s):  
Myriam Gicquello
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Rule Of Law ◽  
Human Beings ◽  
International Arbitration ◽  
Naturalistic Decision Making ◽  
The Rule Of Law ◽  
Behavioural Analysis ◽  
International Adjudication ◽  
Definition Of

This chapter assesses the introduction of artificial intelligence in international arbitration. The contention is that it would not only reinstate confidence in the arbitral system—from the perspective of the parties and the general public—and participate in the development of the rule of law, but also engage with broader systemic considerations in enhancing its legitimacy, fairness, and efficiency. Yet, before addressing the why, what, and how of this proposition, a definition of artificial intelligence is warranted. It should be noted at the outset that this concept has a variety of meanings. Despite the lack of consensus on its meaning, the chapter will thus treat artificial intelligence as encompassing both semi-autonomous and autonomous computer systems dedicated to assisting or replacing human beings in decision-making tasks. It presents the conclusions of two extensive research programs respectively dealing with the performance of statistical models and naturalistic decision-making. From that behavioural analysis, the introduction of artificial intelligence in international arbitration be discussed against the general considerations of international adjudication and the specific goals pertaining to international arbitration.

Neural Network Based Online Anthropomorphic Performance Decision-Making Approach for Dual-Arm Dulcimer Playing Robot

2019 ◽  
Vol 23 (5) ◽  
pp. 838-846
Author(s):  
Ting Fei ◽  
Xin Chen ◽  
Li Zhou ◽  
◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Training Data ◽  
Time Interval ◽  
Human Beings ◽  
Actual Performance ◽  
Artificial Neural Network Ann ◽  
Types Of Information ◽  
Different Characteristics ◽  
Dual Arm

A neural network based online anthropomorphic performance decision-making approach is described for a dual-arm dulcimer playing robot. Because it is difficult to extract experiential rules manually to describe the decision behavior of a human playing a dulcimer, the proposed method relies on the self-learning function of a artificial neural network (ANN). The training data of the network consists of three types of information: the note pitch of adjacent notes, time interval in a piece of music, and decision results in actual performance processes of human beings. A decision-making approach, devised through combining the well-trained ANN with music for which performance decisions were required, is then applied. The numerical results show that, for several pieces of music with different characteristics, the accuracy and precision of the decision results are always relatively high, which verifies the practicability and good generalizability of the method.

Actor–critic-based decision-making method for the artificial intelligence commander in tactical wargames

2020 ◽  
pp. 154851292095454
Author(s):  
Junfeng Zhang ◽  
Qing Xue
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Making ◽  
Reinforcement Learning ◽  
Convolutional Neural Network ◽  
Difficult Problem ◽  
Learning Method ◽  
Rule Based ◽  
Autonomous Decision ◽  
Decision Making Problem

In a tactical wargame, the decisions of the artificial intelligence (AI) commander are critical to the final combat result. Due to the existence of fog-of-war, AI commanders are faced with unknown and invisible information on the battlefield and lack of understanding of the situation, and it is difficult to make appropriate tactical strategies. The traditional knowledge rule-based decision-making method lacks flexibility and autonomy. How to make flexible and autonomous decision-making when facing complex battlefield situations is a difficult problem. This paper aims to solve the decision-making problem of the AI commander by using the deep reinforcement learning (DRL) method. We develop a tactical wargame as the research environment, which contains built-in script AI and supports the machine–machine combat mode. On this basis, an end-to-end actor–critic framework for commander decision making based on the convolutional neural network is designed to represent the battlefield situation and the reinforcement learning method is used to try different tactical strategies. Finally, we carry out a combat experiment between a DRL-based agent and a rule-based agent in a jungle terrain scenario. The result shows that the AI commander who adopts the actor–critic method successfully learns how to get a higher score in the tactical wargame, and the DRL-based agent has a higher winning ratio than the rule-based agent.

Study on the Influence IDs Model of the Structure and the Behavior Choice

2013 ◽  
Vol 380-384 ◽  
pp. 1354-1357
Author(s):  
Ya Ni Zhang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Making ◽  
Utility Function ◽  
Bp Neural Network ◽  
Experimental Result ◽  
Individual Utility ◽  
Decision Making Problem ◽  
Individual Utility Function ◽  
Behavior Choice

The complicated decision making problem is one of the important components for the study on the system of artificial intelligence area. This thesis, based on the Bayesian technology and decision-making theory, is going to optimize the traditional IDs model and improve the ability of expression of the model. and also by using the sum of individual utility function instead of the joint utility function to create the BP neural network to study the utility function structure of the IDs. The experimental result shows the method mentioned above is effective.

scholarly journals Using Artificial Intelligence in IC Substrate Production Predicting

2021 ◽  
Author(s):  
Zhifang Liu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Decision Making ◽  
Prediction Model ◽  
Nonlinear Models ◽  
Future Market ◽  
Prediction Ability ◽  
Grey Correlation ◽  
The Future ◽  
Grey Correlation Analysis

Abstract Today's technology products are changing with each day, the purpose is to bring more convenience to people, but also the competition among the technology industries is more competitive. In such environment, whether the company's decision-making is correct or not will directly affect the future development of an enterprise. Therefore, how an enterprise can formulate and construct a set of appropriate decision-making systems to accurately predict the future market will be the first important issue for enterprises. This research proposed an artificial intelligence predicting system to estimate manufacturing capacities and client demands, and providing it to manufacturing managers as a reference for inventory arrangements so that inventory can be adjusted appropriately to avoid excessive inventory levels. In recent years, neural networks have been widely and effectively applied to many predicting problems. The main reason is that most of the predicting problems are nonlinear models. And the backward neural network has the ability to construct nonlinear models. In this study, a predicting model combining grey correlation and neural network will be used to establish a high-accuracy predition system for the production predict of IC product. First, grey correlation analysis will be used to screen out the most relevant factors among many factors. And then put these factors into the neural network prediction model for training and prediction. The results show that the training prediction error and the empirical error value are about 14%. This value indicates that the prediction ability is better, so the proposed prediction model can be applied to the prediction of IC substrate production. It provided a predictive reference material and provide decision making with a more accurate, convenient and a fast tool to enhance the company’s competitiveness.

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