Game “Divi-Divi-Dip” Based on Interactive Robot

Recently the improved robot working in very various fields, many researchers are trying to develop the robot to help people in the service field. However people who meet a robot in the first time gen- erally get heterogeneous feeling. To resolve this, we suggest an inter- active game with robots to increase the friendliness. The game, called ＂Divi-Divi-Dip＂ with 3 different body poses, is first introduced in a Korean TV prgrmn. The robot is using the visionary technology to recognize the pose with camera for the interactive game.

Research on the Effects of the Most Used Market Power Mitigation Mechanisms Considering Different Market Environments

The market power mitigation method of the supply-side has become one of the key points affecting the stability of the electricity spot market.Different mitigation mechanisms are used in the current mature electricity markets of the world.However,the same market power mitigation mechanism shows different effects in different market environments.Every market operator in the world needs the most efficient way to mitigate market power.Considering that there is no relevant literature discussing the market power effects of different mitigation methods in detail,the mitigation effects need to be discussed and further researched.So,we analyze the effects of the most utilized market power mitigation mechanisms while considering different market environments.Firstly,we establish a Nash-Stackelberg interactive game model to simulate the competitive strategies of power suppliers.Secondly,the different market power mitigation approaches are modeled.Then,a multi-agent system(MAS)genetic interior-point algorithm is proposed to solve the problem of suppliers.Finally,through the simulation analysis,the market power mitigation effects of different mechanisms while considering three operation states of the system in two market structures are all analyzed.

Research on Teaching Method of Children’s Image Cognition Based on AR Technology

In this paper,the research on the teaching method of children’s image cognition based on AR technology is carried out.By analyzing the principle ofARtechnology to recognize images,we understand thatARtechnology can promote children’s image cognition,and this teaching method is in line with the Tower of Experience theory.It further analyzes the current situation of children’s image cognition teaching with simple teaching methods,backward AR teaching tools,and poor perception of teaching objects.Teachers’traditional image teaching methods cannot effectively and efficiently improve children’s image cognition.Therefore,based on AR technology,two commonly used image cognition teaching methods are proposed:AR interactive picture book education andARinteractive game education.Both of these educational methods can improve children’s ability to recognize images.

Game Interactive Learning:A New Paradigm towards Intelligent Decision-Making

Decision-making plays an essential role in various real-world systems like automatic driving,traffic dispatching,information system management,and emergency command and control.Recent breakthroughs in computer game scenarios using deep reinforcement learning for intelligent decision-making have paved decision-making intelligence as a burgeoning research direction.In complex practical systems,however,factors like coupled distracting features,long-term interact links,and adversarial environments and opponents,make decision-making in practical applications challenging in modeling,computing,and explaining.This work proposes game interactive learning,a novel paradigm as a new approach towards intelligent decision-making in complex and adversarial environments.This novel paradigm highlights the function and role of a human in the process of intelligent decision-making in complex systems.It formalizes a new learning paradigm for exchanging information and knowledge between humans and the machine system.The proposed paradigm first inherits methods in game theory to model the agents and their preferences in the complex decision-making process.It then optimizes the learning objectives from equilibrium analysis using reformed machine learning algorithms to compute and pursue promising decision results for practice.Human interactions are involved when the learning process needs guidance from additional knowledge and instructions,or the human wants to understand the learning machine better.We perform preliminary experimental verification of the proposed paradigm on two challenging decision-making tasks in tactical-level War-game scenarios.Experimental results demonstrate the effectiveness of the proposed learning paradigm.