Modeling Complex Networks Based on Deep Reinforcement Learning

The network topology of complex networks evolves dynamically with time. How to model the internal mechanism driving the dynamic change of network structure is the key problem in the field of complex networks. The models represented by WS, NW, BA usually assume that the evolution of network structure is driven by nodes’ passive behaviors based on some restrictive rules. However, in fact, network nodes are intelligent individuals, which actively update their relations based on experience and environment. To overcome this limitation, we attempt to construct a network model based on deep reinforcement learning, named as NMDRL. In the new model, each node in complex networks is regarded as an intelligent agent, which reacts with the agents around it for refreshing its relationships at every moment. Extensive experiments show that our model not only can generate networks owing the properties of scale-free and small-world, but also reveal how community structures emerge and evolve. The proposed NMDRL model is helpful to study propagation, game, and cooperation behaviors in networks.

An Intelligent Mission Planning Model for the Air Strike Operations against Islands Based on Neural Network and Simulation

Mission planning of air strike operations is hard because it has to give instructions to a large number of units during a relatively long period of time in an uncertain environment. If some instruction parameters can be calculated by an intelligent agent, better strategies can be found more quickly. In a specific combat scenario of air strike operations against islands, an intelligent model is proposed to improve the performance and flexibility of mission planning. The proposed intelligent mission planning model is based on rule-based decision and uses a fully connected recurrent neural network to calculate some of the decision parameters. The proposed intelligent mission planning model shows better results as compared to rule-based decision making with randomized parameters, and it performs as good as experts in the test set of the specific combat scenario.

Appreciative Learning for Immersive Reward System in Education Game Development

Game replayability is very important in serious game to maximize the understanding for the learning content. The replayability is the result from the gameplay experience. Games have the advantage of providing a fun experience, and immersion is a vital element in game design to produce the experience. However, the design of immersion in games is often not well conceptualized so that it does not produce the expected experience. This study uses Appreciative Learning based reward system, which focuses on positive things such as peak achievements, opportunities, exploration of potential and optimism for the future. The reward activity consists of four stages, namely Discovery, Dream, Design and Destiny. Reward personalization is done by regulating reward behavior using artificial intelligence which runs in all four stages. Appreciative Learning will be used to design immersive experiences consisting of sensory, imaginary and challenge-based immersion, which are the three main elements of immersive games. Intelligent agent behavior is modeled using the Finite State Machine. This study produces an immersive reward design that is applied to the concept of Appreciative Learning in designing a serious game.

Behavior model of an intelligent agent based on a genetic algorithm

A solution of improving the behavior model of a non-player character as an intelligent agent by optimizing input parameters based on a genetic algorithm is presented. The proposed approach includes the development of a non-player character model: a skeleton, rigid bodies, the implementation of a dynamic model based on the Featherstone algorithm, and modeling of the character's behavior based on a genetic algorithm. The formation of a behavior model using a genetic algorithm that simulates the physical properties of a character, taking into account his actions, is proposed. The stages of the genetic algorithm include creating an initial population, fitness score, selection, crossing and mutation. Based on the results of the experiments, the input parameters of the non-player character behavior model were determined, maximizing the cumulative fitness score, which acts as an estimate of the reward, which can be used as initial values for further experiments. Keywords: non-player character, intelligent agent, simulation, genetic algorithm

Analysis of Artificial Intellectual Agents for E-Learning Systems

The key terms and basic concepts of the agent are analyzed. The structured general classification of agents according to the representation of the model of the external environment, by the type of processing information and by the functions performed is given. The classification of artificial agents (intellectual, reflex, impulsive, trophic) also is s analyzed. The necessary conditions for the implementation of a certain behavior by the agent are given, as well as the scheme of functioning of the intelligent agent. The levels of knowledge that play a key role in the architecture of the agent are indicated. The functional diagram of a learning agent that works relatively independently, demonstrating flexible behavior. It is discussed that the functional scheme of the reactive agent determines the dependence on the environment. The properties of the intelligent agent are described in detail and the block diagram is indicated. Various variants of agent architectures, in particular neural network agent architectures, are considered. The organization of level interaction in the multilevel agent architecture is proposed. Considerable attention is paid to the Will-architecture and InteRRaP- architecture of agents. A multilevel architecture for an autonomous agent of a Turing machine is considered.

Predicate-Based Model of Problem-Solving for Robotic Actions Planning

The aim of the article is to describe a predicate-based logical model for the problem-solving of robots. The proposed article deals with analyses of trends of problem-solving robotic applications for manufacturing, especially for transportations and manipulations. Intelligent agent-based manufacturing systems with robotic agents are observed. The intelligent cores of them are considered from point of view of ability to propose the plans of problem-solving in the form of strategies. The logical model of adaptive strategies planning for the intelligent robotic system is composed in the form of predicates with a presentation of data processing on a base of set theory. The dynamic structures of workspaces, and a possible change of goals are considered as reasons for functional strategies adaptation.

Analysing the Adoption of Intelligent Agent Technology in Food Supply Chain Management: An Empirical Evidence

This article aims to identify and analyse the factors that impact the adoption of intelligent agent technology (IAT) in the food supply chain (FSC). The research was conducted based on 329 respondents from various hotels and the theoretical framework adopted in this study, that is, technological, organizational and environmental (TOE) framework. The findings indicated that multiple factors in TOE contribute significantly to the adoption of IAT. We have validated the proposed framework by structural equation modelling utilizing AMOS 22.0. This research offers a new and vital paradigm for adopting this innovation in the FSC, thereby increasing the overall efficiency of a hotel. The proposed TOE framework has identified several factors like relative advantage, reliability, complexity, cost, innovation adoption, top management support, skilled employees, IT awareness, environmental uncertainty, competitive pressure, information intensity and supplier’s pressure, which helps in the adoption process of IAT in the FSC. It also provides a foundation for future research and significant insights to adopt this new technology in the hotel industry.