text: We propose a framework called inverse augmented reality (IAR) which describes the scenario that a virtual agent living in the virtual world can observe both virtual objects and real objects. This is different from the traditional augmented reality. The traditional virtual reality, mixed reality and augmented reality are all generated for humans, i.e., they are human-centered frameworks. On the contrary, the proposed inverse augmented reality is a virtual agent-centered framework, which represents and analyzes the reality from a virtual agent's perspective. In this paper, we elaborate the framework of inverse augmented reality to argue the equivalence of the virtual world and the physical world regarding the whole physical structure.
  @cite_9: 
  @cite_6: Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
  @cite_7: This chapter discusses the possibility of instilling a virtual world with mechanisms for evolution and natural selection in order to generate rich ecosystems of complex organisms in a process akin to biological evolution. Some previous work in the area is described, and successes and failures are discussed. The components of a more comprehensive framework for designing such worlds are mapped out, including the design of the individual organisms, the properties and dynamics of the environmental medium in which they are evolving, and the representational relationship between organism and environment. Some of the key issues discussed include how to allow organisms to evolve new structures and functions with few restrictions, and how to create an interconnectedness between organisms in order to generate drives for continuing evolutionary activity.
  @cite_2: From the Publisher: In this revised and significantly expanded second edition, distinguished scientist David B. Fogel presents the latest advances in both the theory and practice of evolutionary computation to help you keep pace with developments in this fast-changing field.. "In-depth and updated, Evolutionary Computation shows you how to use simulated evolution to achieve machine intelligence. You will gain current insights into the history of evolutionary computation and the newest theories shaping research. Fogel carefully reviews the "no free lunch theorem" and discusses new theoretical findings that challenge some of the mathematical foundations of simulated evolution. This second edition also presents the latest game-playing techniques that combine evolutionary algorithms with neural networks, including their success in playing competitive checkers. Chapter by chapter, this comprehensive book highlights the relationship between learning and intelligence.. "Evolutionary Computation features an unparalleled integration of history with state-of-the-art theory and practice for engineers, professors, and graduate students of evolutionary computation and computer science who need to keep up-to-date in this developing field.
summary: To make the virtual world intelligent, Taylor al @cite_7 discussed the possibility of making a virtual world evolve by itself. The evolution of the virtual world took advantage of the principle of biological evolution in the physical world. Though the self-learning is not simple, there are still many learning frameworks that can be used to obtain the self-learning ability, such as evolutionary computation @cite_2 , reinforcement learning @cite_6 and deep learning @cite_9 .