Coevolutionary optimization of fuzzy logic intelligence for strategic decision support

©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.We present a description and initial results of a computer code that coevolves fuzzy logic rules to play a two-sided zero-sum competitive game. It is based on the TEMPO Military Planning Game that has been used to teach resource allocation to over 20 000 students over the past 40 years. No feasible algorithm for optimal play is known. The coevolved rules, when pitted against human players, usually win the first few competitions. For reasons not yet understood, the evolved rules (found in a symmetrical competition) place little value on information concerning the play of the opponent.Rodney W. Johnson, Michael E. Melich, Zbigniew Michalewicz, and Martin Schmid

Virtual player design using self-learning via competitive coevolutionary algorithms

The Google Artificial Intelligence (AI) Challenge is an international contest the objective of which is to program the AI in a two-player real time strategy (RTS) game. This AI is an autonomous computer program that governs the actions that one of the two players executes during the game according to the state of play. The entries are evaluated via a competition mechanism consisting of two-player rounds where each entry is tested against others. This paper describes the use of competitive coevolutionary (CC) algorithms for the automatic generation of winning game strategies in Planet Wars, the RTS game associated with the 2010 contest. Three different versions of a prime algorithm have been tested. Their common nexus is not only the use of a Hall-of-Fame (HoF) to keep note of the winners of past coevolutions but also the employment of an archive of experienced players, termed the hall-of-celebrities (HoC), that puts pressure on the optimization process and guides the search to increase the strength of the solutions; their differences come from the periodical updating of the HoF on the basis of quality and diversity metrics. The goal is to optimize the AI by means of a self-learning process guided by coevolutionary search and competitive evaluation. An empirical study on the performance of a number of variants of the proposed algorithms is described and a statistical analysis of the results is conducted. In addition to the attainment of competitive bots we also conclude that the incorporation of the HoC inside the primary algorithm helps to reduce the effects of cycling caused by the use of HoF in CC algorithms.This work is partially supported by Spanish MICINN under Project ANYSELF (TIN2011-28627-C04-01),3 by Junta de Andalucía under Project P10-TIC-6083 (DNEMESIS) and by Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech

Procedural Content Generation for Real-Time Strategy Games

Videogames are one of the most important and profitable sectors in the industry of entertainment. Nowadays, the creation of a videogame is often a large-scale endeavor and bears many similarities with, e.g., movie production. On the central tasks in the development of a videogame is content generation, namely the definition of maps, terrains, non-player characters (NPCs) and other graphical, musical and AI-related components of the game. Such generation is costly due to its complexity, the great amount of work required and the need of specialized manpower. Hence the relevance of optimizing the process and alleviating costs. In this sense, procedural content generation (PCG) comes in handy as a means of reducing costs by using algorithmic techniques to automatically generate some game contents. PCG also provides advantages in terms of player experience since the contents generated are typically not fixed but can vary in different playing sessions, and can even adapt to the player herself. For this purpose, the underlying algorithmic technique used for PCG must be also flexible and adaptable. This is the case of computational intelligence in general and evolutionary algorithms in particular. In this work we shall provide an overview of the use of evolutionary intelligence for PCG, with special emphasis on its use within the context of real-time strategy games. We shall show how these techniques can address both playability and aesthetics, as well as improving the game AI

Red Queen Coevolution on Fitness Landscapes

Species do not merely evolve, they also coevolve with other organisms. Coevolution is a major force driving interacting species to continuously evolve ex- ploring their fitness landscapes. Coevolution involves the coupling of species fit- ness landscapes, linking species genetic changes with their inter-specific ecological interactions. Here we first introduce the Red Queen hypothesis of evolution com- menting on some theoretical aspects and empirical evidences. As an introduction to the fitness landscape concept, we review key issues on evolution on simple and rugged fitness landscapes. Then we present key modeling examples of coevolution on different fitness landscapes at different scales, from RNA viruses to complex ecosystems and macroevolution.Comment: 40 pages, 12 figures. To appear in "Recent Advances in the Theory and Application of Fitness Landscapes" (H. Richter and A. Engelbrecht, eds.). Springer Series in Emergence, Complexity, and Computation, 201

Social Preferences in Small‐Scale Societies

This volume reports on a cross‐cultural investigation of social preferences in 15 small‐scale, non‐Western societies. Participants from all 15 groups played the ultimatum game with members of their own culture; subjects from a subset also played dictator and voluntary contribution to public goods games. The bulk of the book (Chapters 4 through 14) consists of reports by the field workers (mostly anthropologists). Each chapter includes ethnographic information, a description of how members of the group make their living, details on the experimental protocols and results, and some discussion. Although none of the results are consistent with the predictions of the standard rational choice model (as has been true in earlier work), group average offers and rejection frequencies in these experiments display more variation than has been observed in experiments using university students from developed Westernized societies. The editors report that little of this variation can be accounted for by individual economic or demographic variables, such as gender, age, education, or wealth. On the other hand, group dummies account for quite a lot, and social preferences seem to be stronger in groups experiencing greater market integration or whose economic mode offers greater opportunities for gains from cooperative enterprise

A Coevolutionary Model for Actions and Opinions in Social Networks

© 2020 IEEE. In complex social networks, the decision-making mechanisms behind human actions and the cognitive processes that shape opinion formation processes are often intertwined, leading to complex and varied collective emergent behavior. In this paper, we propose a mathematical model that captures such a coevolution of actions and opinions. Following a discrete-time process, each individual decides between binary actions, aiming to coordinate with the actions of other members observed on a network of interactions and taking into account their own opinion. At the same time, the opinion of each individual evolves due to the opinions shared by other members, the actions observed on the network, and, possibly, an external influence source. We provide a global convergence result for a special case of the coupled dynamics. Steady state configurations in which all the individuals take the same action are then studied, elucidating the role of the model parameters and the network structure on the collective behavior of the system

Notes from the Greenhouse World: A Study in Coevolution, Planetary Sustainability, and Community Structure

This paper explores coevolution and governance of common goods using models of coevolving biospheres, in which adapting populations must collectively regulate their planet's climate or face extinction. The results support the Gaia hypothesis against challenges based on the tragedy of the commons: model creatures are often able to work together to maintain the common good (a suitable climate) without being undermined by "free riders." A long-term dynamics appears in which communities that cannot sustain Gaian cooperation give way to communities that can. This result provides an argument why a Gaia scenario should generally be observed, rather than a tragedy of the commons scenario. Second, a close look at how communities fail reveals failures that do not fit the tragedy of the commons framework and are better described in terms of conflict between differently positioned parties, with power over different aspects of the system. In the context of Norgaard's work, all these observations can be read as narratives of coevolution relevant to social communities as well as ecological ones, contrasting with pessimistic scenarios about common governance and supporting respect for traditional arrangements and restraint in intervention.Comment: To appear in a special issue of Ecological Economics in honor of Richard B. Norgaar

Optimización en videojuegos: retos para la comunidad cientí­fica

Este artículo analiza algunos de los desafíos más interesantes a los cuales los miembros de la comunidad MAEB pueden enfrentarse en el área de la aplicación de técnicas de Inteligencia Artificial/Computacional al diseño y creación de videojuegos. El artículo se centra en tres líneas, que en un futuro cercano, seguramente van a influenciar de forma significativa la industria del desarrollo de videojuegos, en concreto se enfoca en la Generación Automática de Contenido, en la Computación Afectiva aplicada a los videojuegos y en la Generación de Comportamientos que gestionen la toma de decisiones de las entidades no controladas por el jugador humano.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Este trabajo está parcialmente financiado por la Junta de Andalucía dentro del proyecto P10-TIC-6083 (DNEMESIS), por el MICINN dentro del proyecto TIN2011-28627-C04-01 (ANYSELF

Game Artificial Intelligence: Challenges for the Scientific Community

This paper discusses some of the most interesting challenges to which the games research community members may face in the área of the application of arti cial or computational intelligence techniques to the design and creation of video games. The paper focuses on three lines that certainly will in uence signi cantly the industry of game development in the near future, speci cally on the automatic generation of content, the a ective computing applied to video games and the generation of behaviors that manage the decisions of entities not controlled by the human player.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech