2,472 research outputs found
Social Bots: Human-Like by Means of Human Control?
Social bots are currently regarded an influential but also somewhat
mysterious factor in public discourse and opinion making. They are considered
to be capable of massively distributing propaganda in social and online media
and their application is even suspected to be partly responsible for recent
election results. Astonishingly, the term `Social Bot' is not well defined and
different scientific disciplines use divergent definitions. This work starts
with a balanced definition attempt, before providing an overview of how social
bots actually work (taking the example of Twitter) and what their current
technical limitations are. Despite recent research progress in Deep Learning
and Big Data, there are many activities bots cannot handle well. We then
discuss how bot capabilities can be extended and controlled by integrating
humans into the process and reason that this is currently the most promising
way to go in order to realize effective interactions with other humans.Comment: 36 pages, 13 figure
Towards the 3D Web with Open Simulator
Continuing advances and reduced costs in computational power, graphics processors and network bandwidth have led to 3D immersive multi-user virtual worlds becoming increasingly accessible while offering an improved and engaging Quality of Experience. At the same time the functionality of the World Wide Web continues to expand alongside the computing infrastructure it runs on and pages can now routinely accommodate many forms of interactive multimedia components as standard features - streaming video for example. Inevitably there is an emerging expectation that the Web will expand further to incorporate immersive 3D environments. This is exciting because humans are well adapted to operating in 3D environments and it is challenging because existing software and skill sets are focused around competencies in 2D Web applications. Open Simulator (OpenSim) is a freely available open source tool-kit that empowers users to create and deploy their own 3D environments in the same way that anyone can create and deploy a Web site. Its characteristics can be seen as a set of references as to how the 3D Web could be instantiated. This paper describes experiments carried out with OpenSim to better understand network and system issues, and presents experience in using OpenSim to develop and deliver applications for education and cultural heritage. Evaluation is based upon observations of these applications in use and measurements of systems both in the lab and in the wild.Postprin
Evolving artificial neural networks applied to generate virtual characters
Computer game industry is one of the most prof itable nowadays. Although this industry has evolved fast in the last years in different fields, Artificial Intelligence (AI) seems to be stuck. Many games still make use of simple state machines to simulate AI. New models can be designed and proposed to replace this jurassic technique. In this paper we propose the use of Artificial Neural Networks (ANN) as a new model. ANN will be then in charge of receiving information from the game (sensors) and carry out actions (actuators) according to the information received. The search for the best ANN is a complex task that strongly affects the task performance while often requiring a high computational time. In this work, we present ADANN, a system for the automatic evolution and adaptation of artificial neural networks based on evolutionary ANN (EANN). This approach use Genetic Algorithm (GA) that evolves fully connected Artificial Neural Network. The testing game is called Unreal Tournament 2004. The new agent obtained has been put to the test jointly with CCBot3, the winner of BotPrize 2010 competition [1], and have showed a significant improvement in the humannesss ratio. Additionally, we have confronted our approach and CCBot3 (winner of BotPrize competition in 2010) to First-person believability assessment (BotPrize original judging protocol), demonstrating that the active involvement of the judge has a great impact in the recognition of human-like behaviour
The Hanabi Challenge: A New Frontier for AI Research
From the early days of computing, games have been important testbeds for
studying how well machines can do sophisticated decision making. In recent
years, machine learning has made dramatic advances with artificial agents
reaching superhuman performance in challenge domains like Go, Atari, and some
variants of poker. As with their predecessors of chess, checkers, and
backgammon, these game domains have driven research by providing sophisticated
yet well-defined challenges for artificial intelligence practitioners. We
continue this tradition by proposing the game of Hanabi as a new challenge
domain with novel problems that arise from its combination of purely
cooperative gameplay with two to five players and imperfect information. In
particular, we argue that Hanabi elevates reasoning about the beliefs and
intentions of other agents to the foreground. We believe developing novel
techniques for such theory of mind reasoning will not only be crucial for
success in Hanabi, but also in broader collaborative efforts, especially those
with human partners. To facilitate future research, we introduce the
open-source Hanabi Learning Environment, propose an experimental framework for
the research community to evaluate algorithmic advances, and assess the
performance of current state-of-the-art techniques.Comment: 32 pages, 5 figures, In Press (Artificial Intelligence
ViZDoom Competitions: Playing Doom from Pixels
This paper presents the first two editions of Visual Doom AI Competition,
held in 2016 and 2017. The challenge was to create bots that compete in a
multi-player deathmatch in a first-person shooter (FPS) game, Doom. The bots
had to make their decisions based solely on visual information, i.e., a raw
screen buffer. To play well, the bots needed to understand their surroundings,
navigate, explore, and handle the opponents at the same time. These aspects,
together with the competitive multi-agent aspect of the game, make the
competition a unique platform for evaluating the state of the art reinforcement
learning algorithms. The paper discusses the rules, solutions, results, and
statistics that give insight into the agents' behaviors. Best-performing agents
are described in more detail. The results of the competition lead to the
conclusion that, although reinforcement learning can produce capable Doom bots,
they still are not yet able to successfully compete against humans in this
game. The paper also revisits the ViZDoom environment, which is a flexible,
easy to use, and efficient 3D platform for research for vision-based
reinforcement learning, based on a well-recognized first-person perspective
game Doom
Towards conscious-like behavior in computer game characters
Proceeding of: IEEE Symposium on Computational Intelligence and Games 2009 (CIG-2009). Milano, Italy, 7-10 Septiembre, 2009.The main sources of inspiration for the design of more engaging synthetic characters are existing psychological models of human cognition. Usually, these models, and the associated Artificial Intelligence (AI) techniques, are based on partial aspects of the real complex systems involved in the generation of human-like behavior. Emotions, planning, learning, user modeling, set shifting, and attention mechanisms are some remarkable examples of features typically considered in isolation within classical AI control models. Artificial cognitive architectures aim at integrating many of these aspects together into effective control systems. However, the design of this sort of architectures is not straightforward. In this paper, we argue that current research efforts in the young field of Machine Consciousness (MC) could contribute to tackle complexity and provide a useful framework for the design of more appealing synthetic characters. This hypothesis is illustrated with the application of a novel consciousness-based cognitive architecture to the development of a First Person Shooter video game character.This work was supported by the Spanish Ministry of Education under CICYT grant TRA2007-67374-C02-02.Publicad
A Bot Approach-Based Capacity Testing Automation for Online Video Games
Online games are a type of computer game that can be accessed using the internet network and played with other players to play the same game. With the advance in online game development, game developers are required to develop games that can be played by many players in one game, especially in the capacity of an online game server. Those needs can be achieved by utilizing bots. However, previous works only conducted bot-based testing for testing network capabilities. In this research, those works will be extended further ftotesting the capacity of a game server. The result of this research suggests that bot approach testing can simulate real players adequately. Other than that, the bot approach also can be scalable. However, the result also suggests that the bot approach still has some limitations as bots cannot simulate the dynamics shown by real players. Special attention is also needed towards clients utilized for executing the bots for them to be scalable
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