25 research outputs found
Optimising Humanness: Designing the best human-like Bot for Unreal Tournament 2004
This paper presents multiple hybridizations of the two best
bots on the BotPrize 2014 competition, which sought for the best humanlike
bot playing the First Person Shooter game Unreal Tournament 2004.
To this aim the participants were evaluated using a Turing test in the
game. The work considers MirrorBot (the winner) and NizorBot (the
second) codes and combines them in two different approaches, aiming to
obtain a bot able to show the best behaviour overall. There is also an
evolutionary version on MirrorBot, which has been optimized by means
of a Genetic Algorithm. The new and the original bots have been tested
in a new, open, and public Turing test whose results show that the evolutionary
version of MirrorBot apparently improves the original bot, and
also that one of the novel approaches gets a good humanness level.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
General Video Game Playing
One of the grand challenges of AI is to create general intelligence: an agent that can excel at many tasks, not just one. In the area of games, this has given rise to the challenge of General Game Playing (GGP). In GGP, the game (typically a turn-taking board game) is defined declaratively in terms of the logic of the game (what happens when a move is made, how the scoring system works, how the winner is declared, and so on). The AI player then has to work out how to play the game and how to win. In this work, we seek to extend the idea of General Game Playing into the realm of video games, thus forming the area of General Video Game Playing (GVGP). In GVGP, computational agents will be asked to play video games that they have not seen before. At the minimum, the agent will be given the current state of the world and told what actions are applicable. Every game tick the agent will have to decide on its action, and the state will be updated, taking into account the actions of the other agents in the game and the game physics. We envisage running a competition based on GVGP playing, using arcadestyle (e.g. similar to Atari 2600) games as our starting point. These games are rich enough to be a formidable challenge to a GVGP agent, without introducing unnecessary complexity. The competition that we envisage could have a number of tracks, based on the form of the state (frame buffer or object model) and whether or not a forward model of action execution is available. We propose that the existing Physical Travelling Salesman (PTSP) software could be extended for our purposes and that a variety of GVGP games could be created in this framework by AI and Games students and other developers. Beyond this, we envisage the development of a Video Game Description Language (VGDL) as a way of concisely specifying video games. For the competition, we see this as being an interesting challenge in terms of deliberative search, machine learning and transfer of existing knowledge into new domains
Decision making styles as deviation from rational action : a super Mario case study
The authors would like to thank Juan Ortega and Noor
Shaker, as well as all players, for their contributions in creating
the dataset. We also thank Robin Baumgarten for making
his code publicly available under the WTFPL license.
Finally, we would like to thank our reviewers for feedback
and suggestions for future work.In this paper we describe a method of modeling play styles as
deviations from approximations of game theoretically rational
actions. These deviations are interpreted as containing information
about player skill and player decision making style.
We hypothesize that this information is useful for differentiating
between players and for understanding why human player
behavior is attributed intentionality which we argue is a prerequisite
for believability. To investigate these hypotheses we
describe an experiment comparing 400 games in the Mario
AI Benchmark testbed, played by humans, with equivalent
games played by an approximately game theoretically rationally
playing AI agent. The player actions’ deviations from
the rational agent’s actions are subjected to feature extraction,
and the resulting features are used to cluster play sessions
into expressions of different play styles. We discuss
how these styles differ, and how believable agent behavior
might be approached by using these styles as an outset for a
planning agent. Finally, we discuss the implications of making
assumptions about rational game play and the problematic
aspects of inferring player intentions from behavior.peer-reviewe
A panorama of artificial and computational intelligence in games
This paper attempts to give a high-level overview
of the field of artificial and computational intelligence (AI/CI)
in games, with particular reference to how the different core
research areas within this field inform and interact with each
other, both actually and potentially. We identify ten main
research areas within this field: NPC behavior learning, search
and planning, player modeling, games as AI benchmarks,
procedural content generation, computational narrative, believable
agents, AI-assisted game design, general game artificial
intelligence and AI in commercial games. We view and analyze
the areas from three key perspectives: (1) the dominant AI
method(s) used under each area; (2) the relation of each area
with respect to the end (human) user; and (3) the placement of
each area within a human-computer (player-game) interaction
perspective. In addition, for each of these areas we consider how
it could inform or interact with each of the other areas; in those
cases where we find that meaningful interaction either exists or
is possible, we describe the character of that interaction and
provide references to published studies, if any. We believe that
this paper improves understanding of the current nature of the
game AI/CI research field and the interdependences between
its core areas by providing a unifying overview. We also believe
that the discussion of potential interactions between research
areas provides a pointer to many interesting future research
projects and unexplored subfields.peer-reviewe
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
Affect and believability in game characters:a review of the use of affective computing in games
Virtual agents are important in many digital environments. Designing a character that highly engages users in terms of interaction is an intricate task constrained by many requirements. One aspect that has gained more attention recently is the effective dimension of the agent. Several studies have addressed the possibility of developing an affect-aware system for a better user experience. Particularly in games, including emotional and social features in NPCs adds depth to the characters, enriches interaction possibilities, and combined with the basic level of competence, creates a more appealing game. Design requirements for emotionally intelligent NPCs differ from general autonomous agents with the main goal being a stronger player-agent relationship as opposed to problem solving and goal assessment. Nevertheless, deploying an affective module into NPCs adds to the complexity of the architecture and constraints. In addition, using such composite NPC in games seems beyond current technology, despite some brave attempts. However, a MARPO-type modular architecture would seem a useful starting point for adding emotions
Evolving interesting maps for a first person shooter
We address the problem of automatically designing maps for first-person shooter (FPS) games. An efficient solution to this procedural content generation (PCG) problem could allow the design of FPS games of lower development cost with near-infinite replay value and capability to adapt to the skills and preferences of individual players. We propose a search-based solution, where maps are evolved to optimize a fitness function that is based on the players’ average fighting time. For that purpose, four different map representations are tested and compared. Results obtained showcase the clear advantage of some representations in generating interesting FPS maps and demonstrate the promise of the approach followed for automatic level design in that game genre.peer-reviewe
Desarrollo de un Bot Evolutivo Interactivo para Unreal Tournament 2004
En este trabajo se ha implementado un algoritmo genético interactivo en un bot para el juego Unreal Tournament 2004, utilizando como base un
bot que se definió anteriormente modelando el conocimiento de un jugador experto. El algoritmo ofrece dos tipos de interacción: por parte de un experto en el juego, o por parte de un experto en el algoritmo.
Cada uno influirá en distintos aspectos del algoritmo, para dirigirlo hacia unos mejores resultado con respecto a la humanidad que presente el bot (objetivo de este artículo). Se ha hecho un análisis de la influencia del experto en la ejecución y los resultados muestran cierta mejoría con la versión sin interactividad.
El mejor bot obtenido como resultado ha sido presentado a la BotPrize competition de 2014 (buscan el bot más humano posible), quedando en segundo lugar.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech