2,946 research outputs found
The Challenge of Believability in Video Games: Definitions, Agents Models and Imitation Learning
In this paper, we address the problem of creating believable agents (virtual
characters) in video games. We consider only one meaning of believability,
``giving the feeling of being controlled by a player'', and outline the problem
of its evaluation. We present several models for agents in games which can
produce believable behaviours, both from industry and research. For high level
of believability, learning and especially imitation learning seems to be the
way to go. We make a quick overview of different approaches to make video
games' agents learn from players. To conclude we propose a two-step method to
develop new models for believable agents. First we must find the criteria for
believability for our application and define an evaluation method. Then the
model and the learning algorithm can be designed
Evolutionary games on graphs
Game theory is one of the key paradigms behind many scientific disciplines
from biology to behavioral sciences to economics. In its evolutionary form and
especially when the interacting agents are linked in a specific social network
the underlying solution concepts and methods are very similar to those applied
in non-equilibrium statistical physics. This review gives a tutorial-type
overview of the field for physicists. The first three sections introduce the
necessary background in classical and evolutionary game theory from the basic
definitions to the most important results. The fourth section surveys the
topological complications implied by non-mean-field-type social network
structures in general. The last three sections discuss in detail the dynamic
behavior of three prominent classes of models: the Prisoner's Dilemma, the
Rock-Scissors-Paper game, and Competing Associations. The major theme of the
review is in what sense and how the graph structure of interactions can modify
and enrich the picture of long term behavioral patterns emerging in
evolutionary games.Comment: Review, final version, 133 pages, 65 figure
Programming Robosoccer agents by modelling human behavior
The Robosoccer simulator is a challenging environment for artificial intelligence, where a human has to program a team of agents and introduce it into a soccer virtual environment. Most usually, Robosoccer agents are programmed by hand. In some cases, agents make use of Machine learning (ML) to adapt and predict the behavior of the opposite team, but the bulk of the agent has been preprogrammed. The main aim of this paper is to transform Robosoccer into an interactive game and let a human control a Robosoccer agent. Then ML techniques can be used to model his/her behavior from training instances generated during the play. This model will be used later to control a Robosoccer agent, thus imitating the human behavior. We have focused our research on low-level behavior, like looking for the ball, conducting the ball towards the goal, or scoring in the presence of opponent players. Results have shown that indeed, Robosoccer agents can be controlled by programs that model human play.Publicad
Correcting and improving imitation models of humans for Robosoccer agents
Proceeding of: 2005 IEEE Congress on Evolutionary Computation (CEC'05),Edimburgo, 2-5 Sept. 2005The Robosoccer simulator is a challenging environment, where a human introduces a team of agents into a football virtual environment. Typically, agents are programmed by hand, but it would be a great advantage to transfer human experience into football agents. The first aim of this paper is to use machine learning techniques to obtain models of humans playing Robosoccer. These models can be used later to control a Robosoccer agent. However, models did not play as smoothly and optimally as the human. To solve this problem, the second goal of this paper is to incrementally correct models by means of evolutionary techniques, and to adapt them against more difficult opponents than the ones beatable by the human.Publicad
On the coexistence of cooperators, defectors and conditional cooperators in the multiplayer iterated Prisoner's Dilemma
Recent experimental evidence [Gruji\'c et al., PLoS ONE 5, e13749 (2010)] on
the spatial Prisoner's Dilemma suggests that players choosing to cooperate or
not on the basis of their previous action and the actions of their neighbors
coexist with steady defectors and cooperators. We here study the coexistence of
these three strategies in the multiplayer iterated Prisoner's Dilemma by means
of the replicator dynamics. We consider groups with n = 2, 3, 4 and 5 players
and compute the payoffs to every type of player as the limit of a Markov chain
where the transition probabilities between actions are found from the
corresponding strategies. We show that for group sizes up to n = 4 there exists
an interior point in which the three strategies coexist, the corresponding
basin of attraction decreasing with increasing number of players, whereas we
have not been able to locate such a point for n = 5. We analytically show that
in the infinite n limit no interior points can arise. We conclude by discussing
the implications of this theoretical approach on the behavior observed in
experiments.Comment: 12 pages, 10 figures, uses elsart.cl
Grouping promotes both partnership and rivalry with long memory in direct reciprocity
Biological and social scientists have long been interested in understanding
how to reconcile individual and collective interests in iterated Prisoner's
Dilemma. Many effective strategies have been proposed, and they are often
categorized into one of two classes, `partners' and `rivals.' More recently,
another class, `friendly rivals,' has been identified in longer-memory strategy
spaces. Friendly rivals qualify as both partners and rivals: They fully
cooperate with themselves, like partners, but never allow their co-players to
earn higher payoffs, like rivals. Although they have appealing theoretical
properties, it is unclear whether they would emerge in evolving population
because most previous works focus on memory-one strategy space, where no
friendly rival strategy exists. To investigate this issue, we have conducted
large-scale evolutionary simulations in well-mixed and group-structured
populations and compared the evolutionary dynamics between memory-one and
memory-three strategy spaces. In a well-mixed population, the memory length
does not make a major difference, and the key factors are the population size
and the benefit of cooperation. Friendly rivals play a minor role because being
a partner or a rival is often good enough in a given environment. It is in a
group-structured population that memory length makes a stark difference: When
memory-three strategies are available, friendly rivals become dominant, and the
cooperation level nearly reaches a maximum, even when the benefit of
cooperation is so low that cooperation would not be achieved in a well-mixed
population. This result highlights the important interaction between group
structure and memory lengths that drive the evolution of cooperation.Comment: 18 pages, 11 figure
Comparing dynamitic difficulty adjustment and improvement in action game
A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Master ResearchDesigning a game difficulty is one of the key things as a game designer. Player will be feeling boring when the game designer makes the game too easy or too hard. In the past decades, most of single player games can allow players to choose the game difficulty either easy, normal or hard which define the overall game difficulty. In action game, these options are lack of flexibility and they are unsuitable to the player skill to meet the game difficulty. By using Dynamic Difficulty Adjustment (DDA), it can change the game difficulty in real time and it can match different player skills. In this paper, the final goal is the comparison of the three DDA systems in action game and apply an improved DDA. In order to apply a new improved DDA, this thesis will evaluate three chosen DDA systems with chosen action decision based AI for action game. A new DDA measurement formula is applied to the comparing section
Evolutionary instability of selfish learning in repeated games
Across many domains of interaction, both natural and artificial, individuals use past experience to shape future behaviors. The results of such learning processes depend on what individuals wish to maximize. A natural objective is one’s own success. However, when two such “selfish” learners interact with each other, the outcome can be detrimental to both, especially when there are conflicts of interest. Here, we explore how a learner can align incentives with a selfish opponent. Moreover, we consider the dynamics that arise when learning rules themselves are subject to evolutionary pressure. By combining extensive simulations and analytical techniques, we demonstrate that selfish learning is unstable in most classical two-player repeated games. If evolution operates on the level of long-run payoffs, selection instead favors learning rules that incorporate social (other-regarding) preferences. To further corroborate these results, we analyze data from a repeated prisoner’s dilemma experiment. We find that selfish learning is insufficient to explain human behavior when there is a trade-off between payoff maximization and fairness
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