30,427 research outputs found
Agents for educational games and simulations
This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications
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Reinforcement Learning for Generative Art
Reinforcement learning (RL) is an efficient class of sequential decision-making algorithms that have achieved remarkable success in a broad range of applications, such as robotic manipulations, strategic games, or autonomous driving. The most well-known example of reinforcement learning is AlphaGo, a computer program that plays the board game Go and outperforms top human Go players. Unlike other two major machine learning categories, supervised learning and unsupervised learning, in which media artists are actively engaged, reinforcement learning has yet to result in many creative applications. Generative art is usually driven, in whole or in part, by autonomous systems that are derived from a set of rules. Interestingly, an RL policy can be seen as an autonomous system where the rules are learned by interacting with its environment. Regardless of its initial purpose, reinforcement learning has the potential to expand the boundary of generative art. However, a formal process of applying reinforcement learning to generative art does not yet exist and the current RL tools require an in-depth understanding of RL concepts. To bridge the gap, the first part of the dissertation introduces a conceptual framework to adapt reinforcement learning for generative art. The framework proposes a term RL-based generative art to denote a novel form of generative art of which the use of RL agents is the key element. The creative process of RL-based generative art and possible emergent behaviors are discussed in the framework. This leads to a discussion of several author's related practices on generative art, deep-learning art, and reinforcement learning. Those practices are critical for understanding the conceptual and technical details of each component in order to construct the framework. The second part introduces RL5, a JavaScript library for rapidly prototyping RL environments and training RL policies in web browsers. The library combines RL algorithms and RL environments into one framework and is fully compatible with p5.js. RL5 is developed with a particular focus on simplicity to favor (re)usability of RL algorithms and development of RL environments. Specifically, the library implemented three RL algorithms, Tabular Q-learning, REINFORCE, and DDPG, to cover all the three families of model-free RL, and nine RL environments that six of them address autonomous agents in steering behaviors, which can be used as building blocks for complex systems. Finally, the author demonstrates four different use cases of how to apply RL5 for pedagogical and creative applications
Player agency in interactive narrative: audience, actor & author
The question motivating this review paper is, how can
computer-based interactive narrative be used as a constructivist learn-
ing activity? The paper proposes that player agency can be used to
link interactive narrative to learner agency in constructivist theory,
and to classify approaches to interactive narrative. The traditional
question driving research in interactive narrative is, âhow can an in-
teractive narrative deal with a high degree of player agency, while
maintaining a coherent and well-formed narrative?â This question
derives from an Aristotelian approach to interactive narrative that,
as the question shows, is inherently antagonistic to player agency.
Within this approach, player agency must be restricted and manip-
ulated to maintain the narrative. Two alternative approaches based
on Brechtâs Epic Theatre and Boalâs Theatre of the Oppressed are
reviewed. If a Boalian approach to interactive narrative is taken the
conflict between narrative and player agency dissolves. The question
that emerges from this approach is quite different from the traditional
question above, and presents a more useful approach to applying in-
teractive narrative as a constructivist learning activity
Automated Game Design Learning
While general game playing is an active field of research, the learning of
game design has tended to be either a secondary goal of such research or it has
been solely the domain of humans. We propose a field of research, Automated
Game Design Learning (AGDL), with the direct purpose of learning game designs
directly through interaction with games in the mode that most people experience
games: via play. We detail existing work that touches the edges of this field,
describe current successful projects in AGDL and the theoretical foundations
that enable them, point to promising applications enabled by AGDL, and discuss
next steps for this exciting area of study. The key moves of AGDL are to use
game programs as the ultimate source of truth about their own design, and to
make these design properties available to other systems and avenues of inquiry.Comment: 8 pages, 2 figures. Accepted for CIG 201
Reuse of Neural Modules for General Video Game Playing
A general approach to knowledge transfer is introduced in which an agent
controlled by a neural network adapts how it reuses existing networks as it
learns in a new domain. Networks trained for a new domain can improve their
performance by routing activation selectively through previously learned neural
structure, regardless of how or for what it was learned. A neuroevolution
implementation of this approach is presented with application to
high-dimensional sequential decision-making domains. This approach is more
general than previous approaches to neural transfer for reinforcement learning.
It is domain-agnostic and requires no prior assumptions about the nature of
task relatedness or mappings. The method is analyzed in a stochastic version of
the Arcade Learning Environment, demonstrating that it improves performance in
some of the more complex Atari 2600 games, and that the success of transfer can
be predicted based on a high-level characterization of game dynamics.Comment: Accepted at AAAI 1
Overview on agent-based social modelling and the use of formal languages
Transdisciplinary Models and Applications investigates a variety of programming languages used in validating and verifying models in order to assist in their eventual implementation. This book will explore different methods of evaluating and formalizing simulation models, enabling computer and industrial engineers, mathematicians, and students working with computer simulations to thoroughly understand the progression from simulation to product, improving the overall effectiveness of modeling systems.Postprint (author's final draft
Enaction-Based Artificial Intelligence: Toward Coevolution with Humans in the Loop
This article deals with the links between the enaction paradigm and
artificial intelligence. Enaction is considered a metaphor for artificial
intelligence, as a number of the notions which it deals with are deemed
incompatible with the phenomenal field of the virtual. After explaining this
stance, we shall review previous works regarding this issue in terms of
artifical life and robotics. We shall focus on the lack of recognition of
co-evolution at the heart of these approaches. We propose to explicitly
integrate the evolution of the environment into our approach in order to refine
the ontogenesis of the artificial system, and to compare it with the enaction
paradigm. The growing complexity of the ontogenetic mechanisms to be activated
can therefore be compensated by an interactive guidance system emanating from
the environment. This proposition does not however resolve that of the
relevance of the meaning created by the machine (sense-making). Such
reflections lead us to integrate human interaction into this environment in
order to construct relevant meaning in terms of participative artificial
intelligence. This raises a number of questions with regards to setting up an
enactive interaction. The article concludes by exploring a number of issues,
thereby enabling us to associate current approaches with the principles of
morphogenesis, guidance, the phenomenology of interactions and the use of
minimal enactive interfaces in setting up experiments which will deal with the
problem of artificial intelligence in a variety of enaction-based ways
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