55,538 research outputs found
Protocol Requirements for Self-organizing Artifacts: Towards an Ambient Intelligence
We discuss which properties common-use artifacts should have to collaborate
without human intervention. We conceive how devices, such as mobile phones,
PDAs, and home appliances, could be seamlessly integrated to provide an
"ambient intelligence" that responds to the user's desires without requiring
explicit programming or commands. While the hardware and software technology to
build such systems already exists, as yet there is no standard protocol that
can learn new meanings. We propose the first steps in the development of such a
protocol, which would need to be adaptive, extensible, and open to the
community, while promoting self-organization. We argue that devices,
interacting through "game-like" moves, can learn to agree about how to
communicate, with whom to cooperate, and how to delegate and coordinate
specialized tasks. Thus, they may evolve a distributed cognition or collective
intelligence capable of tackling complex tasks.Comment: To be presented at 5th International Conference on Complex System
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
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
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
AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Response Against Stationary Opponents
A satisfactory multiagent learning algorithm should, {\em at a minimum},
learn to play optimally against stationary opponents and converge to a Nash
equilibrium in self-play. The algorithm that has come closest, WoLF-IGA, has
been proven to have these two properties in 2-player 2-action repeated
games--assuming that the opponent's (mixed) strategy is observable. In this
paper we present AWESOME, the first algorithm that is guaranteed to have these
two properties in {\em all} repeated (finite) games. It requires only that the
other players' actual actions (not their strategies) can be observed at each
step. It also learns to play optimally against opponents that {\em eventually
become} stationary. The basic idea behind AWESOME ({\em Adapt When Everybody is
Stationary, Otherwise Move to Equilibrium}) is to try to adapt to the others'
strategies when they appear stationary, but otherwise to retreat to a
precomputed equilibrium strategy. The techniques used to prove the properties
of AWESOME are fundamentally different from those used for previous algorithms,
and may help in analyzing other multiagent learning algorithms also
Strategies for fast convergence in semiotic dynamics
Semiotic dynamics is a novel field that studies how semiotic conventions
spread and stabilize in a population of agents. This is a central issue both
for theoretical and technological reasons since large system made up of
communicating agents, like web communities or artificial embodied agents teams,
are getting widespread. In this paper we discuss a recently introduced simple
multi-agent model which is able to account for the emergence of a shared
vocabulary in a population of agents. In particular we introduce a new
deterministic agents' playing strategy that strongly improves the performance
of the game in terms of faster convergence and reduced cognitive effort for the
agents.Comment: 6 pages, 6 figure
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