1,196 research outputs found
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence
Learning agents that are not only capable of taking tests, but also
innovating is becoming a hot topic in AI. One of the most promising paths
towards this vision is multi-agent learning, where agents act as the
environment for each other, and improving each agent means proposing new
problems for others. However, existing evaluation platforms are either not
compatible with multi-agent settings, or limited to a specific game. That is,
there is not yet a general evaluation platform for research on multi-agent
intelligence. To this end, we introduce Arena, a general evaluation platform
for multi-agent intelligence with 35 games of diverse logics and
representations. Furthermore, multi-agent intelligence is still at the stage
where many problems remain unexplored. Therefore, we provide a building toolkit
for researchers to easily invent and build novel multi-agent problems from the
provided game set based on a GUI-configurable social tree and five basic
multi-agent reward schemes. Finally, we provide Python implementations of five
state-of-the-art deep multi-agent reinforcement learning baselines. Along with
the baseline implementations, we release a set of 100 best agents/teams that we
can train with different training schemes for each game, as the base for
evaluating agents with population performance. As such, the research community
can perform comparisons under a stable and uniform standard. All the
implementations and accompanied tutorials have been open-sourced for the
community at https://sites.google.com/view/arena-unity/
Multiparty Dynamics and Failure Modes for Machine Learning and Artificial Intelligence
An important challenge for safety in machine learning and artificial
intelligence systems is a~set of related failures involving specification
gaming, reward hacking, fragility to distributional shifts, and Goodhart's or
Campbell's law. This paper presents additional failure modes for interactions
within multi-agent systems that are closely related. These multi-agent failure
modes are more complex, more problematic, and less well understood than the
single-agent case, and are also already occurring, largely unnoticed. After
motivating the discussion with examples from poker-playing artificial
intelligence (AI), the paper explains why these failure modes are in some
senses unavoidable. Following this, the paper categorizes failure modes,
provides definitions, and cites examples for each of the modes: accidental
steering, coordination failures, adversarial misalignment, input spoofing and
filtering, and goal co-option or direct hacking. The paper then discusses how
extant literature on multi-agent AI fails to address these failure modes, and
identifies work which may be useful for the mitigation of these failure modes.Comment: 12 Pages, This version re-submitted to Big Data and Cognitive
Computing, Special Issue "Artificial Superintelligence: Coordination &
Strategy
Generating Levels That Teach Mechanics
The automatic generation of game tutorials is a challenging AI problem. While
it is possible to generate annotations and instructions that explain to the
player how the game is played, this paper focuses on generating a gameplay
experience that introduces the player to a game mechanic. It evolves small
levels for the Mario AI Framework that can only be beaten by an agent that
knows how to perform specific actions in the game. It uses variations of a
perfect A* agent that are limited in various ways, such as not being able to
jump high or see enemies, to test how failing to do certain actions can stop
the player from beating the level.Comment: 8 pages, 7 figures, PCG Workshop at FDG 2018, 9th International
Workshop on Procedural Content Generation (PCG2018
Program: The 15th International Conference on Gambling & Risk Taking
Listing of presentations, events, and sponsor
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
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