47,292 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/
Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games
Many artificial intelligence (AI) applications often require multiple
intelligent agents to work in a collaborative effort. Efficient learning for
intra-agent communication and coordination is an indispensable step towards
general AI. In this paper, we take StarCraft combat game as a case study, where
the task is to coordinate multiple agents as a team to defeat their enemies. To
maintain a scalable yet effective communication protocol, we introduce a
Multiagent Bidirectionally-Coordinated Network (BiCNet ['bIknet]) with a
vectorised extension of actor-critic formulation. We show that BiCNet can
handle different types of combats with arbitrary numbers of AI agents for both
sides. Our analysis demonstrates that without any supervisions such as human
demonstrations or labelled data, BiCNet could learn various types of advanced
coordination strategies that have been commonly used by experienced game
players. In our experiments, we evaluate our approach against multiple
baselines under different scenarios; it shows state-of-the-art performance, and
possesses potential values for large-scale real-world applications.Comment: 10 pages, 10 figures. Previously as title: "Multiagent
Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat
Games", Mar 201
Collaboration in Social Networks
The very notion of social network implies that linked individuals interact
repeatedly with each other. This allows them not only to learn successful
strategies and adapt to them, but also to condition their own behavior on the
behavior of others, in a strategic forward looking manner. Game theory of
repeated games shows that these circumstances are conducive to the emergence of
collaboration in simple games of two players. We investigate the extension of
this concept to the case where players are engaged in a local contribution game
and show that rationality and credibility of threats identify a class of Nash
equilibria -- that we call "collaborative equilibria" -- that have a precise
interpretation in terms of sub-graphs of the social network. For large network
games, the number of such equilibria is exponentially large in the number of
players. When incentives to defect are small, equilibria are supported by local
structures whereas when incentives exceed a threshold they acquire a non-local
nature, which requires a "critical mass" of more than a given fraction of the
players to collaborate. Therefore, when incentives are high, an individual
deviation typically causes the collapse of collaboration across the whole
system. At the same time, higher incentives to defect typically support
equilibria with a higher density of collaborators. The resulting picture
conforms with several results in sociology and in the experimental literature
on game theory, such as the prevalence of collaboration in denser groups and in
the structural hubs of sparse networks
Gaming techniques and the product development process : commonalities and cross-applications
The use of computer-based tools is now firmly embedded within the product development process, providing a wide range of uses from visualisation to analysis. However, the specialisation required to make effective use of these tools has led to the compartmentalisation of expertise in design teams, resulting in communication problems between individual members. This paper therefore considers how computer gaming techniques and strategies could be used to enhance communication and group design activities throughout the product design process
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