6,108 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/
Evoplex: A platform for agent-based modeling on networks
Agent-based modeling and network science have been used extensively to
advance our understanding of emergent collective behavior in systems that are
composed of a large number of simple interacting individuals or agents. With
the increasing availability of high computational power in affordable personal
computers, dedicated efforts to develop multi-threaded, scalable and
easy-to-use software for agent-based simulations are needed more than ever.
Evoplex meets this need by providing a fast, robust and extensible platform for
developing agent-based models and multi-agent systems on networks. Each agent
is represented as a node and interacts with its neighbors, as defined by the
network structure. Evoplex is ideal for modeling complex systems, for example
in evolutionary game theory and computational social science. In Evoplex, the
models are not coupled to the execution parameters or the visualization tools,
and there is a user-friendly graphical interface which makes it easy for all
users, ranging from newcomers to experienced, to create, analyze, replicate and
reproduce the experiments.Comment: 6 pages, 5 figures; accepted for publication in SoftwareX [software
available at https://evoplex.org
An Approach to Agent-Based Service Composition and Its Application to Mobile
This paper describes an architecture model for multiagent systems that was developed in the European project LEAP (Lightweight Extensible Agent Platform). Its main feature is a set of generic services that are implemented independently of the agents and can be installed into the agents by the application developer in a flexible way. Moreover, two applications using this architecture model are described that were also developed within the LEAP project. The application domain is the support of mobile, virtual teams for the German automobile club ADAC and for British Telecommunications
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