Abstract
This article describes the MultiAgent Decision Process (MADP) toolbox, a software library to support planning and learning for intelligent agents and multiagent systems in uncertain environments. Some of its key features are that it supports partially observable environments and stochastic transition models; has unified support for single- and multiagent systems; provides a large number of models for decision- Theoretic decision making, including one-shot decision mak-ing (e.g., Bayesian games) and sequential decision making under various assumptions of observability and cooperation, such as Dec-POMDPs and POSGs; provides tools and parsers to quickly prototype new problems; provides an extensive range of planning and learning algorithms for single- and multiagent systems; and is written in C++ and designed to be extensible via the object-oriented paradigm.
Original language | English |
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Title of host publication | Sequential Decision Making for Intelligent Agents - Papers from the AAAI 2015 Fall Symposium, Technical Report |
Publisher | AI Access Foundation |
Pages | 59-62 |
Number of pages | 4 |
Volume | FS-15-06 |
ISBN (Electronic) | 9781577357520 |
Publication status | Published - 1 Jan 2015 |
Event | AAAI 2015 Fall Symposium - Arlington, United States Duration: 12 Nov 2015 → 14 Nov 2015 |
Conference
Conference | AAAI 2015 Fall Symposium |
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Country/Territory | United States |
City | Arlington |
Period | 12/11/15 → 14/11/15 |