The MADP Toolbox: An Open-Source Library for Planning and Learning in (Multi-)Agent Systems

This article describes the MultiAgent Decision Process (MADP) toolbox, a software library to support planning and learning for intelligent agents and multiagent systems in un- certain environments. Some of its key features are that it sup- ports partially observable environments and stochastic tran- sition 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 mak- ing under various assumptions of observability and coopera- tion, such as Dec-POMDPs and POSGs; provides tools and parsers to quickly prototype new problems; provides an ex- tensive 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

Covering the Recursive Sets

Contains fulltext : 147448.pdf (preprint version ) (Open Access

Auxiliary particle filter robot localization from high-dimensional sensor observations

We apply the auxiliary particle filter algorithm of Pitt and Shephard (1999) to the problem of robot localization. To deal with the high-dimensional sensor observations (images) and an unknown observation model., we propose the use of an inverted nonparametric observation model computed by nearest neighbor conditional density estimation. We show that the proposed model can lead to a fully adapted optimal filter, and is able to successfully handle image occlusion and robot kidnap. The proposed algorithm is very simple to implement and exhibits a high degree of robustness in practice. We report experiments involving robot localization from omnidirectional vision in an indoor environment