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. 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 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; it is released under a GNU GPL v3 license; and is written in C++ and designed to be extensible via the object-oriented paradigm.

This demo paper serves to describe a research platform for autonomous surveillance by mobile robots. System components from various technical disciplines (including robot navigation, computer vision, discrete event systems, and human-computer interaction) have been combined together into a cohesive, modular system architecture. We employ our architecture to experiment with state-of-the-art methods in planning and decision making under uncertainty, which we demonstrate enable robots to effectively patrol and respond to surveillance events in a large indoor environment.