Abstract: libDAI is a free/opensource C++ library (licensed under GPL) that provides
implementations of various (deterministic) approximate inference methods for
discrete graphical models. libDAI supports arbitrary factor graphs with
discrete variables (this includes discrete Markov Random Fields and Bayesian
Abstract: Probabilistic graphical models, and in particular Bayesian networks, are nowadays well established as a modeling tool for domains with
uncertainty. A drawback is that large, complex graphical models are intractable for exact computation. Therefore there is a lot of research interest in approximate inference.
The lack of opensource "reference" implementations hampers progress in research on approximate inference. Methods differ widely in terms of quality and performance characteristics, which also depend in different ways on various properties of the graphical models. Finding the best approximate inference method for a particular application therefore often requires empirical comparisons. However, implementing and debugging these methods takes a lot of time which could otherwise be spent on research. Therefore we have developed libDAI. libDAI is a free/opensource C++ library (licensed under GPL) that provides implementations of various (deterministic) approximate inference methods for discrete graphical models. libDAI supports arbitrary `factor graphs` with discrete variables (this includes discrete Markov Random Fields and Bayesian Networks).
This release is an additional contribution to the LibDAI library. This code implements the Z2 algorithm, a particular way of correcting the Belief Propagation (BP) solution, developed in the ICIS project SNN1 (see Gomez (2009), Approximate inference on planar graphs using Loop Calculus and Belief Propagation).
Abstract: This paper introduces the MultiAgent Decision Process software toolbox, an opensource C++ library for decision-theoretic planning under uncertainty in multiagent systems. It provides support for several multiagent models, such as POSGs, Dec-POMDPs and MMDPs. The toolbox aims to reduce development time for planning algorithms and to provide a benchmarking platform by providing a number of commonly used problem descriptions. It features a parser for a text-based ﬁle format for discrete Dec-POMDPs, shared functionality for planning algorithms, as well as the implementation of several Dec-POMDP planners. We describe design goals and architecture of the toolbox, and provide an overview of its functionality, illustrated by some usage examples. Finally we report on current and future work.