This paper proposes a new algorithm for recovery of belief network structure from data handling hidden variables. It consists essentially in an extension of the CI algorithm of Spirtes et al. by restricting the number of conditional dependencies checked up to k variables and in an extension of the original CI by additional steps transforming so called partial including path graph into a belief ...

Non-impeding noisy-And Trees (NATs) provide a general, expressive, and efficient causal model for conditional probability tables (CPTs) in discrete Bayesian networks (BNs). A BN CPT may either be directly expressed as a NAT model or be compressed into one. Once CPTs in BNs are so expressed or compressed, complexity of inference (both space and time) can be significantly reduced. The most import...

The Problem: Many robot navigation tasks require accurate sensing of the robot’s location. This is a particularly difficult problem when the environment is uninstrumented or has never been explored before. This paper addresses the problem of computing an accurate estimate of the robot’s position given a sequence of scans from an optical sensor such as a monocular camera, a stereo camera, or a l...

The application of the formal framework of causal Bayesian Networks to children's causal learning provides the motivation to examine the link between judgments about the causal structure of a system, and the ability to make inferences about interventions on components of the system. Three experiments examined whether children are able to make correct inferences about interventions on different ...

Bayesian networks have the ability to combine sparse data, prior assumptions and expert judgment into a single causal model. We present such a model of an Extreme Programming environment and show how it can learn from project data in order to make quantitative effort predictions and risk assessments. This is illustrated with the use of a real world industrial project.

In this paper we introduce multi-agent causal models (MACMs) which are an extension of causal Bayesian networks to a multi-agent setting. Instead of 1 single agent modeling the entire domain, there are several agents each modeling non-disjoint subsets of the domain. Every agent has a causal model, determined by an acyclic causal diagram and a joint probability distribution over its observed var...

To specify a Bayes net (BN), a conditional probability table (CPT), often of an effect conditioned on its n causes, needs to be assessed for each node. It generally has the complexity exponential on n. The non-impeding noisy-AND (NIN-AND) tree is a recently developed causal model that reduces the complexity to linear, while modeling both reinforcing and undermining interactions among causes. Ac...

The development of the Netherlands international airport Schiphol has been the subject of fierce political debate for several decades. One of the considerations has been the safety of the population living around the airport, the density of which has been and still is growing. In the debate about the acceptability of the risks associated with the air traffic above The Netherlands extensive use ...

To specify a Bayes net (BN), a conditional probability table (CPT), often of an effect conditioned on its n causes, needs assessed for each node. Its complexity is generally exponential in n. Noisy-OR reduces the complexity to linear, but can only represent reinforcing causal interactions. The non-impeding noisy-AND (NIN-AND) tree is the first causal model that explicitly expresses reinforcemen...

Data containing deterministic relations cannot be handled by current constraint-based causal learning algorithms; they entail conditional independencies that cannot be represented by a faithful graph. Violation of the faithfulness property is characterized by an information equivalence of two sets of variables with respect to a reference variable. The conditional independencies do not provide i...