The Problem The task of developing qualitative and causal reasoning systems to perform problem solving on physical systems has two aspects : (1) Designing representations for structure, behavior, and causality within which to describe the physical systems of interest and their constituent objects and processes, and (2) Developing algorithms which operate on the chosen representation to efficien...

We address the problem of learning grounded causal models: systems of concepts that are connected by causal relations and explicitly grounded in perception. We present a Bayesian framework for learning these models—both a causal Bayesian network structure over variables and the consequential region of each variable in perceptual space—from dynamic perceptual evidence. Using a novel experimental...

We present simulations of causal dynamical collapse models of field theories on a 1 + 1 null lattice. We use our simulations to compare and contrast two possible interpretations of the models, one in which the field values are real and the other in which the state vector is real. We suggest that a procedure of coarse graining and renormalising the fundamental field can overcome its noisiness an...

This chapter discusses the use of directed acyclic graphs (DAGs) for causal inference in the observational social sciences. It focuses on DAGs’ main uses, discusses central principles, and gives applied examples. DAGs are visual representations of qualitative causal assumptions: They encode researchers’ beliefs about how the world works. Straightforward rules map these causal assumptions onto t...

Rasch's unidimensional models for measurement show how to connect object measures (e.g., reader abilities), measurement mechanisms (e.g., machine-generated cloze reading items), and observational outcomes (e.g., counts correct on reading instruments). Substantive theory shows what interventions or manipulations to the measurement mechanism can be traded off against a change to the object measur...

Despite their success in transferring the powerful human faculty of causal reasoning to a mathematical and computational form, causal models have not been widely used in the context of core AI applications such as robotics. In this paper, we argue that this discrepancy is due to the static, propositional nature of existing causality formalisms that make them difficult to apply in dynamic real-w...

This article describes the use of Bayes factors for comparing dynamic causal models (DCMs). DCMs are used to make inferences about effective connectivity from functional magnetic resonance imaging (fMRI) data. These inferences, however, are contingent upon assumptions about model structure, that is, the connectivity pattern between the regions included in the model. Given the current lack of de...

In previous chapters, we have discussed the ways in which we can model how responsibility can be assigned to agents and how responsibility models can facilitate discussions about the nature of responsibilities in organisations. These models document responsibilities in an organisation, provide insights into possible vulnerabilities due to responsibility misassignment and facilitate discussion a...