Sander Beckers

Counterfactual dependency and actual causation in CP-logic and structural models: a comparison

The solution to the problem of actual causation - i.e. determining what caused an effect in a specific scenario - put forward by Halpern and Pearl recently has received a lot of attention. It forms the basis for many other approaches within the dominant tradition of counterfactual theories of causation. However, their solu- tion runs into a number of difficulties for a certain type of examples exhibiting so- called switching causation and early preemption. We discuss these in the light of the core concept of counterfactual dependency, and offer a comparison with the recent definition of actual causation formulated in CP-logic. We argue both that for this type of examples the CP-logic definition provides better anwers, and that it does more justice to the fundamental intuitions underlying counterfactual dependency.

A general framework for defining and extending actual causation using CP-logic

A central problem in the field of causal modelling is to provide a suitable definition of actual causation, i.e., to define when one specific event caused another. Although current research contains many different definitions, it is pervaded with ambiguities and confusion. Our research has two main goals. First, we wish to provide a clear way to compare competing definitions, and improve upon them so that they can be applied to a more diverse range of instances, including non-deterministic ones. To achieve this we provide a general, abstract definition of actual causation, formulated in the context of the expressive language of CP-logic (Causal Probabilistic logic). We will then show that three recent definitions by Ned Hall (originally formulated for structural models) and a definition of our own (formulated for CP-logic directly) can be viewed and directly compared as instantiations of this abstract definition, which also allows them to deal with a broader range of examples. Second, our framework allows for improving on definitions of actual causation in another way, by incorporating the influence of normality. A recent paper by Halpern and Hitchcock draws on empirical research regarding peoples causal judgements, to suggest a graded and context-sensitive notion of actual causation. We rephrase their approach into the probabilistic setting of our abstract definition, allowing us to improve it. A general, parametrised, definition of actual causation is developed using CP-logic.Four instantiations of this definition are given.These definitions are generalisations of four existing definitions.An extension to causation is developed to incorporate the influence of normality.This extension improves an extension to causation by Halpern and Hitchcock.

Faster and more intelligent object detection by combining OpenCL and KR

In this paper we present a fast implementation of a robust object detector by using OpenCL. The use of fast object detection is of great use for a broad range of applications in multiple domains. OpenCL allows for scalability to more performant and different types of hardware, with minimal changes to the implementation. By using a GPU as execution device, we exploit the data parallelism opportunities of the algorithm. We also discuss the use of knowledge representation as a means to integrate expert knowledge into applications. This can be used both for faster processing by limiting the searching space, and for applications to work more autonomous by exploiting a higher level of intelligence.