Fewer epistemological challenges for connectionism

Seventeen years ago, John McCarthy wrote the note Epistemological challenges for connectionism as a response to Paul Smolensky’s paper 'On the proper treatment of connectionism'. I will discuss the extent to which the four key challenges put forward by McCarthy have been solved, and what are the new challenges ahead. I argue that there are fewer epistemological challenges for connectionism, but progress has been slow. Nevertheless, there is now strong indication that neural-symbolic integration can provide effective systems of expressive reasoning and robust learning due to the recent developments in the field

The significance of digitizing for the film industry and its consequences

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Quo vadis? Reliable and practical rule extraction from neural networks

Rule extraction from neural network algorithms have been investigated for two decades and there have been significant applications. Despite this level of success, rule extraction from neural network methods are generally not part of data mining tools, and a significant commercial breakthrough may still be some time away. This paper briefly reviews the state-of-the-art and points to some of the obstacles, namely a lack of evaluation techniques in experiments and larger benchmark data sets. A significant new development is the view that rule extraction from neural networks is an interactive process which actively involves the user. This leads to the application of assessment and evaluation techniques from information retrieval which may lead to a range of new methods

The logical way to be artificially intelligent

Abstract. Abductive logic programming (ALP) can be used to model reactive, proactive and pre-active thinking in intelligent agents. Reactive thinking assimilates observations of changes in the environment, whereas proactive thinking reduces goals to sub-goals and ultimately to candidate actions. Pre-active thinking generates logical consequences of candidate actions, to help in deciding between the alternatives. These different ways of thinking are compatible with any way of deciding between alternatives, including the use of both decision theory and heuristics. The different forms of thinking can be performed as they are needed, or they can be performed in advance, transforming high-level goals and beliefs into lower-level condition-action rule form, which can be implemented in neural networks. Moreover, the higher-level and lowerlevel representations can operate in tandem, as they do in dual-process models of thinking. In dual process models, intuitive processes form judgements rapidly, sub-consciously and in parallel, while deliberative processes form and monitor judgements slowly, consciously and serially. ALP used in this way can not only provide a framework for constructing artificial agents, but can also be used as a cognitive model of human agents. As a cognitive model, it combines both a descriptive model of how humans actually think with a normative model of humans can think more effectively.