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Making logic programs reactive
Logic programming languages based on linear logic have been of recent interest, particularly as such languages provide a logical basis for programs which execute within a dynamic environment. Most of these languages are implemented using standard resolution or backward-chaining techniques. However, there are applications for which the use of forward-chaining techniques within a dynamic environment are appropriate, such as genetic algorithms, active databases and agent-based systems, and for which it is difficult or impossible to specify an appropriate goal in advance. In this paper we discuss the foundations for a forward-chaining approach (or in logic programming parlance, a bottom-up approach) to the execution of linear logic programs, which thus provides forward-chaining within a dynamic environment. In this way it is possible not only to execute programs in a forward-chaining manner, but also to combine forward- and backward-chaining execution. We describe and discuss the appropriate inference rules for such a system, the formal results about such rules, the role of search strategies, and applications
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Neurons and symbols: a manifesto
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of
neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty
Semantic and logical foundations of global computing: Papers from the EU-FET global computing initiative (2001–2005)
Overvew of the contents of the volume "Semantic and logical foundations of global computing
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