Bøger i Applied Logic serien

This book provides the blueprint of a thinking machine.While most of the current works in Artificial Intelligence (AI) focus on individual aspects of intelligence and cognition, the project described in this book, Non-Axiomatic Reasoning System (NARS), is designed and developed to attack the AI problem as a whole.This project is based on the belief that what we call "intelligence" can be understood and reproduced as "the capability of a system to adapt to its environment while working with insufficient knowledge and resources". According to this idea, a novel reasoning system is designed, which challenges all the dominating theories in how such a system should be built. The system carries out reasoning, learning, categorizing, planning, decision making, etc., as different facets of the same underlying process. This theory also provides unified solutions to many problems in AI, logic, psychology, and philosophy.This book is the most comprehensive description of this decades-long project, including its philosophical foundation, methodological consideration, conceptual design details, its implications in the related fields, as well as its similarities and differences to many related works in cognitive sciences.

In contrast to the prevailing tradition in epistemology, the focus in this book is on low-level inferences, i.e., those inferences that we are usually not consciously aware of and that we share with the cat nearby which infers that the bird which she sees picking grains from the dirt, is able to fly. Presumably, such inferences are not generated by explicit logical reasoning, but logical methods can be used to describe and analyze such inferences.Part 1 gives a purely system-theoretic explication of belief and inference. Part 2 adds a reliabilist theory of justification for inference, with a qualitative notion of reliability being employed. Part 3 recalls and extends various systems of deductive and nonmonotonic logic and thereby explains the semantics of absolute and high reliability. In Part 4 it is proven that qualitative neural networks are able to draw justified deductive and nonmonotonic inferences on the basis of distributed representations. This is derived from a soundness/completeness theorem with regard to cognitive semantics of nonmonotonic reasoning. The appendix extends the theory both logically and ontologically, and relates it to A. Goldman's reliability account of justified belief.

- Donation refusal is high in all the regions of Argentina. - The deficient operative structure is a negative reality that allows inadequate donor maintenance and organ procurement. - In more developed regions, there are a high number of organs which are not utilized. This is true for heart, liver and lungs. Small waiting lists for these organs probably reflect an inadequate economic coverage for these organ transplant activities. - There is a long waiting list for cadaveric kidney transplants, which reflect poor procurement and transplant activity. - Lack of awareness by many physicians leads to the denouncing of brain deaths. In spite of these factors, we can say that there has been a significant growth in organ procuration and transplantation in 1993, after the regionalization of the INCUCAI. Conclusions Is there a shortage of organs in Argentina? There may be. But the situation in Argentina differs from that in Europe, as we have a pool of organs which are not utilized (donation refusal, operational deficits, lack of denouncing of brain deaths). Perhaps, in the future, when we are able to make good use of all the organs submitted for transplantation, we will be able to say objectively whether the number of organs is sufficient or not. Acknowledgements I would like to thank the University of Lyon and the Merieux Foundation, especially Professors Traeger, Touraine and Dr. Dupuy for the honour of being invited to talk about the issue of organ procurement.