In the past, control systems for automation and robotics and the expert systems employed in artificial intelligence were generally based on classical, or Boolean, logic. However, this proved to be inadequate by virtue of its binary nature, for portraying the uncertainties and inconsistencies of the 'real' world, and so from the late 1990s, research has been ongoing into the application of paraconsistent, or non-classical logics in these fields. This book aggregates much of this research, from 1999 up to the present. Organized to facilitate an understanding of the theory and the development of the applied methods, Uncertainty Treatment Using Praconsistent Logic presents the material in a sequential fashion and is divided into three parts. Notions of Paraconsistent Annotated Logic (PAL) summarizes the basic theory and fundamentals of the subject. The second part, Paraconsistent Analysis Networks (PANets), describes the utilization of paraconsistent logic in constructing networks which can deal with representative data from uncertain information. The final section, Paraconsistent Artificial Neural Networks (PANNets), is composed of six chapters which chart the applications of PAL, from a comparison between Paraconsistent Analysis Nodes (PANs) and the action of the human brain through to complex PANNet architecture capable of processing signals inspired by human brain function. This invaluable state-of-the-art overview will be of interest to all those involved with the development of robotics or artificial intelligence and will serve as reference for future application of paraconsistent logics in all computer and electronic systems.
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