Heuristics, abductions and adaptive algorithms : AA toolbox for human decision making

Without the ability to decide and thus give order to a universe - that was originally a chaotic mass of data and phenomena with-out regularity and clear and decipherable structures - the evolution of our species would have been unthinkable. The adaptation to that universe took place on the basis of incomplete and fragmentary information, limited cognitive capacities and narrow time. That scenario selected adaptive behaviors made of fast, imperfect, but highly effective cognitive solutions: heuristics. Although these natural logic tools are commonly studied in cognitive psychology - particularly in the field of judgment and decision-making - their application in Artificial Intelligence and computer simulation of cognition is disclosing prospects for the study of decision-making processes, starting with the classification of alternatives, in search algorithms, to make decisions on the basis of available information. Aim of this paper is to show how this logic is subtended by rules of inference strictly linked to the 'natural' way of reasoning. And moreover, how these inferences could constitute, in the wider AI and ICT horizon, the test bench for the development of neural network representation and learning systems for the understanding of human action.