The ultimate goal of research into computational intelligence is the construction of a fully embodied and fully autonomous artificial agent. This ultimate artificial agent must not only be able to act, but it must be able to act morally. In order to realize this goal, a number of challenges must be met, and a number of questions must be answered, the upshot being that, in doing so, the form of agency to which we must aim in developing artificial agents comes (...) into focus. This chapter explores these issues, and from its results details a novel approach to meeting the given conditions in a simple architecture of information processing. (shrink)

This book discusses how scientific and other types of cognition make use of models, abduction, and explanatory reasoning in order to produce important or creative changes in theories and concepts. It includes revised contributions presented during the international conference on Model-Based Reasoning (MBR’015), held on June 25-27 in Sestri Levante, Italy. The book is divided into three main parts, the first of which focuses on models, reasoning and representation. It highlights key theoretical concepts from an applied perspective, addressing issues concerning (...) information visualization, experimental methods and design. The second part goes a step further, examining abduction, problem solving and reasoning. The respective contributions analyze different types of reasoning, discussing various concepts of inference and creativity and their relationship with experimental data. In turn, the third part reports on a number of historical, epistemological and technological issues. By analyzing possible contradictions in modern research and describing representative case studies in experimental research, this part aims at fostering new discussions and stimulating new ideas. All in all, the book provides researchers and graduate students in the field of applied philosophy, epistemology, cognitive science and artificial intelligence alike with an authoritative snapshot of current theories and applications of model-based reasoning. (shrink)

This paper focuses on abduction as explicit or readily formulatable inference to possible explanatory hypotheses--as contrasted with inference to conceptual innovations or abductive logic as a cycle of hypotheses, deduction of consequences and inductive testing. Inference to an explanation is often a matter of projection or extrapolation of elements of accepted theory for the solution of outstanding problems in particular domains of inquiry. I say "projections or extrapolation" of accepted theory, but I mean to point to something broader and suggest (...) how elements of accepted theory constrain emergent models and plausible inferences to explanations--in a quasi-rationalist fashion. I draw on illustrations from quantum gravity below just because there is so little direct evidence available in the field. It is in such cases that Peirce's discussions of abductive inference provide the most plausible support for the idea of a logic of abduction--as inference to readily formulatable explanatory hypotheses. The possible need for conceptual innovation points to the limits on the possibility of a logic of abduction of a more rationalistic character--selecting uniquely superior explanations. Abduction conceived as a repeated cycle of inquiry also points to limits on our expectations for an abductive logic. My chief point is that the character of inference to an explanation, viewed below as embedded within arguments from analogy, is so little compelling, as a matter of logical form alone, that there will always be a pluralism of plausible alternatives among untested hypotheses and inferences to them--calling for some comparative evaluation. This point leads on to some consideration of the virtues of hypotheses--as a description of the range of this pluralism. (shrink)