4,097 research outputs found
Deep Analogical Inference as the Origin of Hypotheses
The ability to generate novel hypotheses is an important problem-solving capacity of humans. This ability is vital for making sense of the complex and unfamiliar world we live in. Often, this capacity is characterized as an inference to the best explanationâselecting the âbestâ explanation from a given set of candidate hypotheses. However, it remains unclear where these candidate hypotheses originate from. In this paper we contribute to computationally explaining these origins by providing the contours of the computational problem solved when humans generate hypotheses. The origin of hypotheses, otherwise known as abduction proper, is hallmarked by seven properties: (1) isotropy, (2) open-endedness, (3) novelty, (4) groundedness, (5) sensibility, (6) psychological realism, and (7) computational tractability. In this paper we provide a computational-level theory of abduction proper that unifies the first six of these properties and lays the groundwork for the seventh property of computational tractability. We conjecture that abduction proper is best seen as a process of deep analogical inference
Space exploration: The interstellar goal and Titan demonstration
Automated interstellar space exploration is reviewed. The Titan demonstration mission is discussed. Remote sensing and automated modeling are considered. Nuclear electric propulsion, main orbiting spacecraft, lander/rover, subsatellites, atmospheric probes, powered air vehicles, and a surface science network comprise mission component concepts. Machine, intelligence in space exploration is discussed
The role of abduction in production of new ideas in design
The pragmatist philosopher Peirce insisted that besides deduction and induction there is a third main form of inference, abduction, which is the only type of inference capable of producing new ideas. Also he defined abduction as a stage of the methodological process in science, where hypotheses are formed to explain anomalies. Basing on these seminal ideas, scholars have proposed modified, widened or alternative definitions of abduction and devised taxonomies of abductive inferences. Influenced by Peirceâs seminal writings and subsequent treatments on abduction in philosophy of science, design scholars have in the last 40 years endeavoured to shed light on design by means of the concept of abduction. The first treatment was provided by March in 1976. He viewed that abduction, which he called âproductive reasoningâ, is the key mode of reasoning in design. He also presented a three-step cyclic design process, similar to Peirceâs methodological process in science. Among the many other later treatments of design abduction, Roozenburgâs definition of explanatory and innovative abduction is noteworthy. However, an evaluation of the related literature suggests that research into abduction in design is still in an undeveloped stage. This research shows gaps in coverage, lack of depth and diverging outcomes. By focusing on the differences between science and design as well as on empirical knowledge of different phenomena comprising design, new conceptions of abduction in design are derived. Given the differences of context, abduction in design shows characteristics not yet found or identified in science. For example, abduction can occur in connection to practically all inference types in design; it is a property of an inference besides an inference itself. A number of the most important abductive inference types as they occur in design are identified and discussed in more detail.Peer reviewe
A theory of relation learning and cross-domain generalization
People readily generalize knowledge to novel domains and stimuli. We present
a theory, instantiated in a computational model, based on the idea that
cross-domain generalization in humans is a case of analogical inference over
structured (i.e., symbolic) relational representations. The model is an
extension of the LISA and DORA models of relational inference and learning. The
resulting model learns both the content and format (i.e., structure) of
relational representations from non-relational inputs without supervision, when
augmented with the capacity for reinforcement learning, leverages these
representations to learn individual domains, and then generalizes to new
domains on the first exposure (i.e., zero-shot learning) via analogical
inference. We demonstrate the capacity of the model to learn structured
relational representations from a variety of simple visual stimuli, and to
perform cross-domain generalization between video games (Breakout and Pong) and
between several psychological tasks. We demonstrate that the model's trajectory
closely mirrors the trajectory of children as they learn about relations,
accounting for phenomena from the literature on the development of children's
reasoning and analogy making. The model's ability to generalize between domains
demonstrates the flexibility afforded by representing domains in terms of their
underlying relational structure, rather than simply in terms of the statistical
relations between their inputs and outputs.Comment: Includes supplemental materia
Scientific discovery reloaded
The way scientific discovery has been conceptualized has changed drastically in the last few decades: its relation to logic, inference, methods, and evolution has been deeply reloaded. The âphilosophical matrixâ moulded by logical empiricism and analytical tradition has been challenged by the âfriends of discoveryâ, who opened up the way to a rational investigation of discovery. This has produced not only new theories of discovery (like the deductive, cognitive, and evolutionary), but also new ways of practicing it in a rational and more systematic way. Ampliative rules, methods, heuristic procedures and even a logic of discovery have been investigated, extracted, reconstructed and refined. The outcome is a âscientific discovery revolutionâ: not only a new way of looking at discovery, but also a construction of tools that can guide us to discover something new. This is a very important contribution of philosophy of science to science, as it puts the former in a position not only to interpret what scientists do, but also to provide and improve tools that they can employ in their activity
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