Welcome to the 1st International Workshop on HYbrid models for coupling Deductive and inductive ReAsoning (HYDRA).
The workshop is co-located with the 16th International Conference on Logic Programming and Non-monotonic Reasoning (LPNMR 2022).
In the last decades, deductive reasoning has been widely used in several fields such as planning, scheduling problems, robotics controls applications, and many others thanks to its ability in developing specific conclusions based on valid evidence or facts, leading to a wide range of real-world and industrial applications.
Nevertheless, although deductive reasoning enhances a deeper analysis of a context, it requires consistent premises and a proper knowledge base to make the right inferences; on the other hand, inductive reasoning extracts a (possibly) generalized conclusion from limited and specific observations. In particular, recent advancements in inductive reasoning, such as Machine and Deep Learning, have proved to be greatly promising in recognizing meaningful patterns and connections from several observations (i.e., huge amounts of data). However, such approaches suffer from the lack of proper means for interpreting the model's choices and for driving the decisions according to prior knowledge.
Considering that neither deductive nor inductive methods cannot be considered the ultimate, comprehensive solutions to Artificial Intelligence, novel approaches combining and intertwining such methods advantageously allow to take advantage of the peculiarities and strengths of the two methods.
Motivations and Main Goals
The HYDRA workshop aims at bringing together the scientific community, and welcomes both theoretical and practical papers on frameworks, applications, and methods for integrating and combining deductive and inductive systems in different scenarios, to any extent. The workshop also welcomes summaries of recently published papers, as well as work-in-progress contributions.
Keywords: Logic programming, Deductive Reasoning, Inductive Reasoning, Hybrid reasoning models, Deep Learning, Machine Learning