Workshop: Bridging the Gap: Is Logic and Automated Reasoning a Foundation for Human Reasoning?

Reasoning is a core ability in human cognition. Its power lies in the ability to theorize about the environment, to make implicit knowledge explicit, to generalize given knowledge and to gain new insights. It is a well researched topic in cognitive psychology and cognitive science and over the past decade impressive results have been achieved. Early researchers starting with Störing (1908) often used propositional logic as a normative framework. Any deviation from it has been considered an error. Central results like findings from the Wason selection task (Wason, 1968) or the suppression task (Byrne, 1989) inspired a shift from propositional logic and the assumption of monotonicity in human reasoning towards other reasoning approaches. This includes but is not limited to models using probabilistic approaches (Oaksford & Chater, 2007), mental models (Johnson-Laird, 2006), or nonmonotonic logics (Stenning & Lambalgen, 2008). Considering cognitive theories for syllogistic reasoning show that none of the existing theories is close to the existing data (Khemlani & Johnson-Laird, 2012). But some formally inspired cognitive complexity measures can predict human reasoning difficulty for instance in spatial relational reasoning (Ragni & Knauff, 2013). Automated deduction, on the other hand, is mainly focusing on the automated proof search in logical calculi. And indeed there is tremendous success during the last decades. Recently a coupling of the areas of cognitive science and automated reasoning is addressed in several approaches. For example there is increasing interest in modeling human reasoning within automated reasoning systems including modeling with answer set programming, deontic logic or abductive logic programming (Dietz & Hölldobler, 2015; Dietz, Hölldobler, & Wernhard, 2014). There are also various approaches within AI research for common sense reasoning (Furbach & Schon, 2014, 2016). Despite a common research interest – reasoning – there are still several milestones necessary to foster a better interdisciplinary research. First, to develop a better understanding of methods, techniques, and approaches applied in both research fields. Second, to have a synopsis of the relevant state-of-the-art in both research directions. Third, to combine methods and techniques from both fields and find synergies. E.g., techniques and methods from computational logic have never been directly applied to model adequately human reasoning. They have always been adapted and changed. Fourth, we need more and better experimental data that can be used as a benchmark system. Fifth, cognitive theories can benefit from a computational modeling. Hence, both fields – human and automated reasoning – can both contribute to these milestones and are in fact a conditio sine qua non. Achievements in both fields can inform the others. Deviations between fields can inspire to seek a new and profound understanding of the nature of reasoning. This is the third workshop in a series of successful Bridging the Gap workshop1 located at previous conferences: 2015 at the International Conference on Automated Deduction in Berlin (CADE-25) focused on the automated reasoning aspects. 2016 at the International Conference on Artificial Intelligence in New York (IJCAI 2016) included an AI perspective. The Annual Meeting of the Cognitive Science Society is the central place for bringing a strong human centric perspective into discussion.