Tutorial on Cognitive Logics:
Formal and Cognitive Methods for Reasoning in a Dynamic World

Systems and methods for Artificial Intelligence (AI) applicable in the real world require to represent and reason about uncertain knowledge. While this is a limitation of classical first-order logic, there is a large number of so-called non-monotonic logics, i.e., logics that aim to draw inferences only cautiously, allowing for revising them if new information becomes available. Cognitive analysis have shown that human inferential behavior can be better described employing such logics. In this tutorial we introduce the cognitive and formal foundations of cognitive logics, relevant benchmark problems, and challenges in modeling cognitive reasoning. The topics of the tutorial will be as follows:

• What is the right logic for human reasoning in AI? - Classical fallacies
• Cognitive perspective
• Formal models of commonsense reasoning
  • Reiter's default logic
  • Logic programming and weak completion semantics
  • Conditionals and ranking functions: system Z and c-representations
• Cognitive aspects of Cognitive Logics
  • Cognitive theories and principles, psychological benchmark examples
  • Cognitive modeling of human reasoning, especially of conditional and syllogistic reasoning
  • Logical incorrectness of human reasoning and how to escape from irrationality
  • The role of background knowledge in psychological experiments
• From nonmonotonic reasoning to belief revision
• Discussion

Download the slides (Tutorial from the KR Conventicle 2019 at the PRICAI 2019): normal or 4on1