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Scope & Description

Knowledge representation is a lively and well-established field of AI, where knowledge and belief is represented declaratively and suitable for machine processing. It is often claimed that this declarative nature makes knowledge representation cognitively more adequate than e.g. sub-symbolic approaches, such as machine learning. This cognitive adequacy has important ramifications for the explainability of approaches in knowledge representation, which on its turn is essential for the trustworthiness of these approaches. However, exactly how cognitive adequacy is ensured has been often left implicit, and connections with cognitive science and psychology have only recently been taken up.

The goal of this workshop is to bring together experts from fields including artificial intelligence, psychology, cognitive science and philosophy to discuss important questions related to cognitive aspects of knowledge representation, such as:

IJCAI 22 Workshop

This workshop is part of the IJCAI 22 workshop programme.

Important Dates

Keynote Speaker

Ruth Byrne (Professor of Cognitive Science, Trinity College Dublin) University of Dublin will deliver the following keynote talk at the CAKR workshop:

Knowledge representation: Evidence from the cognitive science of counterfactual reasoning [slides]. Abstract: I discuss several discoveries about the cognitive processes that underlie human knowledge representation that are relevant to Artificial Intelligence models. I rely on cognitive science research on counterfactual reasoning to illustrate several key points. First, I outline evidence that people construct iconic mental simulations of possibilities, they compare multiple such representations, and in doing so, they must track the epistemic status of representations, a requirement currently neglected in many models. Next, I describe evidence that the representations people construct are iconic but nonetheless can include symbolic information when necessary. Moreover, people combine knowledge of different sorts in their mental representations, and experimental evidence indicates that they prioritize knowledge of background conditions above knowledge of presuppositions. Finally, I consider the implications of the findings that people simulate counterfactual possibilities to verify facts, and represent impossibilities as if they were possible. I suggest that these recent discoveries in understanding knowledge representation in human counterfactual reasoning provide a rich source for future developments in Artificial Intelligence.

Schedule (July 23)

09:30-10:30 Keynote: Ruth M.J. Byrne: Knowledge representation: Evidence from the cognitive science of counterfactual reasoning.
10:30-11:00 Coffee break
11:00-11:30 Greta Warren, Mark T. Keane and Ruth M.J. Byrne Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAI.
11:30-12:00 Claudia Schon Associative Reasoning for Commonsense Knowledge.
12:00-12:30 Meghna Bhadra and Steffen Hölldobler Identification of Noise Variables in Singular Decisions using Counterfactual Reasoning.
12:30-14:00 Lunch break
14:00-14:30 Stefania Costantini, Andrea Formisano and Valentina Pitoni Cognitive Aspects in Epistemic Logic L-DINF (remote talk).
14:30-15:00 Mohan Sridharan Cognitive Adequacy: Insights from Developing Robot Architectures (remote talk).
15:00-15:30 Coffee break
15:30-16:00 Animesh Nighojkar, Anna Khlyzova and John Licato Cognitive Modeling of Semantic Fluency Using Transformers.
16:00-16:30 Jorge Fernandez Davila, Dominique Longin, Emiliano Lorini and Frédéric Maris A Simple Framework for Cognitive Planning
16:30-17:00 Umberto Grandi, Emiliano Lorini, Timothy Parker and Rachid Alami Logic-Based Ethical Planning
17:00-17:20 Michael Giancola, Selmer Bringsjord and Naveen Sundar Govindarajulu Novel Intensional Defeasible Reasoning for AI: Is it Cognitively Adequate? (remote talk)

Programme Committee

Organizing Committee