Overview
The objective of this workshop is to bridge the gap between human cognitive science and artificial intelligence by bringing together researchers working on computational models of human cognition, neurosymbolic AI, human-AI interaction, and cognitively-inspired machine learning. Recent advances in AI have demonstrated remarkable capabilities, yet these systems often lack the interpretability, causal reasoning, and generalization abilities that characterize human intelligence. Meanwhile, cognitive science has made significant progress in understanding human reasoning, learning, and decision-making processes.
We believe that incorporating insights from human cognition into AI systems can lead to more robust, interpretable, and human-aligned artificial intelligence. This workshop aims to facilitate cross-pollination of ideas between cognitive scientists, neuroscientists, and AI researchers to develop the next generation of AI systems that can reason more like humans while maintaining computational efficiency.
The workshop will explore how explicit models of human knowledge, cognitive capabilities, and mental states can be integrated into AI reasoning processes. We will examine approaches that combine neural and symbolic methods inspired by human cognition, incorporate human causal reasoning patterns, and leverage human teaching signals to create more interpretable and aligned AI systems.
The workshop will focus on research related to all aspects of human cognition and AI reasoning. This topic features technical problems that are of interest across multiple fields including cognitive science, machine learning, AI planning, human-robot interaction, and neurosymbolic AI. We welcome submissions that address formal as well as empirical issues on topics such as:
- Explicit modeling of human knowledge and cognitive capabilities.
- Introducing explicit human models into the reasoning process.
- How AI can model and reason about human mental states and intentions.
- Combining neural and symbolic methods inspired by human cognition.
- Incorporating human causal reasoning patterns into AI systems.
- Using human cognitive models to make AI systems more interpretable.
- Incorporating human teaching and correction into learning processes.
- Structured approaches to human-inspired AI reasoning.
- Probabilistic approaches to human-like reasoning.
Invited Speakers
Organizing Committee
Julie A Shah
Massachusetts Institute of Technology, USA
Sarath Sreedharan
Colorado State University, USA
Silvia Tulli
Sorbonne University, France