Current AI models formalize planning as a search in a decision tree of potential actions and outcomes. The size of this tree determines the computational cost of the problem, or its theoretical difficulty. However, theoretical difficulty rarely aligns with human experience, as people often easily solve problems deemed intractable in theory. The key characteristic of real-world problems that people are so adept at is their compositional, semi-regular structure, such as predictable patterns of hills and valleys in nature, or structured street networks in built environments. Computational study of how people plan in such contexts is central to engineering resource-efficient AI that can plan with human-like efficiency, while adapting to increasing problem scales. In this project, we will integrate AI, Large Language Models, and psychology in new ways to develop a computational understanding of how people conceptualize maps and plan in realistic spatial navigation tasks. We will build computational models that can anticipate environmental structure and create human-like AI that can produce efficient plans in realistic domains.

Accepting: PhD students

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Dalhousie's Faculty of Computer Science offers competitive funding to qualified graduate students and is committed to promoting excellence in research and teaching.