Anytime heuristic search, including Weighted A* and ARA*, lets agents commit to a suboptimal plan immediately and refine it as time allows, with provable bounds on solution quality at every step.
How STRIPS formalizes agent planning problems and how the delete relaxation trick produces powerful heuristics that guide search toward goals efficiently
How agents learn faster and more robustly by training on the right task at the right time — from hand-crafted curricula to adversarial environment generation
How mean field theory lets you solve game-theoretic problems with millions of agents by replacing individual interactions with a statistical summary of the crowd
Learn how cooperative game theory and Shapley values provide a mathematically principled way to assign credit among collaborating agents, with practical Python implementations and connections to modern LLM agent teams.
How to specify complex, multi-step tasks for AI agents using finite-state automata called reward machines, enabling non-Markovian rewards and compositional task structure
