AI4 Search in Complex AI Environments
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AI4 Search in Complex AI Environments
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This chapter expands upon search algorithms by addressing more complex, real-world environments that relax simplifying assumptions. It introduces local search and optimization problems, where the focus is on finding a good final state rather than the path, and discusses techniques like hill climbing and simulated annealing. The text then progresses to search with nondeterministic actions, where agents need to formulate conditional plans due to unpredictable outcomes, utilizing AND-OR search trees. Finally, the chapter explores search in partially observable and unknown environments, introducing the concept of belief states and the challenges of online search agents that learn about the environment as they interact with it, including methods like LRTA* for efficient exploration and adaptation.