Abstract
The classic way to solve any problem of artificial intelligence is to model it like a graph, which assumes that the environment is static, fully observable, deterministic, discrete, single agent, and sequential in decision-making. Most assumptions hold if the problem is solved at a coarser level of abstraction, while plan correction/control algorithms at a finer level handle the modelling errors. This chapter introduces the principles of searching for an optimal path from a source to a goal in a graph using breadth-first search, depth-first search, uniform cost search, and other variants. Heuristics are added to make an optimally efficient A* algorithm, where a heuristic function gives an estimated cost to the goal. The A* algorithm is given an optimality relaxation by a factor of ɛ for a speedup, making the ɛ-A* algorithm. Adversarial planning or game playing deals with planning in the presence of an adversary by using game trees.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
