CSPs - Caltech

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Summary: Par)al observability ! Can reduce any par)ally observable problem to fully observable problem on belief states ! Belief state = set of states the environment could be in ! Use exis)ng algorithms on belief states ! Sensor-‐less case: Find opt. sequence using IDS, A*, etc. ! Observa)ons: Develop condi)onal plans using AND-‐OR search ! Games: Use α-‐β pruning etc. on belief states ! # belief states exponen)al in # states… ! Need concise way to summarize the states we could be in logic! (coming up soon) 18
Page 1 and 2: Introduc)on to Ar)ficial Intellig
Page 3 and 4: Games vs. search ! In games, ac
Page 5: α-‐β-‐pruning ! Key idea
Page 8 and 9: AND-‐OR trees 8
Page 10 and 11: Par)al observability ! Suppose ou
Page 12 and 13: Noisy ac)ons (slippery vacuum w
Page 14 and 15: Planning in belief space ! Belie
Page 17: AND-‐OR trees with noisy ob
Page 21 and 22: Example: Map coloring ! Variables
Page 23 and 24: Example: 8 queens 23
Page 25 and 26: ! Discrete variables ! Finite dom
Page 27 and 28: Example: higher-‐order constr
Page 29 and 30: Real-‐world examples of CSPs
Page 31 and 32: Backtracking search ! Variable as
Page 33 and 34: Backtracking example 33
Page 35 and 36: Backtracking example 35

CSPs - Caltech

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