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Initial state: The monkey, the suspended bananas, and two cratesin the room Goal test: Monkey has bananas. Successor function: To jump on crate; to jump off crate; Push crate from One spot to another; Walk from one spot to another;Grab bananas (if standing on crate). Cost function: Number of ...


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It's not possible to solve version 1) of the problem in general. To see why, consider a graph with 2 cities, and 2 agents, where the agents start in opposite nodes. Since both agents need to move every turn, they will never meet in the same city. For version 2), I'm going to make some assumptions that aren't completely clear from your text: Agents have ...


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Consistency is a property of heuristics. You can think of consistency as the common sense idea that our guess at the time to go from $A \rightarrow B \rightarrow C$ cannot be more than the time to go from $A \rightarrow B$, plus our guess of the time to go from $B \rightarrow C$. Supposing we remove a given edge $c(n,m)$ from our graph, but that our ...


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This is probably more easily understood as the collapse/restore macro. The idea is that the previously explored state was collapsed and only the minimum f-cost from the sub-tree was stored. This represents the best unexpanded state in the subtree that was collapsed. When restoring the portion of the collapsed tree, the f-cost of the restored node could ...


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