Write Algorithm to Decision-making for a wumpus hunter using influence maps. Wumpus-Hunter-React(v) in: current room v ∈ V out: action a ∈ {shoot, move}, u ∈ neighbourhood(v) constant: threshold ts for shooting an arrow local: influence maps bats, pit, wumpus, and visited (initially ∀ u ∈ V : bats(u) = pit(u) = wumpus(u) = visited(u) = 1)

Write Algorithm to Decision-making for a wumpus hunter using influence maps. Wumpus-Hunter-React(v) in: current room v ∈ V out: action a ∈ {shoot, move}, u ∈ neighbourhood(v) constant: threshold ts for shooting an arrow local: influence maps bats, pit, wumpus, and visited (initially ∀ u ∈ V : bats(u) = pit(u) = wumpus(u) = visited(u) = 1)

Operations Research : Applications and Algorithms

4th Edition

ISBN:9780534380588

Author:Wayne L. Winston

Publisher:Wayne L. Winston

Chapter14: Game Theory

Section: Chapter Questions

Problem 7RP

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Write Algorithm to Decision-making for a wumpus hunter using influence maps.Wumpus-Hunter-React(v)in: current room v ∈ Vout: action a ∈ {shoot, move}, u ∈ neighbourhood(v)constant: threshold ts for shooting an arrowlocal: influence maps bats, pit, wumpus, and visited (initially∀ u ∈ V : bats(u) = pit(u) = wumpus(u) = visited(u) = 1)
Algorithm for Decision-making for a wumpus hunter using influence maps.Wumpus-Hunter-React(v)in: current room v ∈ Vout: action a ∈ {shoot, move}, u ∈ neighbourhood(v)constant: threshold ts for shooting an arrowlocal: influence maps bats, pit, wumpus, and visited (initially∀ u ∈ V : bats(u) = pit(u) = wumpus(u) = visited(u) = 1)
make Algorithm to Decision-making for a wumpus hunter using influence maps.in: current room v ∈ Vout: action a ∈ {shoot, move}, u ∈ neighbourhood(v)constant: threshold ts for shooting an arrowlocal: influence maps bats, pit, wumpus, and visited (initially∀ u ∈ V : bats(u) = pit(u) = wumpus(u) = visited(u) = 1)
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Consider the following equation that defines the conditions under which a square is breezy in the Wumpus World: ∀x,y Breezy(x, y) ⇔ ∃u,v Adjacent(u, v, x, y)∧Pit(u, v). Here we consider two other ways to describe this aspect of the Wumpus World. We can write diagnostic rulesleading from observed effects to hidden causes. For finding pits, the obvious diagnostic rules say that if a square is breezy, some adjacent square must contain a pit; and if a square is not breezy, then no adjacent square contains a pit. Write these two rules in first-order logic and show that their conjunction is logically equivalent to equation above. We can write causal rulesleading from cause to effect. One obvious causal rule is that a pit causes all adjacent squares to be breezy. Write this rule in first-order logic, explain why it is incomplete compared to equation above, and supply the missing axiom.
Assume we have three binary input attributes, A, B, C, and one binary outcome attribute Y. The three input attributes, A, B, C take values in the set {0,1} while the Y attribute takes values in the set {True, False} a. Which attribute (A, B, or C) has the highest information gain? b. Construct the full decision tree for this problem, without doing any pruning. If at any point you have a choice between splitting on two equally desirable attributes, choose the one that comes first alphabetically. If there is a tie on how to label a leaf, then choose True.(NEED ONLY HANDWRITTEN SOLUTIION)