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Explain heuristic and cost function using Informed Serach/Blind search to reach goal for below problem, use Python for coding. Assume a white queen on a
Explain heuristic and cost function using Informed Serach/Blind search to reach goal for below problem, use Python for coding. "Assume a white queen on a chessboard at a specified location. There are other black and white kings, pawn, rook and a black queen in different locations. The problem is leading the white queen through the chess grid safely so that she reaches her white king. The black chess coins attack the white queen while it is safe to pass near the white coins. Help the white queen find her way through the chess grid to reach her king. Assume the only the white queen moves and other coins remain in their positions. The following figure gives the initial board position. One point detected when white queen comes near black coins and one point added when it comes near white coins. It is alright to get negative points provided the total points do not become negative. The desired solution must have maximum cost but have travelled through less number of squares. The environment is fully observable. i 1 1 1 1 Explain heuristic and cost function using Informed Serach/Blind search to reach goal for below problem, use Python for coding. "Assume a white queen on a chessboard at a specified location. There are other black and white kings, pawn, rook and a black queen in different locations. The problem is leading the white queen through the chess grid safely so that she reaches her white king. The black chess coins attack the white queen while it is safe to pass near the white coins. Help the white queen find her way through the chess grid to reach her king. Assume the only the white queen moves and other coins remain in their positions. The following figure gives the initial board position. One point detected when white queen comes near black coins and one point added when it comes near white coins. It is alright to get negative points provided the total points do not become negative. The desired solution must have maximum cost but have travelled through less number of squares. The environment is fully observable. i 1 1 1 1
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