Artificial Intelligence : Unit - 2 Part - 16 : Evaluation Functions

 

UNIT - II

 Evaluation Functions

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Part A: Introduction

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What is an Evaluation Function?

An Evaluation Function (also called a Heuristic Evaluation Function) is used to estimate the value of a game position or state when it is not possible to search until the end of the game.

It gives a numerical score that helps an AI agent decide how favorable a game state is for the MAX (AI) player.

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Why Do We Need Evaluation Functions?

• In many games (like Chess), it’s impossible to explore the full game tree due to time or memory limits.
• So instead of reaching terminal states (win/lose), AI uses an evaluation function to judge non-terminal states.

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Part B: Characteristics of a Good Evaluation Function

Characteristic	Description
Speed	Must be fast to compute (for real-time use)
Accuracy	Must give values that truly reflect the chances of winning
Consistency	If state A is better than state B, evaluation(A) > evaluation(B)
Scalability	Should work even as the game becomes more complex
Domain-specific	Must be designed to suit the particular game being played

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Part C: Working of Evaluation Function

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• AI explores the game tree up to a certain depth limit (due to time/memory constraints).
• Instead of waiting to reach the end of the game, the evaluation function is applied at that depth.
• The function returns a score:
• High score = good for MAX (AI)
• Low score = good for MIN (opponent)

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Real-Life Example: Chess Evaluation Function

A simple evaluation function in Chess may be:

f(state) = (Sum of white piece values) - (Sum of black piece values)

Piece	Value
Pawn	1
Knight	3
Bishop	3
Rook	5
Queen	9
King	Infinite (but usually not used in evaluation)

So if white has a Queen and a Rook (9 + 5 = 14), and black has two rooks (5 + 5 = 10),
then the evaluation function = 14 – 10 = +4 → advantage to white (MAX).

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Part D: Designing an Evaluation Function

When building an evaluation function for a game, consider:

Factor	Purpose
Material count	Total value of pieces or assets
Mobility	Number of legal moves available
Control of board/area	Dominating important zones
Threats or risks	Are any pieces or units in danger?
Progress toward goal	How close are we to winning?

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Example: Tic-Tac-Toe Evaluation Function

• +10 if AI is winning
• -10 if opponent is winning
• 0 for a draw or neutral position

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Part E: Role in Game Playing Algorithms

Algorithm	How Evaluation Function Helps
Minimax	Helps decide best move when full game tree isn’t explored
Alpha-Beta Pruning	Evaluates leaf nodes during pruning
Monte Carlo Tree Search	Helps simulate outcomes and score states

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Part F: Advantages and Disadvantages

Advantages	Disadvantages
✅ Speeds up decision making	❌ Not always accurate (it's just an estimate)
✅ Makes AI playable in complex games	❌ Must be carefully tuned for each game
✅ Allows depth-limited search strategies	❌ Can lead to bad decisions if poorly designed

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✅ Common Use Cases

Game	Evaluation Factors
Chess	Piece values, board control, king safety
Tic-Tac-Toe	Number of winning lines open to player
Checkers	Piece count, number of kings, corner occupation
Connect Four	Number of connected discs, threats to win

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Summary

• An Evaluation Function gives a numeric score to a non-terminal game state to help AI decide its next move.
• It is used when the full game tree cannot be explored due to time or memory limitations.
• A well-designed evaluation function must be fast, accurate, and domain-specific.
• It plays a crucial role in game-playing AI using Minimax, Alpha-Beta Pruning, and other search techniques.

"When you can’t see the finish line, a good evaluation function tells you which path looks most promising."