strat-gameplay-webapp/backend/app/core/ai_opponent.py

"""
AI Opponent - Automated decision-making for AI-controlled teams.

Provides strategic decision generation for AI opponents in single-player
and AI vs AI game modes.

This is a stub implementation for Week 7. Full AI logic will be developed
in Week 9 as part of Phase 3 AI opponent integration.

Author: Claude
Date: 2025-10-29
"""

import logging
from typing import Optional

from app.models.game_models import GameState, DefensiveDecision, OffensiveDecision

logger = logging.getLogger(f'{__name__}.AIOpponent')


class AIOpponent:
    """
    AI opponent decision-making engine.

    Generates defensive and offensive decisions for AI-controlled teams.
    Current implementation uses simple heuristics. Full AI logic will be
    implemented in Week 9.
    """

    def __init__(self, difficulty: str = "balanced"):
        """
        Initialize AI opponent.

        Args:
            difficulty: AI difficulty level
                - "balanced": Standard decision-making
                - "yolo": Aggressive playstyle (more risks)
                - "safe": Conservative playstyle (fewer risks)
        """
        self.difficulty = difficulty
        logger.info(f"AIOpponent initialized with difficulty: {difficulty}")

    async def generate_defensive_decision(
        self,
        state: GameState
    ) -> DefensiveDecision:
        """
        Generate defensive decision for AI-controlled fielding team.

        Week 7 stub: Returns default "normal" positioning.
        Week 9: Implement full decision logic based on game situation.

        Args:
            state: Current game state

        Returns:
            DefensiveDecision with AI-generated strategy

        TODO (Week 9):
        - Analyze batter tendencies (pull/opposite field)
        - Consider runner speed for hold decisions
        - Evaluate double play opportunities
        - Adjust positioning based on inning/score
        """
        logger.debug(f"Generating defensive decision for game {state.game_id}")

        # Week 7 stub: Simple default decision
        decision = DefensiveDecision(
            alignment="normal",
            infield_depth="normal",
            outfield_depth="normal",
            hold_runners=[]
        )

        # TODO Week 9: Add actual AI logic
        # if state.outs < 2 and state.is_runner_on_first():
        #     decision.infield_depth = "double_play"
        # if state.is_runner_on_third() and state.outs < 2:
        #     decision.infield_depth = "in"

        logger.info(f"AI defensive decision: IF: {decision.infield_depth}, OF: {decision.outfield_depth}")
        return decision

    async def generate_offensive_decision(
        self,
        state: GameState
    ) -> OffensiveDecision:
        """
        Generate offensive decision for AI-controlled batting team.

        Week 7 stub: Returns default "normal" approach.
        Week 9: Implement full decision logic based on game situation.

        Args:
            state: Current game state

        Returns:
            OffensiveDecision with AI-generated strategy

        TODO (Week 9):
        - Evaluate stealing opportunities (runner speed, pitcher, catcher)
        - Consider bunting in appropriate situations
        - Adjust batting approach based on score/inning
        - Implement hit-and-run logic
        """
        logger.debug(f"Generating offensive decision for game {state.game_id}")

        # Week 7 stub: Simple default decision
        decision = OffensiveDecision(
            approach="normal",
            steal_attempts=[],
            hit_and_run=False,
            bunt_attempt=False
        )

        # TODO Week 9: Add actual AI logic
        # if state.is_runner_on_first() and not state.is_runner_on_second():
        #     # Consider stealing second
        #     if self._should_attempt_steal(state):
        #         decision.steal_attempts = [2]

        logger.info(f"AI offensive decision: steal={decision.steal_attempts}, hr={decision.hit_and_run}")
        return decision

    def _should_attempt_steal(self, state: GameState) -> bool:
        """
        Determine if AI should attempt a steal (Week 9).

        TODO: Implement steal decision logic
        - Evaluate runner speed
        - Evaluate pitcher hold rating
        - Evaluate catcher arm
        - Consider game situation (score, inning, outs)

        Returns:
            True if steal should be attempted
        """
        # Week 7 stub: Never steal
        return False

    def _should_attempt_bunt(self, state: GameState) -> bool:
        """
        Determine if AI should attempt a bunt (Week 9).

        TODO: Implement bunt decision logic
        - Consider sacrifice bunt situations
        - Evaluate batter bunting ability
        - Assess defensive positioning

        Returns:
            True if bunt should be attempted
        """
        # Week 7 stub: Never bunt
        return False


# Singleton instance for global access
ai_opponent = AIOpponent()