Enhance chefBot decision-making intelligence

[kevinmaes]

Overview

The chefBot currently has a solid scoring system for choosing which eggs to catch, but there are several opportunities to make it significantly smarter about decision-making and catch optimization.

Current Implementation

The bot uses findBestEgg() in tests/machines/helpers.ts with multi-factor scoring:

• Base value scoring (gold=5pts, white=1pt, black=-10pts)
• Distance and time-to-catch calculations
• Black egg danger detection and path blocking
• Cluster bonuses for sequential eggs
• Direction change penalties

Sub-Issues (Implementation Roadmap)

Phase 1: Quick Wins

• Implement predictive trajectory analysis for chefBot #181 - Predictive trajectory analysis ⭐ (High Impact)
• Implement risk-adjusted value scoring for chefBot #184 - Risk-adjusted value scoring

Phase 2: Advanced Optimizations

• Add momentum-aware movement optimization for chefBot #183 - Momentum-aware movement optimization
• Implement dynamic safety margins for chefBot #186 - Dynamic safety margins

Phase 3: Complex Features

• Implement multi-egg sequence planning for chefBot #182 - Multi-egg sequence planning ⭐⭐ (Very High Impact)
• Add edge-case optimization for chefBot #185 - Edge-case optimization

Files to Modify

• tests/machines/helpers.ts - Core scoring and decision logic
• tests/machines/chefBot.machine.ts - Bot state machine integration

Success Metrics

• Higher average scores in automated test runs
• Fewer missed catchable eggs
• Fewer black egg collisions
• Better performance with multiple simultaneous eggs