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Description: I need an experienced AI developer to implement classical AI algorithms for a multi-agent taxi dispatch system. This project requiring strong expertise in pathfinding, search algorithms, and probabilistic reasoning - NOT LLMs or chatbots. Core Requirements: Implement A* or Dijkstra algorithms for optimized path planning Develop multi-agent coordination and bidding systems Create probabilistic reasoning for traffic and fare allocation Optimize dispatcher logic for revenue maximization Conduct performance analysis with before/after metrics Technical Stack: Python (primary language) NumPy & PyGame only (NO other AI libraries permitted) Custom simulation environment (provided) Git for version control Key Deliverables: Enhanced _planPath function with optimized pathfinding Improved _allocateFare for dispatcher optimization Advanced _bidOnFare with probabilistic reasoning 500 word technical report with analysis Clean, documented code with regular commits Must-Have Experience: Classical AI algorithms (pathfinding, search, constraint satisfaction) Multi-agent systems coordination Probabilistic reasoning and optimization Academic or research projects in AI Python algorithmic development Nice-to-Have: Experience with simulation environments Academic writing/reporting skills Traffic modeling or routing systems Screening Question: "Describe a specific project where you implemented A or Dijkstra algorithm, and what optimization challenges you faced."* What We DON'T Need: Web/mobile developers LLM/chatbot specialists Data scientists without algorithmic background Full-stack developers focusing on UI/UX Application Instructions: Please include: Specific examples of pathfinding/search algorithm projects Your experience with multi-agent systems Availability for weekly progress updates (3 week project) Your approach to the three main tasks above