Project: AI-Powered Robo-Uber System – Coursework Implementation Objective Develop an intelligent taxi dispatch and routing system using AI techniques, including path planning, constraint satisfaction, probabilistic reasoning, and multi-agent coordination. Key Tasks & Deliverables 1. Path Planning Improvement (20%) 1a: Analyze the baseline system over multiple simulated days. Evaluate metrics like: Average revenue per taxi and dispatcher Total joint revenue Number of active taxis over time 1b: Implement an optimized _planPath function for taxis using a suitable pathfinding algorithm (e.g., A*, Dijkstra). Justify the choice and compare results with the baseline. 1c (Optional): Develop a probabilistic path planner that accounts for traffic and estimates journey time. Analyze impact on fare cancellations. 2. Dispatcher Optimization (10%) Modify _allocateFare to maximize total daily returns for both taxis and dispatcher. Consider: Time to pickup Number of bids Fairness in allocation Compare performance before and after changes. 3. Bidding & Probabilistic Reasoning (20%) 3a: Improve _bidOnFare to maximize taxi ROI. Test in both: Deterministic (no traffic) Probabilistic (with traffic) environments Run at least 3 trials and analyze results. 3b: Write a brief evaluation of the system’s commercial viability and identify areas needing real-world testing. 3c (Optional): Enhance _costFare to minimize fare cancellations using probabilistic reasoning. Technical Stack & Constraints Language: Python (presumably, based on skeleton code) Libraries: Only numpy and pygame allowed. No pre-built AI libraries (e.g., scikit-learn, TensorFlow, GPT-generated code). Environment: Grid-based world with roads, traffic, intersections, and dynamic fares. Agents: Taxis and a central dispatcher with bidding and allocation logic. Deliverables Fully documented and functional source code with regular Git commits. A 3000-word report including: Implementation rationale Algorithm justifications Performance analysis (before/after improvements) Commercial evaluation (for Task 3b) Declaration of AI use (if any) in the report. Ideal Candidate Should Have Strong background in AI search algorithms, path planning, and multi-agent systems. Experience with probabilistic reasoning and constraint satisfaction. Ability to write clean, documented code and conduct systematic performance analysis. Understanding of commercial AI system deployment is a plus.