This project implements agent-based navigation using graph-theoretic and information-theoretic measures. Inspired by Marcus Clements' research on navigation in urban environments, the agent simulates decision-making in graph networks through two methods:
- Random Walks
- Shortest Pathfinding
The agent tracks visited nodes and performs simulations to compare movement strategies. The project also implements core graph metrics:
- Degree Centrality
- Closeness Centrality
- Betweenness Centrality
- Agent Simulation: Navigate graphs using random walks and shortest paths.
- Graph Metrics: Calculate centrality measures to evaluate node importance.
- Memory Tracking: Record visited nodes to compare strategies.
- Simulation Results: Compare random walk and shortest path strategies over 1000 simulations.
This work is based on:
Empowerment and Relevant Goal Information as Alternatives to Graph-Theoretic Centrality for Navigational Decision Making by Marcus Clements.
The paper explores the correlation between graph centrality and human navigation using empowerment and goal information measures.
Figure 1: The Implemented Graph
Figure 2: Centrality Measures