πŸ“… 2024-12-04 β€” Session: Game Theory Strategy Optimization

πŸ•’ 19:55–20:20
🏷️ Labels: Game Theory, Strategy, Expected Payoff, Optimization, Numerical Methods
πŸ“‚ Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to optimize strategies in game theory, focusing on maximizing expected payoffs against opponents with varying strategies.

Key Activities

  • Evaluated numerical solutions for optimal strategies in game theory.
  • Analyzed expected payoffs using custom strategies and distributions.
  • Implemented Python strategies to handle multiple probability density functions (PDFs) and cumulative distribution functions (CDFs).
  • Initiated a screening process for knowledge management in the MatΓ­as Automation Lab.
  • Addressed errors in symbolic evaluation of piecewise expressions using numerical integration.
  • Verified optimal strategies for single and multiple opponent scenarios.

Achievements

  • Successfully identified optimal strategies for maximizing expected payoffs in different game scenarios.
  • Developed a structured approach to evaluate expected returns and manage symbolic evaluation errors.

Pending Tasks

  • Further refinement of strategies based on additional opponent data.
  • Continued development of automation processes for knowledge management.