π 2024-12-04 β Session: Game Theory Strategy Optimization
π 19:55β20:20
π·οΈ Labels: Game Theory, Strategy, Expected Payoff, Optimization, Numerical Methods
π Project: Dev
β 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.