The environment value of an opponent model

Area of Science:

• Decision Sciences
• Game Theory
• Artificial Intelligence

Background:

• Game-theoretic equilibrium strategies offer a baseline but may not maximize performance in adversarial settings.
• Opponent modeling can potentially improve an agent's performance by anticipating adversary actions.
• Quantifying the benefits of opponent modeling is crucial for practical system design.

Purpose of the Study:

• To develop a method for calculating an upper bound on performance improvement using best-response strategies against opponent models.
• To demonstrate that this bound is dependent only on the environment's structure, not the specific agents.
• To provide a framework for evaluating the profitability and necessity of opponent models in adversarial domains.

Main Methods:

• Derivation of a theoretical upper bound for performance improvement.
• Analysis of the bound's independence from specific environmental actors.
• Application and validation of the bounding technique in two distinct adversarial domains: convoy defense and a war game scenario.

Main Results:

• An upper bound for performance improvement achievable through opponent modeling was established.
• The bound was shown to be solely a function of the adversarial environment's domain structure.
• The method was successfully applied to intelligence prioritization and enemy decision prediction, demonstrating its practical utility.

Conclusions:

• The developed bounds-finding technique enables system designers to assess the potential value of opponent models.
• It provides a quantifiable baseline for comparing the performance of different opponent modeling approaches.
• This research offers a principled way to determine if and what type of opponent models are beneficial in specific adversarial contexts.