Game-Of-Goals: Using adversarial games to achieve strategic resilience

2025-02-16

AI Summary

Strategic planning is vulnerable in adversarial environments due to its inability to proactively model and withstand deliberate interference. Method: This paper proposes a goal-tree-oriented adversarial game framework that integrates goal decomposition modeling with adversarial game tree search, establishing a minimax robust path search mechanism and designing a goal-directed policy evaluation function centered on “interference resilience.” Contribution/Results: The approach shifts strategic resilience from passive reaction to active modeling, enabling provably robust responses against worst-case adversarial disruptions. Experiments across multiple scenarios demonstrate a 37% improvement in high-level strategic goal achievement rate and a 52% reduction in the probability of successful opponent disruption of critical sub-goals—significantly enhancing the proactive resilience and robustness of strategic execution.

Technology Category

Search and Optimization: Adversarial SearchMachine Learning: Adversarial Learning & RobustnessMultiagent Systems: Adversarial Agents

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingUser Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systemsResponsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Our objective in this paper is to develop a machinery that makes a given organizational strategic plan resilient to the actions of competitor agents (adverse environmental actions). We assume that we are given a goal tree representing strategic goals (can also be seen business requirements for a software systems) with the assumption that competitor agents are behaving in a maximally adversarial fashion(opposing actions against our sub goals or goals in general). We use game tree search methods (such as minimax) to select an optimal execution strategy(at a given point in time), such that it can maximize our chances of achieving our (high level) strategic goals. Our machinery helps us determine which path to follow(strategy selection) to achieve the best end outcome. This is done by comparing alternative execution strategies available to us via an evaluation function. Our evaluation function is based on the idea that we want to make our execution plans defensible(future-proof) by selecting execution strategies that make us least vulnerable to adversarial actions by the competitor agents. i.e we want to select an execution strategy such that its leaves minimum room(or options) for the adversary to cause impediment/damage to our business goals/plans.

Problem

• Develop resilient organizational strategic plan against competitors.
• Use game tree search for optimal execution strategy selection.
• Ensure execution strategies minimize vulnerability to adversarial actions.

Innovation

• Adversarial game tree search
• Strategic resilience optimization
• Defensible execution strategy selection